Ranking MPs by how much they cite their sources (version 2.0)

This article ranks UK politicians by how likely they are to back up their factual assertions with references. I use their Twitter streams as a starting point.  The method is somewhat of a blunt instrument and it’s certainly not the sort of thing that I’d call science, but it illustrates some points nicely.

 

The method goes like this: we gather all recent tweets by mps, take those that involve numbers (because tweets with numbers are more likely to be a fact that should be cited) and check if it has a url attached to it.

We’re looking at the very basics of knowledge and information sharing here.  If we are to have grown-up conversations we must use information that is verifiable.  And the first step to providing verifiable information is providing a source.

You can view the original prototype post here.  I checked how effective the method was with human volunteers here and it worked very well. I’m certainly happy that the heuristic is relatively accurate.

All of the code lives in this github repositry, and if you’d like to look at the files for individual MPs they are in this directory.

 

Last time, someone on reddit said something very smart:

 

I think it’s pretty clear that the top 20% of those MPs are doing something right methodologically, compared to the bottom 20%. But I’d be careful of singling out any individual case with such a blunt tool. Especially if all 5 are from the same party

…and you should take that as your general guide. If your MP is in the bottom half of the table, there is probably something wrong…

This is the ranking for the last little while, it’s the first ranking since the new intake of MPs, so some of them have got a little bit of work to do. If you are an MP and you are reading this (…and I know some of you do, because two of you complained last time) then you might want to ask John Redwood for a guide.


1Richard Bacon ( @Bacon4SNorfolk) 32 4 4 100.00%
2John Redwood ( @johnredwood) 2096 139 135 97.12%
3Victoria Borwick ( @backborwick) 252 13 11 84.62%
4Nick Gibb ( @NickGibbMP) 148 13 11 84.62%
5Rehman Chishti ( @Rehman_Chishti) 1055 150 124 82.67%
6Harry Harpham MP ( @Harry4BH) 454 42 34 80.95%
7Stephen McPartland ( @SMcPartlandMP) 982 77 62 80.52%
8Ed Vaizey ( @edvaizey) 3221 479 376 78.50%
9Marie Rimmer MP ( @MarieRimmerMP) 287 32 25 78.12%
10Alberto Costa ( @Costa4SLeics) 209 9 7 77.78%
11Desmond Swayne ( @DesmondSwayne) 322 18 14 77.78%
12Sir Edward Garnier ( @EGarnierMP) 547 58 45 77.59%
13Dave Anderson MP ( @DaveAndersonMP) 459 53 41 77.36%
14Edward Timpson MP ( @edwardtimpson) 196 13 10 76.92%
15Dan Jarvis ( @DanJarvisMP) 3222 398 306 76.88%
16Grahame Morris ( @grahamemorris) 3186 366 280 76.50%
17Jon Cruddas ( @JonCruddas_1) 1051 84 64 76.19%
18Owen Paterson MP ( @Owen_PatersonMP) 475 45 34 75.56%
19Sadiq Khan MP ( @SadiqKhan) 3207 232 173 74.57%
20Alan Mak ( @AlanMakMP) 1192 98 72 73.47%
21Gisela Stuart MP ( @GiselaStuart) 3206 333 239 71.77%
22Sammy Wilson ( @eastantrimmp) 143 7 5 71.43%
23Andrew Smith ( @AndrewSmithMP) 503 27 19 70.37%
24Mickey Brady MP ( @MickeyBradySF) 1679 148 104 70.27%
25Mark Field ( @MarkFieldMP) 860 56 39 69.64%
26Jeremy Lefroy ( @JeremyLefroyMP) 2411 240 166 69.17%
27Dr Tania Mathias MP ( @tania_mathias) 545 58 40 68.97%
28Alex Salmond ( @AlexSalmond) 2362 252 172 68.25%
29Imran Hussain MP ( @Imran_HussainMP) 766 66 45 68.18%
30Philippa Whitford MP ( @P_Whitford_MP) 1431 102 69 67.65%
31Andy Slaughter MP ( @hammersmithandy) 3220 317 214 67.51%
32Nicola Blackwood ( @nicolablackwood) 1577 150 100 66.67%
33John Penrose MP ( @JohnPenroseNews) 1837 197 131 66.50%
34Dawn Butler ( @DawnButlerBrent) 3205 298 197 66.11%
35Harriett Baldwin ( @HBaldwinMP) 3230 407 268 65.85%
36Kit Malthouse ( @kitmalthouse) 947 114 75 65.79%
37Mike Gapes ( @MikeGapes) 3213 278 182 65.47%
38Rupa Huq MP ( @RupaHuq) 2904 320 208 65.00%
39Alison McGovern ( @Alison_McGovern) 3238 248 161 64.92%
40Jason McCartney MP ( @JasonMcCartney) 3220 410 266 64.88%
41Alan Campbell ( @alancampbellmp) 1054 122 79 64.75%
42Geraint Davies MP ( @GeraintDaviesMP) 1844 237 153 64.56%
43Richard Burden MP ( @RichardBurdenMP) 3238 311 200 64.31%
44Kate Osamor MP ( @KateOsamor) 3222 249 160 64.26%
45Grant Shapps ( @grantshapps) 3214 286 183 63.99%
46tom_watson ( @tom_watson) 3222 124 79 63.71%
47Adam Afriyie ( @AdamAfriyie) 1127 85 54 63.53%
48Frank Field MP ( @frankfieldteam) 567 41 26 63.41%
49Chris Law MP (SNP) ( @ChrisLawSNP) 3222 172 109 63.37%
50Diane Abbott MP ( @HackneyAbbott) 3229 272 172 63.24%
51Robert Halfon ( @halfon4harlowMP) 3171 303 191 63.04%
52Naz Shah MP ( @NazShahBfd) 3208 192 121 63.02%
53Chris Davies MP ( @ChrisDavies_MP) 331 27 17 62.96%
54Guy Opperman MP ( @GuyOpperman) 3226 433 272 62.82%
55Angela Eagle ( @angelaeagle) 3216 266 167 62.78%
56Mary Robinson MP ( @MaryRobinson01) 599 48 30 62.50%
57Liz Kendall ( @leicesterliz) 3219 237 148 62.45%
58Brendan O'Hara MP ( @BrendanOHaraSNP) 540 50 31 62.00%
59Huw Irranca-Davies ( @IrrancaDaviesMP) 3228 259 160 61.78%
60Angus Robertson ( @AngusRobertson) 3222 251 155 61.75%
61Craig Mackinlay ( @cmackinlay) 1614 154 95 61.69%
62Craig Williams MP ( @Craig4CardiffN) 3221 219 135 61.64%
63Huw Merriman MP ( @HuwMerriman) 458 26 16 61.54%
64Debbie Abrahams ( @Debbie_abrahams) 3209 323 197 60.99%
65Steve Baker MP ( @SteveBakerHW) 3204 256 156 60.94%
66Amanda Milling MP ( @amandamilling) 783 66 40 60.61%
67Ann Clwyd ( @AnnClwyd) 338 43 26 60.47%
68Richard Arkless MP ( @ArklessRichard) 1743 159 96 60.38%
69Rt Hon Lord Maude ( @UKTradeMinister) 1083 126 76 60.32%
70Gareth Thomas ( @GarethThomasMP) 3203 428 258 60.28%
71Lucy Powell MP ( @LucyMPowell) 3196 239 144 60.25%
72Patrick Grady ( @GradySNP) 1241 93 56 60.22%
73john spellar ( @spellar) 3228 273 164 60.07%
74Rachel Reeves ( @RachelReevesMP) 3232 373 224 60.05%
75Judith Cummins MP ( @JudithCummins) 1073 140 84 60.00%
76Seema Malhotra ( @SeemaMalhotra1) 3238 312 187 59.94%
77Henry Smith MP ( @HenrySmithMP) 3232 329 197 59.88%
78David Lammy ( @DavidLammy) 3205 224 134 59.82%
79Michael Dugher MP ( @MichaelDugher) 3239 263 157 59.70%
80Patricia Gibson MP ( @PGibsonSNP) 610 47 28 59.57%
81Tristram Hunt ( @TristramHuntMP) 2240 173 103 59.54%
82Graham Evans ( @GrahamEvansMP) 3212 451 268 59.42%
83Paul Blomfield ( @PaulBlomfieldMP) 3204 246 146 59.35%
84Paul Maskey ( @PaulMaskeyMP) 3227 250 148 59.20%
85Jeff Smith ( @JeffSmithetc) 3193 289 171 59.17%
86Margaret Greenwood ( @MGreenwoodWW) 2493 319 188 58.93%
87Valerie Vaz MP ( @Valerie_VazMP) 946 107 63 58.88%
88Rob Marris MP ( @WSW_Labour) 537 46 27 58.70%
89Wendy Morton MP ( @morton_wendy) 637 46 27 58.70%
90George Freeman ( @Freeman_George) 3200 349 204 58.45%
91Rory Stewart ( @RoryStewartUK) 3189 485 281 57.94%
92Michelle Donelan MP ( @michelledonelan) 3209 272 157 57.72%
93Kate Green ( @KateGreenSU) 3235 300 173 57.67%
94Damian Collins ( @DamianCollins) 3207 359 207 57.66%
95Andrea Jenkyns MP ( @andreajenkyns) 3206 361 208 57.62%
96Bob Blackman ( @bobblackmanmp) 3230 304 175 57.57%
97Maria Eagle MP ( @meaglemp) 3219 334 192 57.49%
98James Gray MP ( @JamesGrayMP) 713 47 27 57.45%
99Suella Fernandes MP ( @SuellaFernandes) 1019 61 35 57.38%
100Karl Turner MP ( @KarlTurnerMP) 3230 279 160 57.35%
101Martyn Day MP ( @MartynDaySNP) 1602 157 90 57.32%
102Ruth Cadbury MP ( @RuthCadbury) 3197 413 236 57.14%
103Sir Malcolm Rifkind ( @MalcolmRifkind) 99 7 4 57.14%
104HebridesMP ( @hebridesmp) 422 21 12 57.14%
105David Cameron ( @David_Cameron) 2046 221 126 57.01%
106Johnny Mercer MP ( @JohnnyMercerMP) 3194 204 116 56.86%
107David Lidington ( @DLidington) 3217 437 248 56.75%
108Matt Hancock ( @MattHancockMP) 3229 372 211 56.72%
109Lyn Brown ( @lynbrownmp) 3188 295 167 56.61%
110Nick Herbert ( @nickherbertmp) 2361 258 146 56.59%
111Alison Thewliss ( @alisonthewliss) 3237 274 155 56.57%
112Douglas Chapman MP ( @DougChapmanSNP) 3211 419 237 56.56%
113William Wragg MP ( @William_Wragg) 1130 69 39 56.52%
114Graham Allen MP ( @GrahamAllenMP) 3224 299 169 56.52%
115Stephen Kinnock MP ( @SKinnock) 933 71 40 56.34%
116Caroline Spelman MP ( @spelmanc) 765 87 49 56.32%
117Andy Burnham ( @andyburnhammp) 3203 373 210 56.30%
118Andrew Gwynne MP ( @GwynneMP) 3180 273 153 56.04%
119Dr James Davies MP ( @jamesdaviesmp) 691 50 28 56.00%
120Nick Clegg ( @nick_clegg) 2215 154 86 55.84%
121Gavin Williamson ( @GWilliamsonMP) 616 54 30 55.56%
122Edward Leigh MP ( @EdwardLeighMP) 146 9 5 55.56%
123Lucy Allan MP ( @lucyallan) 3226 216 120 55.56%
124Ranil Jayawardena MP ( @TellRanil) 1403 135 75 55.56%
125Ian Paisley ( @ianpaisleymp) 321 27 15 55.56%
126Julie Elliott MP ( @JulieElliottMP) 1851 251 139 55.38%
127Rob Wilson ( @RobWilson_RDG) 3229 354 196 55.37%
128Sir Oliver Heald MP ( @OliverHealdMP) 1506 159 88 55.35%
129Chuka Umunna ( @ChukaUmunna) 3170 253 139 54.94%
130teresa pearce ( @tpearce003) 3223 284 156 54.93%
131Jane Ellison MP ( @JaneEllison) 2659 352 193 54.83%
132Hannah Bardell MP ( @HannahB4LiviMP) 1779 104 57 54.81%
133Kevin Hollinrake MP ( @kevinhollinrake) 2666 261 143 54.79%
134Deidre Brock ( @DeidreBrock) 3178 327 179 54.74%
135Angela Rayner MP ( @AngelaRayner) 3190 287 157 54.70%
136Stephen Gethins MP ( @StephenGethins) 1364 108 59 54.63%
137John Whittingdale ( @JWhittingdale) 65 11 6 54.55%
138Louise Haigh MP ( @LouHaigh) 3231 259 141 54.44%
139George Kerevan ( @GeorgeKerevan) 1872 210 114 54.29%
140Caroline Flint ( @CarolineFlintMP) 3213 247 134 54.25%
141Karl McCartney ( @karlmccartney) 3227 266 144 54.14%
142Sue Hayman ( @SueHayman1) 1928 170 92 54.12%
143Tasmina Sheikh MP ( @TasminaSheikh) 3213 222 120 54.05%
144Jo Cox MP ( @Jo_Cox1) 3231 265 143 53.96%
145Luciana Berger ( @lucianaberger) 3228 282 152 53.90%
146Barry Gardiner ( @BarryGardiner) 3201 321 173 53.89%
147Liam Byrne ( @LiamByrneMP) 3189 313 168 53.67%
148Jamie Reed ( @jreedmp) 3215 127 68 53.54%
149Carol Monaghan MP ( @CMonaghanMP) 1323 99 53 53.54%
150Jim Fitzpatrick ( @FitzMP) 3224 297 159 53.54%
151David Crausby ( @DavidCrausby) 370 43 23 53.49%
152Kevin Brennan ( @KevinBrennanMP) 3216 322 172 53.42%
153Marion Fellows MP ( @marion53f) 835 62 33 53.23%
154Sajid Javid ( @sajidjavid) 2876 319 169 52.98%
155Rebecca Long-Bailey ( @RLong_Bailey) 412 34 18 52.94%
156Sir Gerald Howarth ( @geraldhowarth) 460 51 27 52.94%
157chi onwurah ( @ChiOnwurah) 3228 225 119 52.89%
158Caroline Lucas ( @CarolineLucas) 3224 216 114 52.78%
159Charlie Elphicke ( @CharlieElphicke) 3174 330 174 52.73%
160John Woodcock ( @JWoodcockMP) 3223 147 77 52.38%
161Liz Saville Roberts ( @LSRPlaid) 1495 153 80 52.29%
162Pat Doherty ( @PatDohertyMP) 759 67 35 52.24%
163Tom Brake MP ( @thomasbrake) 3203 450 235 52.22%
164Peter Grant MP ( @PeterGrantMP) 1560 182 95 52.20%
165Alec Shelbrooke MP ( @AlecShelbrooke) 3170 361 188 52.08%
166Mark Pawsey ( @MarkPawsey) 2818 327 170 51.99%
167Joan Ryan MP ( @joanryanEnfield) 1402 133 69 51.88%
168Mary Creagh ( @MaryCreaghMP) 3214 353 183 51.84%
169Martin Docherty MP ( @MartinJDocherty) 3233 269 139 51.67%
170Sarah Champion MP ( @SarahChampionMP) 3210 246 127 51.63%
171Denis Skinner ( @BolsoverBeast) 3214 219 113 51.60%
172John McNally MP ( @JohnMcNallyMP) 856 64 33 51.56%
173Daniel Kawczynski MP ( @KawczynskiMP) 3206 295 152 51.53%
174Alun Cairns ( @AlunCairns) 3194 328 169 51.52%
175Jeffrey Donaldson ( @J_Donaldson_MP) 1495 138 71 51.45%
176Matthew Pennycook MP ( @mtpennycook) 3230 316 162 51.27%
177Wes Streeting MP ( @wesstreeting) 3212 199 102 51.26%
178Sharon Hodgson MP ( @SharonHodgsonMP) 3216 291 149 51.20%
179Philip Hammond ( @PHammondMP) 642 45 23 51.11%
180Zac Goldsmith ( @ZacGoldsmith) 3206 194 99 51.03%
181Gloria De Piero MP ( @GloriaDePiero) 3198 355 181 50.99%
182David Mackintosh MP ( @davidmackintosh) 3235 308 157 50.97%
183Robert Buckland MP ( @RobertBuckland) 3189 433 219 50.58%
184Ed Miliband ( @Ed_Miliband) 3235 273 138 50.55%
185Simon Hart ( @Simonhartmp) 1061 117 59 50.43%
186Derek Thomas ( @DerekThomasMP) 87 10 5 50.00%
187Stuart Donaldson MP ( @StuBDonaldson) 753 60 30 50.00%
188Nigel Huddleston MP ( @HuddlestonNigel) 572 72 36 50.00%
189Steven Paterson MP ( @Steven4Stirling) 914 118 59 50.00%
190Alex Chalk ( @AlexChalkChelt) 998 50 25 50.00%
191Leader's Office ( @CommonsLeader) 24 2 1 50.00%
192Greg Clark ( @gregclarkmp) 641 64 32 50.00%
193Jo Churchill MP ( @Jochurchill4) 654 46 23 50.00%
194Keir Starmer ( @Keir_Starmer) 571 46 23 50.00%
195Chris Green ( @ChrisGreenMP) 3227 313 156 49.84%
196Robin Walker MP ( @WalkerWorcester) 3192 486 242 49.79%
197Helen Jones MP ( @HelenJonesMP) 2206 173 86 49.71%
198stellacreasy ( @stellacreasy) 3204 171 85 49.71%
199Christina Rees ( @Rees4Neath) 1883 151 75 49.67%
200Nicholas Soames ( @nsoamesmp) 1575 123 61 49.59%
201Karen Buck ( @KarenPBuckMP) 3242 317 157 49.53%
202Kirsty Blackman ( @KirstySNP) 1140 79 39 49.37%
203Stuart McDonald MP ( @Stuart_McDonald) 976 63 31 49.21%
204Peter Aldous ( @peter_aldous) 818 59 29 49.15%
205Karen Bradley ( @karen__bradley) 756 104 51 49.04%
206Nadhim Zahawi ( @nadhimzahawi) 3180 414 203 49.03%
207Stephen Metcalfe ( @Metcalfe_SBET) 1012 98 48 48.98%
208Rushanara Ali ( @rushanaraali) 2779 241 118 48.96%
209Damian Hinds ( @damian57) 1449 178 87 48.88%
210Jonathan Djanogly ( @JDjanogly) 1381 121 59 48.76%
211Philip Davies MP ( @PhilipDaviesMP) 3185 265 129 48.68%
212Rebecca Harris ( @RebeccaHarrisMP) 3208 452 220 48.67%
213James Cartlidge MP ( @jc4southsuffolk) 628 37 18 48.65%
214Barry Sheerman ( @BarrySheerman) 3237 183 89 48.63%
215Melanie Onn MP ( @OnnMel) 3220 364 177 48.63%
216Corri Wilson MP ( @CorriWilsonSNP) 3206 216 105 48.61%
217Nick Thomas-Symonds ( @NickTorfaenMP) 1791 68 33 48.53%
218Vernon Coaker MP ( @Vernon_Coaker) 3218 302 146 48.34%
219David Morris MP ( @Davidmpmorris) 3188 201 97 48.26%
220Victoria Prentis MP ( @VictoriaPrentis) 259 27 13 48.15%
221Nigel Dodds ( @NigelDoddsDUP) 1373 102 49 48.04%
222Andrew Stephenson MP ( @Andrew4Pendle) 3191 425 204 48.00%
223Albert Owen MP ( @AlbertOwenMP) 3217 290 139 47.93%
224Mike Weir ( @mikeweirsnp) 3213 217 104 47.93%
225Mark Lancaster ( @MarkLancasterMK) 1521 263 126 47.91%
226Mark Harper MP ( @Mark_J_Harper) 824 94 45 47.87%
227Phil Wilson MP ( @PhilWilsonMP) 3207 360 172 47.78%
228Jo Johnson ( @JoJohnsonMP) 457 67 32 47.76%
229Jo Stevens ( @JoStevensLabour) 3243 243 116 47.74%
230Emma Lewell-Buck ( @EmmaLewellBuck) 3185 172 82 47.67%
231Brandon Lewis MP ( @BrandonLewis) 3199 357 170 47.62%
232Kirsten Oswald MP ( @kirstenoswald) 2192 124 59 47.58%
233Mark Williams ( @mark4ceredigion) 3177 248 118 47.58%
234Simon Danczuk ( @SimonDanczuk) 3231 227 108 47.58%
235Nic Dakin ( @NicDakinMP) 3225 330 157 47.58%
236Caroline Ansell ( @Caroline_Ansell) 1539 141 67 47.52%
237Andy McDonald MP ( @AndyMcDonaldMP) 1274 118 56 47.46%
238Anne-Marie Trevelyan ( @annietrev) 3207 277 131 47.29%
239Caroline Dinenage ( @cj_dinenage) 3195 366 173 47.27%
240Ian Lucas ( @IanCLucas) 3232 301 142 47.18%
241Stewart Jackson MP ( @Stewart4Pboro) 3238 395 186 47.09%
242Drew Hendry MP ( @drewhendrySNP) 3194 240 113 47.08%
243Kevan Jones MP ( @KevanJonesMP) 3203 308 145 47.08%
244Ann Coffey ( @anncoffey_mp) 1117 85 40 47.06%
245Shabana Mahmood MP ( @ShabanaMahmood) 1739 119 56 47.06%
246Catherine West MP ( @CatherineWest1) 3221 221 104 47.06%
247Angela Crawley MP ( @AngelaCrawleyMP) 3161 270 127 47.04%
248John McDonnell MP ( @johnmcdonnellMP) 3170 237 111 46.84%
249Nigel Mills MP ( @NigelMillsMP) 717 88 41 46.59%
250Alasdair McDonnell ( @AlasdairMcD_MP) 1201 88 41 46.59%
251David Simpson MP ( @DavidSimpsonDUP) 689 73 34 46.58%
252Mark Durkan MP ( @markdurkan) 3199 688 320 46.51%
253David Mowat MP ( @mowat4ws) 1530 170 79 46.47%
254Charlotte Leslie ( @CLeslieMP) 3219 181 84 46.41%
255Justin Madders MP ( @justinmadders) 1331 95 44 46.32%
256Angus B MacNeil MP ( @AngusMacNeilSNP) 3211 324 150 46.30%
257Jesse Norman ( @Jesse_Norman) 3200 238 110 46.22%
258Andrew Griffiths ( @agriffithsmp) 3194 329 152 46.20%
259Stewart McDonald MP ( @StewartMcDonald) 3208 182 84 46.15%
260Nick Smith ( @BlaenauGwentMP) 812 76 35 46.05%
261Jack Dromey MP ( @JackDromeyMP) 3226 513 236 46.00%
262Dr Lisa Cameron MP ( @lisacameronsnp) 1524 124 57 45.97%
263Bill Esterson ( @Bill_Esterson) 3217 253 116 45.85%
264Neil Coyle ( @coyleneil) 3229 430 197 45.81%
265James Berry MP ( @JamesBerryMP) 2175 247 113 45.75%
266Daniel Zeichner ( @DanielZeichner) 3214 245 112 45.71%
267Virendra Sharma MP ( @VirendraSharma) 1577 182 83 45.60%
268Clive Efford ( @CliveEfford) 2423 248 113 45.56%
269Heidi Alexander ( @heidi_mp) 3215 308 140 45.45%
270Ronnie Cowan MP ( @ronniecowan) 3191 361 164 45.43%
271Byron Davies ( @Byron_Davies) 2897 262 119 45.42%
272Justine Greening ( @JustineGreening) 1024 97 44 45.36%
273Clive Lewis MP ( @labourlewis) 3222 219 99 45.21%
274Carolyn Harris ( @carolynharris24) 2848 217 98 45.16%
275Margaret Ferrier MP ( @MargaretFerrier) 2082 268 121 45.15%
276Maria Caulfield MP ( @mariacaulfield) 3197 204 92 45.10%
277Geoffrey Cox QC MP ( @Geoffrey_Cox) 779 58 26 44.83%
278Tom Blenkinsop ( @TomBlenkinsop) 3228 404 181 44.80%
279Ian Blackford ( @IBlackfordSNPMP) 3196 190 85 44.74%
280Owen Thompson MP ( @OwenThompson) 3221 186 83 44.62%
281Nick Hurd MP ( @nickhurdmp) 3232 531 236 44.44%
282Richard Graham ( @RichardGrahamMP) 3213 469 208 44.35%
283Anna Turley MP ( @annaturley) 3196 235 104 44.26%
284Barbara Keeley ( @KeeleyMP) 3207 364 161 44.23%
285Sarah Newton ( @SarahNewtonMP) 1769 181 80 44.20%
286David Warburton MP ( @DJWarburton) 3192 220 97 44.09%
287Stewart Hosie ( @StewartHosieSNP) 3213 372 164 44.09%
288Hywel Williams AS/MP ( @HywelPlaidCymru) 3169 286 126 44.06%
289Douglas Carswell MP ( @DouglasCarswell) 3223 184 81 44.02%
290Antoinette Sandbach ( @ASandbachMP) 3230 309 136 44.01%
291vickyfoxcroft ( @vickyfoxcroft) 2602 253 111 43.87%
292Greg Hands ( @GregHands) 3225 349 153 43.84%
293Emily Thornberry ( @EmilyThornberry) 3206 324 142 43.83%
294Mark Hendrick ( @MpHendrick) 853 89 39 43.82%
295Yvonne Fovargue ( @Y_FovargueMP) 1649 181 79 43.65%
296Michael Fabricant ( @Mike_Fabricant) 3191 250 109 43.60%
297David Davies MP ( @DavidTCDavies) 2211 273 119 43.59%
298Jonathan Reynolds MP ( @jreynoldsMP) 3202 214 93 43.46%
299Tom Tugendhat MP ( @TomTugendhat) 3211 215 93 43.26%
300Norman Lamb ( @normanlamb) 3197 155 67 43.23%
301Mike Kane ( @MikeKaneMP) 3123 262 113 43.13%
302Jonathan Edwards ( @JonathanPlaid) 3220 232 100 43.10%
303Ian Murray ( @IanMurrayMP) 3218 441 190 43.08%
304Diana Johnson ( @DianaJohnsonMP) 3189 316 136 43.04%
305Ben Bradshaw ( @BenPBradshaw) 3249 200 86 43.00%
306Pat McFadden ( @patmcfaddenmp) 3223 242 104 42.98%
307Dr Phillip Lee ( @DrPhillipLeeMP) 2099 203 87 42.86%
308Natalie McGarry MP ( @NatalieMcGarry) 331 14 6 42.86%
309David Evennett MP ( @DavidEvennett) 115 7 3 42.86%
310Hugo Swire ( @HugoSwire) 3222 271 116 42.80%
311Jonathan Ashworth MP ( @JonAshworth) 3233 229 98 42.79%
312Will Quince MP ( @willquince) 3229 219 93 42.47%
313Owen Smith ( @OwenSmith_MP) 3195 311 132 42.44%
314David Burrowes ( @davidburrowesmp) 1738 172 73 42.44%
315Jessica Morden ( @jessicamordenmp) 1243 165 70 42.42%
316Conor Murphy ( @conormurphysf) 3215 269 114 42.38%
317Jake Berry ( @JakeBerry) 3196 288 122 42.36%
318Margaret Hodge ( @margarethodge) 1280 116 49 42.24%
319Graham Stuart MP ( @grahamstuart) 2988 299 126 42.14%
320Neil Carmichael ( @stroud_neil) 3207 278 117 42.09%
321MimsDaviesMP ( @mimsdavies) 3190 473 199 42.07%
322Karen Lumley MP ( @Tell_Karen) 2661 250 105 42.00%
323Nicky Morgan ( @NickyMorgan01) 3207 348 146 41.95%
324John Pugh MP ( @johnpughmp) 746 62 26 41.94%
325Tommy Sheppard MP ( @TommySheppard) 1097 124 52 41.94%
326Tim Farron ( @timfarron) 3223 43 18 41.86%
327Khalid Mahmood ( @khalid4PB) 104 12 5 41.67%
328Chris Stephens MP ( @ChrisStephens) 3210 224 93 41.52%
329Guto Bebb ( @GutoBebb) 2489 174 72 41.38%
330Liz McInnes ( @LizMcInnesMP) 2951 278 115 41.37%
331Jeremy Corbyn MP ( @jeremycorbyn) 3209 295 122 41.36%
332Julie Hilling ( @JulieHilling) 1455 143 59 41.26%
333David Rutley ( @DavidRutleyMP) 1498 148 61 41.22%
334Paul Maynard ( @PaulMaynardMP) 3196 265 109 41.13%
335Royston Smith ( @Royston_Smith) 3222 275 113 41.09%
336Michael Ellis ( @Michael_Ellis1) 1272 95 39 41.05%
337Harriet Harman ( @HarrietHarman) 3193 289 118 40.83%
338Catherine McKinnell ( @CatMcKinnellMP) 3179 267 109 40.82%
339Paula Sherriff MP ( @paulasherriff) 3239 317 129 40.69%
340Penny Mordaunt MP ( @PennyMordauntMP) 3217 222 90 40.54%
341Sir Eric Pickles ( @EricPickles) 3040 363 147 40.50%
342Bob Neill ( @neill_bob) 989 141 57 40.43%
343Helen Grant ( @HelenGrantMP) 2522 213 86 40.38%
344Yvette Cooper ( @YvetteCooperMP) 2788 322 130 40.37%
345Alok Sharma MP ( @AlokSharma_RDG) 1546 134 54 40.30%
346Stephen Timms ( @stephenctimms) 1885 194 78 40.21%
347Conor McGinn MP ( @ConorMcGinn) 3109 316 127 40.19%
348julian knight mp ( @julianknight15) 3210 426 171 40.14%
349Karin Smyth MP ( @karinsmyth) 3210 299 120 40.13%
350Chris Bryant MP ( @RhonddaBryant) 3214 182 73 40.11%
351Bill Cash ( @BillCashMP) 94 5 2 40.00%
352Pauline Latham ( @Pauline_Latham) 2060 225 90 40.00%
353Stephen Hepburn ( @jarrowstevemp) 123 5 2 40.00%
354Holly Lynch MP ( @HollyLynch5) 644 65 26 40.00%
355Tobias Ellwood MP ( @Tobias_Ellwood) 1087 130 52 40.00%
356Stephen Hammond MP ( @S_Hammond) 1326 123 49 39.84%
357Emma Reynolds ( @EmmaReynoldsMP) 3205 324 129 39.81%
358Ian Mearns MP ( @IanMearnsMP) 3199 319 127 39.81%
359Joanna Cherry QC MP ( @joannaccherry) 2798 249 99 39.76%
360Mhairi Black MP ( @MhairiBlack) 2112 131 52 39.69%
361Callum McCaig MP ( @callum_mccaig) 1909 126 50 39.68%
362Rachael Maskell MP ( @RachaelMaskell) 3083 285 113 39.65%
363Andrew Jones MP ( @AJonesMP) 890 86 34 39.53%
364Rebecca Pow MP ( @pow_rebecca) 2275 253 100 39.53%
365Yasmin Qureshi MP ( @YasminQureshiMP) 3223 284 112 39.44%
366Stephen Doughty ( @SDoughtyMP) 3240 297 117 39.39%
367Mark Spencer MP ( @Mark_Spencer) 3200 267 105 39.33%
368Dr Paul Monaghan MP ( @_PaulMonaghan) 3208 189 74 39.15%
369R. Blackman-Woods ( @robertabwMP) 3186 299 117 39.13%
370Christopher Pincher ( @ChrisPincher) 3181 394 154 39.09%
371Andrea Leadsom MP ( @andrealeadsom) 1639 174 68 39.08%
372Toby Perkins ( @tobyperkinsmp) 3192 351 137 39.03%
373Alistair Burt ( @AlistairBurtMP) 3236 316 123 38.92%
374Kelly Tolhurst ( @KellyTolhurst) 976 72 28 38.89%
375Lilian Greenwood ( @LilianGreenwood) 3199 288 112 38.89%
376Lisa Nandy ( @lisanandy) 2697 229 89 38.86%
377Alex Cunningham ( @ACunninghamMP) 3232 461 179 38.83%
378Gavin Newlands MP ( @GavinNewlandsMP) 3195 312 121 38.78%
379Mark Prisk MP ( @PriskMark) 1286 114 44 38.60%
380Cheryl Gillan MP ( @CherylGillanMP) 3183 363 140 38.57%
381James Cleverly ( @JamesCleverly) 3210 236 91 38.56%
382Ben Howlett MP ( @ben4bath) 3202 211 81 38.39%
383Gordon Marsden ( @GordonMarsden) 3224 353 135 38.24%
384Phil Boswell MP ( @PhilBoswellSNP) 978 76 29 38.16%
385Glyn Davies ( @glyndaviesmp) 3224 291 111 38.14%
386Robert Jenrick MP ( @RobertJenrick) 1620 181 69 38.12%
387Kate Hoey ( @KateHoeyMP) 1572 163 62 38.04%
388Crispin Blunt MP ( @crispinbluntmp) 385 50 19 38.00%
389Jon Trickett ( @jon_trickett) 3188 279 106 37.99%
390Michelle Thomson MP ( @MichelleThomson) 2400 182 69 37.91%
391Helen Hayes ( @helenhayes_) 3192 290 109 37.59%
392Steve Brine MP ( @sbrine) 3211 311 116 37.30%
393Nadine Dorries ( @NadineDorriesMP) 3195 199 74 37.19%
394Alan Brown MP ( @AlanBrownSNP) 1591 113 42 37.17%
395Sheryll Murray MP ( @sheryllmurray) 2452 178 66 37.08%
396Greg Mulholland ( @GregMulholland1) 3229 205 76 37.07%
397Marcus Fysh MP ( @MarcusFysh) 1892 141 52 36.88%
398Amber Rudd MP ( @AmberRudd_MP) 2465 410 151 36.83%
399David Hanson ( @DavidHansonMP) 3203 522 192 36.78%
400Ivan Lewis ( @IvanLewis_MP) 3190 237 87 36.71%
401Steve Reed ( @SteveReedMP) 3225 237 87 36.71%
402Anna Soubry MP ( @Anna_Soubry) 2122 189 69 36.51%
403Stephen Twigg ( @StephenTwigg) 2874 231 84 36.36%
404Richard Burgon MP ( @RichardBurgon) 3226 333 121 36.34%
405Calum Kerr MP ( @CalumKerrSNP) 1846 157 57 36.31%
406angela smith ( @angelasmithmp) 3201 251 91 36.25%
407James Brokenshire ( @JBrokenshire) 1461 103 37 35.92%
408Tracey Crouch ( @tracey_crouch) 3219 296 106 35.81%
409Danny Kinahan ( @DdeBK) 1572 95 34 35.79%
410Steve Barclay ( @SteveBarclayMP) 1755 224 80 35.71%
411Boris Johnson ( @BorisJohnson) 159 14 5 35.71%
412Gavin Barwell MP ( @GavinBarwellMP) 3237 255 91 35.69%
413Matt Warman MP ( @mattwarman) 3224 115 41 35.65%
414Chris Heaton-Harris ( @chhcalling) 3202 262 93 35.50%
415JOHN NICOLSON M.P. ( @MrJohnNicolson) 3184 141 50 35.46%
416James Morris ( @JamesMorrisHRR) 2047 144 51 35.42%
417andrew murrison ( @murrisonMP) 2020 195 69 35.38%
418Thangam DebbonaireMP ( @tdebbonaire) 3211 232 82 35.34%
419Gavin Shuker ( @gavinshuker) 3200 221 78 35.29%
420Philip Dunne ( @Dunne4Ludlow) 474 68 24 35.29%
421Stuart Andrew MP ( @StuartAndrew) 3212 153 54 35.29%
422Madeleine Moon ( @MadeleineMoon) 1459 407 143 35.14%
423Seema Kennedy ( @SeemaKennedy) 3184 316 111 35.13%
424Sarah Wollaston MP ( @sarahwollaston) 3223 120 42 35.00%
425Justin Tomlinson MP ( @JTomlinsonMP) 3209 486 170 34.98%
426Bernard Jenkin ( @bernardjenkin) 2413 163 57 34.97%
427Bridget Phillipson ( @bphillipsonMP) 3231 167 58 34.73%
428Tulip Siddiq ( @TulipSiddiq) 3239 280 97 34.64%
429Gavin Robinson ( @GRobinsonDUP) 3195 257 89 34.63%
430Dr Liam Fox MP ( @LiamFoxMP) 909 29 10 34.48%
431Julie Cooper MP ( @JulieForBurnley) 1742 116 40 34.48%
432Julian Smith MP ( @juliansmithmp) 3190 357 123 34.45%
433Alan Whitehead ( @alanwhiteheadmp) 1001 131 45 34.35%
434Chris Leslie ( @ChrisLeslieMP) 1841 205 70 34.15%
435Neil Gray MP ( @NeilGrayMP) 3181 296 101 34.12%
436Cat Smith ( @CatSmithMP) 3179 302 103 34.11%
437David Mundell ( @DavidMundellDCT) 1434 91 31 34.07%
438Rosie Cooper MP ( @rosie4westlancs) 1271 144 49 34.03%
439Scott Mann ( @scottmannmp) 995 98 33 33.67%
440Chris White MP ( @ChrisWhite_MP) 3234 339 114 33.63%
441Craig Tracey MP ( @craig4nwarks) 3144 241 81 33.61%
442Mark Garnier ( @Mark4WyreForest) 2025 244 82 33.61%
443James Wharton MP ( @jameswhartonuk) 3195 253 85 33.60%
444Chris Matheson MP ( @ChrisM4Chester) 655 66 22 33.33%
445Francie Molloy ( @FrancieMolloy) 661 99 33 33.33%
446Eilidh Whiteford ( @EilidhWhiteford) 747 51 17 33.33%
447Natascha Engel ( @nengel4ned) 1135 85 28 32.94%
448Stephen Crabb ( @scrabbmp) 2983 331 109 32.93%
449Chloe Smith ( @NorwichChloe) 2851 246 81 32.93%
450Heidi Allen MP ( @heidiallen75) 1825 150 49 32.67%
451James Heappey MP ( @JSHeappey) 573 40 13 32.50%
452Alistair Carmichael ( @acarmichaelmp) 1613 102 33 32.35%
453Kevin Foster ( @kevin_j_foster) 3216 290 93 32.07%
454Kate Hollern ( @CllrKate) 2021 156 50 32.05%
455Therese Coffey ( @theresecoffey) 3216 291 93 31.96%
456Peter Heaton-JonesMP ( @PeterNorthDevon) 2811 286 91 31.82%
457Iain Wright ( @IainWrightMP) 3196 327 104 31.80%
458Ruth Smeeth MP ( @RuthSmeeth) 3181 381 121 31.76%
459Elizabeth Truss ( @trussliz) 2831 316 100 31.65%
460Sam Gyimah MP ( @SamGyimah) 3223 449 141 31.40%
461Ian Austin ( @IanAustinMP) 3232 239 75 31.38%
462Matthew Offord ( @Offord4Hendon) 672 67 21 31.34%
463Tom Pursglove MP ( @VotePursglove) 1727 144 45 31.25%
464Mark Tami ( @MarkTamiMP) 335 29 9 31.03%
465Robert Syms MP ( @robertsymsmp) 941 187 58 31.02%
466Iain Stewart MP ( @iainastewart) 1477 100 31 31.00%
467Oliver Colvile MP ( @olivercolvile) 2739 291 90 30.93%
468Tim Loughton ( @timloughton) 3222 493 152 30.83%
469Peter Dowd MP ( @Peter_Dowd) 1494 159 49 30.82%
470Caroline Nokes ( @carolinenokes) 3232 298 91 30.54%
471Gerald Jones MP ( @GeraldJonesLAB) 2177 256 78 30.47%
472Chris Evans MP ( @ChrisEvansMP) 324 23 7 30.43%
473John Glen ( @JohnGlenMP) 732 83 25 30.12%
474Roger Mullin MP ( @RogMull) 3197 251 75 29.88%
475Jenny Chapman ( @JennyChapman) 3189 251 75 29.88%
476Roger Godsiff MP ( @RogerGodsiff) 702 77 23 29.87%
477James Duddridge ( @JamesDuddridge) 3002 419 125 29.83%
478Steve McCabe ( @steve_mccabe) 3220 312 93 29.81%
479Craig Whittaker ( @CWhittakerMP) 3154 347 103 29.68%
480Dr Dan Poulter ( @drdanpoulter) 925 91 27 29.67%
481Hilary Benn ( @hilarybennmp) 2605 261 77 29.50%
482Kerry McCarthy ( @KerryMP) 3237 204 60 29.41%
483Nick Boles ( @NickBolesMP) 658 65 19 29.23%
484Peter Kyle MP ( @peterkyle) 3210 168 49 29.17%
485Margaret Ritchie MP ( @MRitchieMP) 3187 292 85 29.11%
486Lindsay Hoyle MP ( @LindsayHoyle_MP) 1175 90 26 28.89%
487Anne Marie Morris MP ( @AMMorrisMP) 2020 240 69 28.75%
488Damian ( @damiangreenmp) 2271 185 53 28.65%
489John Healey MP ( @JohnHealey_MP) 1605 151 43 28.48%
490Andrew Bridgen ( @AndrewBridgenMP) 770 71 20 28.17%
491Richard Benyon ( @RichardBenyonMP) 1565 160 45 28.12%
492Ben Gummer ( @ben4ipswich) 3218 262 73 27.86%
493Jeremy Hunt ( @Jeremy_Hunt) 2010 405 111 27.41%
494Chris Skidmore ( @cskidmoremp) 1440 262 71 27.10%
495Steve Rotheram MP ( @Steve_Rotheram) 3179 318 86 27.04%
496Laurence RobertsonMP ( @lrobertsonmp) 186 15 4 26.67%
497Louise Ellman MP ( @LouiseEllman) 524 75 20 26.67%
498Jess Phillips MP ( @jessphillips) 3226 188 50 26.60%
499John Stevenson MP ( @JohnStevensonMP) 329 34 9 26.47%
500Andrew Percy ( @andrewpercy) 3204 160 42 26.25%
501Derek Twigg ( @DerekTwiggMP) 270 23 6 26.09%
502Marcus Jones ( @Marcus4Nuneaton) 2352 223 58 26.01%
503David Nuttall MP ( @DavidNuttallMP) 1188 125 32 25.60%
504Wayne David ( @WayneDavid_MP) 3190 289 73 25.26%
505Andrew Turner ( @TheIslandsMP) 3237 143 36 25.17%
506Eleanor Laing ( @eleanor4epping) 81 4 1 25.00%
507Ian Lavery MP ( @IanLaveryMP) 2801 293 73 24.91%
508Helen Goodman ( @HelenGoodmanMP) 3194 531 132 24.86%
509David Jones ( @DavidJonesMP) 3231 156 38 24.36%
510Keith Vaz ( @Keith_Vaz) 2801 175 42 24.00%
511Paul Flynn ( @PaulFlynnMP) 3220 471 113 23.99%
512John Mann ( @JohnMannMP) 3215 351 83 23.65%
513Chris Philp ( @chrisphilp_mp) 977 138 32 23.19%
514Jackie Doyle-Price ( @JackieDP) 2423 179 41 22.91%
515Paul Scully MP ( @scullyp) 3206 284 65 22.89%
516Mike Wood MP ( @mikejwood) 2000 202 45 22.28%
517Alan Duncan ( @AlanDuncanMP) 223 18 4 22.22%
518Michael Tomlinson MP ( @Michael4MDNP) 1880 117 26 22.22%
519Fiona Mactaggart ( @fionamacmp) 3132 402 89 22.14%
520David Gauke ( @DavidGauke) 1224 164 35 21.34%
521Heather Wheeler MP ( @HeatherWheeler) 1382 127 27 21.26%
522MegHillierMP ( @Meg_HillierMP) 2741 299 63 21.07%
523Simon Hoare ( @Simon4NDorset) 358 24 5 20.83%
524Pete Wishart ( @PeteWishart) 3235 197 41 20.81%
525Anne McLaughlin ( @AnneMcLaughlin) 3228 415 86 20.72%
526Claire Perry ( @claire4devizes) 3211 353 73 20.68%
527Peter Bone ( @PeterBoneMP) 1248 129 26 20.16%
528Adrian Bailey ( @AdrianBailey4MP) 230 10 2 20.00%
529margot james ( @margot_james_mp) 3223 335 65 19.40%
530Martin Vickers ( @martinvickersmp) 323 31 6 19.35%
531Conor Burns ( @conorburns_mp) 3217 203 39 19.21%
532Mike Freer MP ( @mikefreermp) 3034 249 47 18.88%
533Flick Drummond MP ( @FlickD) 1462 139 25 17.99%
534Jack Lopresti ( @Jackloprestimp) 326 51 9 17.65%
535Pat Glass ( @PatGlassMP) 3209 544 96 17.65%
536Richard Harrington ( @Richard4Watford) 1359 150 26 17.33%
537AngelaWatkinsonMP ( @AngelaWatkinson) 85 6 1 16.67%
538Sir Greg Knight MP ( @gregknightmp) 1718 157 26 16.56%
539Helen Whately ( @Helen_Whately) 472 25 4 16.00%
540Steve Double MP ( @stevedouble) 3190 282 45 15.96%
541George Osborne ( @George_Osborne) 2112 338 50 14.79%
542Patrick McLoughlin ( @Patrick4Dales) 87 7 1 14.29%
543Susan Elan Jones MP ( @susanelanjones) 82 7 1 14.29%
544Tom Elliott ( @telliott_UUP) 2742 588 83 14.12%
545Anne Milton ( @AnneMiltonMP) 3237 332 46 13.86%
546Ben Wallace ( @BWallaceMP) 1829 160 18 11.25%
547John Howell ( @JohnHowellMP) 1570 183 17 9.29%
548Andrew Bingham ( @HighPeakAndrew) 1820 162 15 9.26%
549John Cryer ( @JohnCryerMP) 173 11 1 9.09%
550Andrew Selous MP ( @AndrewSelous) 947 167 13 7.78%
551Maggie Throup MP ( @mthroup) 329 14 1 7.14%
552Mark Pritchard ( @MPritchardMP) 1920 188 13 6.91%
553Oliver Letwin ( @OliverLetwinMP) 231 20 1 5.00%
554Kevin Barron ( @KevinBarronMP) 74 5 0 0.00%
555Paul Beresford ( @PaulBeresfordMP) 1 0 0 0.00%
556David Davis MP ( @DavidDavisMP) 65 3 0 0.00%
Ranking Name Twitter Handle Tweets Tweets with Figures Sourced Tweets with Figures/td> Percentage

To give even more data than anyone asked for – regular viewers will notice that the code has moved from Java to Python and it now generated the HTML automatically. It also runs fairly quickly on the server – so it should be a lot easier to update in the future – I may even make it update automatically like the disability blog rankings.

 

MPs citing data… drilling down

Indeed it isn’t a dry fact, dry facts are things you can reference and cite, and you haven’t.  More to the point, the very wonderful and independent fullfact.org have already taken this one apart…

Anyway…

In November, and then again last week I presented automatically generated rankings of how likely MPs are to cite their sources.  Today we’re going to look in much more detail at the ten MPs who *do* cite most of the sources.  We want to know two things:

  • which of the MPs are really doing it properly
  • how accurate are the automatically generated rankings.

Along the way we’ll discover a couple of other interesting things.

So as I told you in advance, I used my normal script to download all the tweets from all MPs and rank them. The top ten MPs happened to be:

 

  • David Cameron
  • Ed Balls
  • Ed Vaizey
  • Jon Cruddas
  • Liz Kendall
  • Mark Lazarowicz
  • Sadiq Khan
  • Steve Baker
  • Tessa Jowell
  • Vince Cable

(presented alphabetically)

 

I then took all of the tweets from those ten and anonomised them so that examples like this:

Became:

“1m vanished from electoral register in just 1 year – why are Party & Partys so complacent with our democracy? (£)

I replaced all twitter tags with “@someone”, all Party names with “party” and removed links. I also replaced names of major leaders with “Leader” and so on.  We were left with an anonomised corpus that you can see here.

This means you occasionally find you are now looking at tweets like:

“@someone @someone @someone @someone @someone @someone @someone @someone so lovely to meet you all!”

Marvellous.

So – those anonomised tweets (all 1,500 of them) were reviewed by three people (myself and two others) to see if they would be counted as a ‘fact’. To be clear, we mean – “fact that should be referenced because it’s something we might argue about”  For an example this is a fact:

 

 

But this is a diary entry:

 

Some things are on the edge:

 

“If the Party win the election, Britain will face the biggest spending cuts of any major advanced economy”

While one could argue that this is presented as a fact, it’s understood by tone that this is a guess (which politician said this is, of course, left as an exercise for the reader).

So the reviews came back (you can see them all in the Google spreadsheet and we accepted majority vote (so if two of us thought it was a fact that needed a reference, then it was a fact that needed a reference). There will doubtless be errors and omissions, and indeed, you can probably find many of both, but we did the best we could, unpaid, and with hours of effort.

How many ‘facts’ are there?

We, as humans found 232 facts in 1,530 tweets.   This means that:

Among 10 of the MPs that were most likely to cite sources, the panel found that about 16% of their tweets needed references.

I suspect this number reflects the general noise of communication.

 

How effective is the automatic ranking?

One of the things that is an issue with the automatic ranking that you see on the previous page is that it works by a fairly simple heuristic of “If the tweet has a number in it, it’s probably a fact”.  One of the things I wanted to do with today’s post was look into how accurate that was.  So this table looks into the relationship between how ‘facty’ our judges found it things that matched the heuristic:

Tweets containing number
3 votes for fact 72.08%
2 votes for fact 59.49%
0 vote for fact(100 sample tweets) 21%

This is actually biased against the heuristic because a lot of “With 89 days to go until the election” tweets that made it into the 0 vote sample.

It’s clear that containing a number correlates fairly well with being regarded as a fact by our judges. This is a stronger result than I expected and makes me more comfortable with the ranking as a whole.

 

So how does the top ten shake out? (Or: Just how sick are you of looking at Ed Vaizey’s Tweets)

 

Ed Vaizey tweets a lot, a very lot. And he’s pretty good at referencing them

 

Here’s one I like…

…although I’m unconvinced that having lots of advertising is a good thing… In any case I quite like Ed’s tweets, although they do have the slight feel that he may have privatised his Twitter and sold it to Sky Broadband.

In any case this is how the world shakes out for the top ten, ranked by percentage:

Rank MP Facts Cited Facts Percentage
1= Jon Cruddas 3 3 100.00%
1= Liz Kendall 1 1 100.00%
1= Mark Lazarowicz 7 7 100.00%
4 Ed Vaizey 73 64 87.67%
5 Ed Balls 9 7 77.78%
6 Vince Cable 13 10 76.92%
7 Tessa Jowell 40 30 75.00%
8 Sadiq Khan 41 25 60.98%
9 Steve Baker 5 3 60.00%
10 David Cameron 26 12 46.15%

(The eagle-eyed amongst you will notice that there are slightly fewer tweets here than you would expect – that’s got a lot to do with the person who stole my laptop with the full corpus on it last week)

The thing to notice is: this isn’t that many facts for some of these guys. It’s interesting to consider what the purpose of an MPs twitter is (and, of course, they might use them differently) is it to respond to constituents? Promote debate? Defend their actions? Make arguments to sway people? Or is it to throw mud, distort issues and generally heckle? Almost everything I want an MP to do requires referencing their sources….

 

Some extra bits…

For those paying close attention, you’ll notice that the top ten MPs in this article isn’t the same as the top ten MPs in the last article. That’s because I download the MPs data in march and then passed (the anonmised) data onto my judges.   Then my laptop got stolen with the corpus on.  I redownloaded the main data and published it last week but it was different data to the data my judges had worked on.  So today’s post is based on the March data and last week’s is based on May data. Hope that clears things up.

Does your MP cite their sources? Or do they make up the facts? Find out here!

Hello everybody!  As I promised weeks ago, I now present the ranking of British Members of Parliament (as was, they currently technically aren’t because we’re in the short campaign) by how much they reference sources on Twitter.  The methodology is here (see the bottom of this post for some clarifications) and the motivation is here.

This is an automatically generated list of MPs, my code counts the number of times that MPs have figures in their tweets and the number of times they are accompanied by a link.

 

So this is good:

Screen Shot 2014-11-23 at 12.42.57

…and this is bad:

Screen Shot 2014-11-23 at 12.42.46

 

Obviously it’s less than perfect scientifically, but if your MP is in the bottom quarter of the list, then perhaps it’s worth asking him/her why…. (if they aren’t on the list at all, it might be because either they lack a Twitter account or because they made less than five tweets that included a number).

 

 

So, the list:

Rank TwitterId Name Tweets Tweets with figures Figures and links Percentage
1= @krishopkins2015 Kris Hopkins 37 7 7 100.00%
1= @cathyjamieson Cathy Jamieson 139 7 7 100.00%
1= @leicesterliz Liz Kendall 170 8 8 100.00%
1= @michaelmeacher Michael Meacher 104 10 10 100.00%
1= @johnredwood John Redwood 137 3 3 100.00%
6 @TessaJowell Tessa Jowell 494 59 55 93.22%
7 @SteveBakerHW Steve Baker 241 14 13 92.86%
8 @marklazarowicz Mark Lazarowicz 118 11 10 90.91%
9= @jonCruddas_1 Jon Cruddas 86 9 8 88.89%
9= @JohnPenroseNews John Penrose 85 9 8 88.89%
11 @AlecShelbrooke Alec Shelbrooke 292 69 59 85.51%
12 @David_Cameron David Cameron 233 32 27 84.38%
13= @jeremycorbyn Jeremy Corbyn 74 6 5 83.33%
13= @hammersmithandy Andy Slaughter 159 6 5 83.33%
13= @VirendraSharma Virendra Sharma 178 12 10 83.33%
13= @MikeCrockart Mike Crockart 281 24 20 83.33%
13= @vincecable Vince Cable 254 30 25 83.33%
13= @SadiqKhan Sadiq Khan 443 30 25 83.33%
13= @dan4barnsley Dan Jarvis 404 48 40 83.33%
20 @edballsmp Ed Balls 232 17 14 82.35%
21 @LucyMPowell Lucy Powell 271 9 7 77.78%
22 @DWard David Ward 370 67 52 77.61%
23= @Y_FovargueMP Yvonne Fovargue 87 8 6 75.00%
23= @spellar John Spellar 419 12 9 75.00%
25 @GarethThomasMP Gareth Thomas 469 49 36 73.47%
26 @GHollingbery George Hollingbery 211 36 26 72.22%
27 @stephen_mosley Stephen Mosley 119 7 5 71.43%
28 @Owen_PatersonMP Owen Paterson 96 7 5 71.43%
29 @EmmaLewellBuck Emma Lewell-Buck 384 7 5 71.43%
30 @YvetteCooperMP Yvette Cooper 131 21 15 71.43%
31 @Ed_Miliband Ed Miliband 292 24 17 70.83%
32 @NiaGriffithMP Nia Griffith 102 10 7 70.00%
33 @grantshapps Grant Shapps 166 20 14 70.00%
34 @StuartAndrew Stuart Andrew 149 6 4 66.67%
35 @MarkPawsey Mark Pawsey 92 6 4 66.67%
36 @IanDavidson4MP Ian Davidson 112 6 4 66.67%
37 @acarmichaelmp Alistair Carmichael 28 6 4 66.67%
38 @KateGreenSU Kate Green 206 18 12 66.67%
39 @AlunCairns Alun Cairns 159 18 12 66.67%
40 @MichaelDugher Michael Dugher 379 21 14 66.67%
41 @tom_watson tom_watson 709 32 21 65.63%
42 @IanMurrayMP Ian Murray 189 26 17 65.38%
43 @RachelReevesMP Rachel Reeves 478 34 22 64.71%
44 @meaglemp Maria Eagle 216 25 16 64.00%
45 @PhilWilsonMP Phil Wilson 258 22 14 63.64%
46 @CharlieElphicke Charlie Elphicke 273 30 19 63.33%
47 @edvaizey Ed Vaizey 239 29 18 62.07%
48 @HarriettBaldwin Harriett Baldwin 252 26 16 61.54%
49 @HenrySmithMP Re-Elect Henry Smith 531 48 29 60.42%
50 @Alison_McGovern Alison McGovern 284 25 15 60.00%
51 @RichardBurdenMP Richard Burden 530 47 28 59.57%
52 @GrahamAllenMP Graham Allen 334 27 16 59.26%
53 @NorwichChloe Chloe Smith 553 47 27 57.45%
54 @JeremyLefroyMP Jeremy Lefroy 82 7 4 57.14%
55 @nick_clegg Nick Clegg 225 14 8 57.14%
56 @GeraintDaviesMP Geraint Davies MP 95 14 8 57.14%
57 @GrahamEvans Graham Evans 240 16 9 56.25%
58 @margarethodge Margaret Hodge 80 9 5 55.56%
59 @grahamemorris Grahame Morris 177 9 5 55.56%
60 @frankfieldteam Frank Field 89 9 5 55.56%
61 @thomasbrake Tom Brake 771 128 70 54.69%
62 @halfon4harlowMP Robert Halfon 455 61 33 54.10%
63 @ChukaUmunna Chuka Umunna 385 26 14 53.85%
64 @ChiOnwurah chi onwurah 394 26 14 53.85%
65 @RobWilson_RDG Rob Wilson 345 32 17 53.13%
66 @FitzMP Jim Fitzpatrick 191 17 9 52.94%
67 @SarahChampionMP Sarah Champion 412 19 10 52.63%
68 @Debbie_abrahams Debbie Abrahams 254 40 21 52.50%
69 @sbrine Steve Brine 158 21 11 52.38%
70 @RobertJenrick Robert Jenrick 169 27 14 51.85%
71 @agriffithsmp Andrew Griffiths 262 27 14 51.85%
72 @Jesse_Norman Jesse Norman 397 29 15 51.72%
73 @lisanandy Lisa Nandy 149 31 16 51.61%
74 @sajidjavid Sajid Javid 261 33 17 51.52%
75 @JackDromeyMP Jack Dromey 636 127 64 50.39%
76 @JYDenham John Denham 67 6 3 50.00%
77 @bernardjenkin Bernard Jenkin 90 6 3 50.00%
78 @damiangreenmp Damian 130 8 4 50.00%
79 @CherylGillanMP Cheryl Gillan 79 10 5 50.00%
80 @AlistairBurtMP Alistair Burt 140 10 5 50.00%
81 @PaulMaskeyMP Paul Maskey 412 16 8 50.00%
82 @PaulBurstow Paul Burstow 383 24 12 50.00%
83 @TristramHuntMP Tristram Hunt 290 26 13 50.00%
84 @GrahamJones_MP Graham Jones 261 32 16 50.00%
85 @NickyMorgan01 Nicky Morgan 290 38 19 50.00%
86 @tpearce003 teresa pearce 578 40 20 50.00%
87 @DamianCollins Damian Collins 292 40 20 50.00%
88 @lucianaberger Luciana Berger 462 41 20 48.78%
89 @MartinChelt Martin Horwood 432 25 12 48.00%
90 @jon_trickett Jon Trickett 390 25 12 48.00%
91 @KevanJonesMP Kevan Jones 499 53 25 47.17%
92 @CarolineLucas Caroline Lucas 486 28 13 46.43%
93 @DavidHansonMP David Hanson 316 67 31 46.27%
94 @karen__bradley Karen Bradley 102 13 6 46.15%
95 @chhcalling Chris Heaton-Harris 188 13 6 46.15%
96 @HackneyAbbott Diane Abbott 652 24 11 45.83%
97 @SeemaMalhotra1 Seema Malhotra 244 11 5 45.45%
98 @mark4ceredigion Mark Williams 149 11 5 45.45%
99 @ChriswMP Chris Williamson 619 103 46 44.66%
100 @lfeatherstone Lynne Featherstone 231 9 4 44.44%
101 @carolinenokes Caroline Nokes 332 27 12 44.44%
102 @JWoodcockMP John Woodcock 555 16 7 43.75%
103 @Simonhartmp Simon Hart 65 7 3 42.86%
104 @RobFlelloMP Rob Flello 108 7 3 42.86%
105 @KevinBrennanMP Kevin Brennan 129 7 3 42.86%
106 @johnmcdonnellMP John McDonnell 201 7 3 42.86%
107 @JBrokenshire James Brokenshire 78 7 3 42.86%
108 @William_Bain William Bain 126 14 6 42.86%
109 @KarlTurnerMP Karl Turner 189 14 6 42.86%
110 @julianhuppert Julian Huppert 333 14 6 42.86%
111 @sarahwollaston Sarah Wollaston 543 21 9 42.86%
112 @EmilyThornberry Emily Thornberry 408 61 26 42.62%
113 @Andrew4Pendle Andrew Stephenson 338 40 17 42.50%
114 @markdurkan Mark Durkan 137 33 14 42.42%
115 @Stewart4Pboro Stewart Jackson 492 55 23 41.82%
116 @HugoSwire Hugo Swire 173 12 5 41.67%
117 @nadhimzahawi Nadhim Zahawi 257 46 19 41.30%
118 @SDoughtyMP Stephen Doughty 296 17 7 41.18%
119 @LorelyBurt Lorely Burt 230 17 7 41.18%
120 @BenPBradshaw Ben Bradshaw 580 39 16 41.03%
121 @annebegg Anne Begg 156 22 9 40.91%
122 @Mary4Wakefield Mary Creagh 450 47 19 40.43%
123 @tessamunt Tessa Munt 164 10 4 40.00%
124 @JTomlinsonMP Justin Tomlinson MP 111 10 4 40.00%
125 @rosie4westlancs Rosie Cooper 152 15 6 40.00%
126 @SimonDanczuk Simon Danczuk 510 25 10 40.00%
127 @LiamByrneMP Liam Byrne 300 30 12 40.00%
128 @RichardGrahamMP Richard Graham 560 83 33 39.76%
129 @BrandonLewis Brandon Lewis 614 68 27 39.71%
130 @simonhughes Simon Hughes 136 43 17 39.53%
131 @olivercolvile Oliver Colvile 300 33 13 39.39%
132 @PaulBlomfieldMP Paul Blomfield 169 13 5 38.46%
133 @EricPickles Eric Pickles 336 65 25 38.46%
134 @fionamacmp Fiona Mactaggart 162 21 8 38.10%
135 @stephen_gilbert Stephen Gilbert 135 8 3 37.50%
136 @GiselaStuart Gisela Stuart 153 8 3 37.50%
137 @davidburrowesmp David Burrowes 119 8 3 37.50%
138 @sheryllmurray Sheryll Murray 245 16 6 37.50%
139 @hilarybennmp Hilary Benn 142 24 9 37.50%
140 @HarrietHarman Harriet Harman 334 30 11 36.67%
141 @patmcfaddenmp Pat McFadden 129 11 4 36.36%
142 @gildernewmp Michelle Gildernew 200 11 4 36.36%
143 @EdwardDaveyMP Edward Davey 42 11 4 36.36%
144 @JamesDuddridge James Duddridge 217 22 8 36.36%
145 @IrrancaDaviesMP Huw Irranca-Davies 455 22 8 36.36%
146 @CarolineFlintMP Caroline Flint 232 22 8 36.36%
147 @JDjanogly Jonathan Djanogly 89 14 5 35.71%
148 @cj_dinenage Caroline Dinenage 177 14 5 35.71%
149 @GregMulholland1 Greg Mulholland 537 42 15 35.71%
150 @Freeman_George George Freeman 554 51 18 35.29%
151 @JasonMcCartney Jason McCartney 364 71 25 35.21%
152 @karlmccartney Karl McCartney 224 20 7 35.00%
153 @Maria_MillerMP Maria_Miller 182 23 8 34.78%
154 @georgegalloway George Galloway 323 23 8 34.78%
155 @ChrisPincher Christopher Pincher 277 32 11 34.38%
156 @SCrabb2015 Stephen Crabb 122 6 2 33.33%
157 @MarkHunter Mark Hunter 72 6 2 33.33%
158 @Mark4WyreForest Mark Garnier 74 6 2 33.33%
159 @JohnGlenMP John Glen 73 6 2 33.33%
160 @NickBolesMP Nick Boles 140 9 3 33.33%
161 @gregknightmp Sir Greg Knight 49 9 3 33.33%
162 @DLidington David Lidington 131 9 3 33.33%
163 @PeterJLuff Sir Peter Luff 158 15 5 33.33%
164 @Margaret_Curran Margaret Curran 251 15 5 33.33%
165 @timfarron Tim Farron 807 18 6 33.33%
166 @RichardBenyonMP Richard Benyon 189 18 6 33.33%
167 @GwynneMP Andrew Gwynne 286 18 6 33.33%
168 @heidi_mp Heidi Alexander 364 27 9 33.33%
169 @IvanLewis_MP Ivan Lewis 351 22 7 31.82%
170 @andyburnhammp Andy Burnham 168 19 6 31.58%
171 @PennyMordauntMP Penny Mordaunt 187 16 5 31.25%
172 @PatGlassMP Pat Glass 81 16 5 31.25%
173 @angelaeagle Angela Eagle 240 16 5 31.25%
174 @EmmaReynoldsMP Emma Reynolds 217 29 9 31.03%
175 @robertabwMP Robert Blackman-Woods 156 13 4 30.77%
176 @MadeleineMoon Madeleine Moon 84 26 8 30.77%
177 @geraldhowarth Sir Gerald Howarth 52 10 3 30.00%
178 @theresecoffey Therese Coffey 674 47 14 29.79%
179 @SamGyimah Sam Gyimah 299 34 10 29.41%
180 @Richard4Watford Richard Harrington 43 7 2 28.57%
181 @HelenJonesMP Helen Jones 142 7 2 28.57%
182 @IainWrightMP Iain Wright 140 14 4 28.57%
183 @S_Hammond Stephen Hammond 225 21 6 28.57%
184 @tomgreatrexmp Tom Greatrex 528 75 21 28.00%
185 @swilliamsmp Stephen Williams 394 36 10 27.78%
186 @ZacGoldsmith Zac Goldsmith 372 11 3 27.27%
187 @IanCLucas Ian Lucas 459 50 13 26.00%
188 @stellacreasy stellacreasy 526 31 8 25.81%
189 @IanAustinMP Ian Austin 521 31 8 25.81%
190 @jameswhartonuk James Wharton 295 8 2 25.00%
191 @alanwhiteheadmp Alan Whitehead 76 12 3 25.00%
192 @tracey_crouch Tracey Crouch 265 16 4 25.00%
193 @stephenctimms Stephen Timms 106 16 4 25.00%
194 @SteveReedMP Steve Reed 592 28 7 25.00%
195 @tobyperkinsmp Toby Perkins 431 53 13 24.53%
196 @AlbertOwenMP Albert Owen 430 41 10 24.39%
197 @Jeremy_Hunt Jeremy Hunt 268 62 15 24.19%
198 @LilianGreenwood Lilian Greenwood 518 54 13 24.07%
199 @Mike_Fabricant Michael Fabricant 515 38 9 23.68%
200 @nickdebois Nick de Bois 660 47 11 23.40%
201 @TomBlenkinsop Tom Blenkinsop 371 35 8 22.86%
202 @RoryStewartUK Rory Stewart 111 18 4 22.22%
203 @MikeGapes Mike Gapes 204 18 4 22.22%
204 @CLeslieMP Charlotte Leslie 442 14 3 21.43%
205 @NicDakinMP Nic Dakin 360 42 9 21.43%
206 @MRitchieMP Margaret Ritchie MP 144 19 4 21.05%
207 @BarrySheerman Barry Sheerman 582 19 4 21.05%
208 @GregHands Greg Hands 445 39 8 20.51%
209 @KeeleyMP Barbara Keeley 115 10 2 20.00%
210 @greggmcclymont Gregg McClymont 211 10 2 20.00%
211 @Mike4Eastleigh Mike Thornton 171 15 3 20.00%
212 @jreynoldsMP Jonathan Reynolds 411 15 3 20.00%
213 @duncanhames Duncan Hames 305 15 3 20.00%
214 @PeteWishart Pete Wishart 773 56 11 19.64%
215 @JennyChapman Jenny Chapman 252 16 3 18.75%
216 @KerryMP Kerry McCarthy 718 48 9 18.75%
217 @PaulFlynnMP Paul Flynn 354 54 10 18.52%
218 @AndySawfordMP Andy Sawford 295 11 2 18.18%
219 @DouglasCarswell Douglas Carswell 457 22 4 18.18%
220 @johnleechmcr John Leech 286 17 3 17.65%
221 @alisonseabeck Alison Seabeck 290 23 4 17.39%
222 @MarkReckless Mark Reckless 425 29 5 17.24%
223 @PeterHain Peter Hain 115 6 1 16.67%
224 @gavinshuker Gavin Shuker 164 6 1 16.67%
225 @angelasmithmp angela smith 86 6 1 16.67%
226 @AnasSarwar AnasSarwar 87 6 1 16.67%
227 @naomi_long Naomi Long 744 25 4 16.00%
228 @andrealeadsom Andrea Leadsom 212 26 4 15.38%
229 @GordonMarsden Gordon Marsden 185 20 3 15.00%
230 @ben4ipswich Ben Gummer 436 27 4 14.81%
231 @JennyWillott Jenny Willott 453 41 6 14.63%
232 @malcolmbruce Malcolm Bruce 91 7 1 14.29%
233 @fabianhamilton Fabian Hamilton 112 7 1 14.29%
234 @ChrisWhite_MP Chris White 148 7 1 14.29%
235 @AnnMcKechinMP Ann McKechin 75 7 1 14.29%
236 @claire4devizes Claire Perry 184 14 2 14.29%
237 @DavidLammy David Lammy 368 21 3 14.29%
238 @NadineDorriesMP Nadine Dorries 620 29 4 13.79%
239 @steve_mccabe Steve McCabe 415 22 3 13.64%
240 @ACunninghamMP Alex Cunningham 211 22 3 13.64%
241 @timloughton Tim Loughton 204 37 5 13.51%
242 @A_DarlingMP Alister Darling 544 23 3 13.04%
243 @GemmaWDMP Gemma Doyle 198 8 1 12.50%
244 @AndyMcDonaldMP Andy McDonald 76 8 1 12.50%
245 @andrewpercy Andrew Percy 758 32 4 12.50%
246 @IanMearnsMP Ian Mearns 571 56 7 12.50%
247 @LouiseEllman Louise Ellman 67 25 3 12.00%
248 @JonAshworth Jonathan Ashworth 569 25 3 12.00%
249 @HelenGoodmanMP Helen Goodman 396 124 14 11.29%
250 @DavidGauke David Gauke 185 27 3 11.11%
251 @iswales Ian Swales 203 28 3 10.71%
252 @DianaJohnsonMP Diana Johnson 267 38 4 10.53%
253 @danielbyles Dan Byles 527 29 3 10.34%
254 @PhilipDaviesMP Philip Davies 181 10 1 10.00%
255 @MichaelMcCannMP Michael McCann 136 10 1 10.00%
256 @DavidJonesMP David Jones 256 10 1 10.00%
257 @normanlamb Norman Lamb 551 21 2 9.52%
258 @George_Osborne George Osborne 483 106 10 9.43%
259 @Siobhain4MandM Siobhain McDonagh 249 71 6 8.45%
260 @uxbridgewalrus Sir John Randall 461 12 1 8.33%
261 @Pauline_Latham Pauline Latham 85 12 1 8.33%
262 @GlynDaviesMont Glyn Davies 184 12 1 8.33%
263 @ericjoyce Eric Joyce 689 36 3 8.33%
264 @Annette4MDNP Annette Brooke 122 14 1 7.14%
265 @joswinson Jo Swinson 626 28 2 7.14%
266 @StephenTwigg Stephen Twigg 185 16 1 6.25%
267 @JohnMannMP John Mann 400 62 3 4.84%
268 @AVMitchell2010 Austin Mitchell 142 7 0 0.00%
269 @HeatherWheeler Heather Wheeler 67 8 0 0.00%
270 @CliveEfford Clive Efford 69 9 0 0.00%
271 @RogerGodsiff Roger Godsiff 54 11 0 0.00%
272 @murrisonMP andrew murrison 130 11 0 0.00%
273 @GutoBebb Guto Bebb 233 16 0 0.00%
274 @AnneMiltonMP Anne Milton 222 20 0 0.00%
275 @nickhurdmp Nick Hurd PPC 170 21 0 0.00%
276 @MPritchardMP Mark Pritchard 174 22 0 0.00%
277 @JohnHowellMP John Howell 174 29 0 0.00%

In general I think removing MPs with a small number of tweets has been sensible (although they are still available in the github). I think that it’s nice to see that some of the bottom 16 from last time have upped their game a little bit, but there is still a lot more to do.

I’ve delivered this slightly differently than I talked about in previous posts.  I was very clear about my methodology and approach and even announced when I’d collected the corpus for anonomising. The plan was to do a main list, and then do something special with the top ten people.

Unfortunately my laptop was stolen on Tuesday night, and with it the whole of my corpus.  I (or my reviewers) still have the anonmised version that they’d worked very hard on, but the resources I need to match up the anonomised tweets with the real ones have gone.  So that’s rubbish.

So, the full ranking of MPs above was compiled in the last 48-hours. As before you can view the code on github (modified to remove tweets before 2015), as well as each individual corpus. In the next post I’m going to do an in-depth look at the top ten, unfortunately it might be the a slightly different top 10 than given here…

 

 

MPs who fail to cite sources: fair warning post.

 gov_avatar-300x300
Before Christmas I did a proof of concept  ranking of MPs based on how often they cited their sources via twitter. It was fairly popular and has resulted in an interesting range of correspondence, so I thought it was time to revisit the post and make it a little more scientifically solid. I believe that the world is a better place when politicians actually cite sources rather than plucking facts out of the air and if I have to embarrass some of them into it then that’s fine by me.
I’m writing (and publishing) this post before we work out the ranking because
  1. good science is setting out your method in advance.
  2. this is the sort of thing where it’s easy to accuse people of political bias
  3. because I’m going to ask for people to help!
As you will recall, the last post did everything automatically because it’s obviously very difficult to go thought all  227,392 of the tweets in the study by hand.
Instead I used some simple code to count up how often the MPs included figures, and how many of those posts included links.  I was very clear that this was a very rough metric at the time and, in fact, this is why I only listed the top 20 MPs and the bottom 16 or so – it’s a little easy for slight style differences to make quite a lot of difference in the ranking.  The main thing, however is this: if you find your MP in the bottom 20 you should probably ask yourself why that’s happened…

However, for the next version I’d like to do something a little more impressive and scientific. Here’s the process I’m going to follow, paying particular attention to the changes from last time.
First: cull the MPs with few tweets. Once I’ve downloaded the fresh data set, I’m going to cull all of those MPs who had fewer than five tweets that included numbers (a worrying thing on it’s own I think, but irrelevant to the main issue), this stops strange things like Glenda Jackson coming very high up because she happened to cite a source in the one tweet she made with a number.
Second: extract the top and bottom 10 tweeters. Culling the MPs will give us a basic ranking. I’m then going to take the top 10 and bottom 10 MPs and subject them to a more rigorous examination. I’m going to anonomise their (full set of) tweets by removing their name and also keywords like ‘Tories’ or ‘Labour’ and scramble the order (I’m also going to remove any links that exist). This anonomised set will go over to a range of volunteer reviewers who will go though and pick out those tweets that represent facts.

If all the reviewers recognise something as an intended fact being quoted then it goes in the list of proper facts, and I then re-calculate the rankings for the top and bottom 10 MPs using this set. So we can be relatively sure that the top and bottom of the list are looked at in a much more scientific way.

Third: Publish

I like to think ‘publish’ goes without saying, but in this case I mean: “publish, along with the code, which will have to be modified, the original set of tweets, the list of MPs after the cull, the set of tweets judged ‘facts’ and all of the numbers involved, under a creative commons licence”.

How you can help

I’m looking for people willing to give me a hand with the ‘identifying facts from within the tweets of the top and bottom ten’ bit.  I’d like to avoid it being ‘Joe’s mates’ because there is an inherent selection bias there.  Feel free to mail joe@reddington.com if you are interested in getting involved.

A politician went “to see” the sea, “to see” what he could see…

So a few weeks ago I produced a tongue-in-cheek-with-a-serious-point post where I ranked MPs by how likely they were to provide citations for facts. It was quite fun and gratifyingly meant I was known to some people before I met them.
Shortly afterwards I was phoned by someone I know who works in politics.  Part of his role was social media and he wanted to know if he was imagining the tendency of MPs to provide tweets of this form:

 

 

If you work in politics, you don’t know many people who understand regular expressions (link to labour list) so I got the call.  I used the same dataset as I did for the citations – there was no point re-spidering for this sort of ’non time critical’ task, plus it meant that I wouldn’t have to add a repo if something interesting turned up.
First of all, in many cases, my friend wasn’t imagining it.  Step forward Andrew Selous MP,  the Conservative MP for South West Bedfordshire. A staggering  18% of his tweets of the classic “to see” format.
Now – there’s nothing wrong with that formation – in fact I quite like it-  it’s a useful format to showcase (in most cases) good work being done in your area.  Here’s the interesting bit.
When I did the citation ranking it was clear that there was no difference between the parties.  All three where represented at both the top and the bottom.  In general MPs where equally bad at citing, you know, evidence.
On the other hand, when you look up the particular structure they use… this is what happens.  Here are the top 30 users of the “to see” tweet formation.
Rank TwitterId Name Tweets “To See” Percent to see
1 @Andrew_SelousMP Andrew Selous MP 651 117 18% Conservative
2 @JackLoprestiMP Jack Lopresti MP 192 32 17% Conservative
3 @RobinWalkerMP Robin Walker 339 41 12% Conservative
4 @CrockartMP Mike Crockart MP 588 59 10% Lib Dem
5 @edvaizey Ed Vaizey 424 42 10% Conservative
6 @MarkLancasterMP Mark Lancaster MP 696 60 9% Conservative
7 @DavidRutleyMP David Rutley 509 42 8% Conservative
8 @BrandonLewis Brandon Lewis MP 615 50 8% Conservative
9 @SarahNewtonMP Sarah Newton 420 34 8% Conservative
10 @JakeBerryMP Jake Berry 557 44 8% Conservative
11 @DLidington David Lidington MP 219 17 8% Conservative
12 @RobertBuckland Robert Buckland MP 288 21 7% Conservative
13 @sheryllmurray Sheryll Murray MP 733 52 7% Conservative
14 @NickyMorgan01 Nicky Morgan MP 564 40 7% Conservative
15 @SCrabbMP Stephen Crabb MP 452 30 7% Conservative
16 @ChrisWhite_MP Chris White MP 395 26 7% Conservative
17 @HelenGrantMP Helen Grant MP 699 45 6% Conservative
18 @karen__bradley Karen Bradley 406 26 6% Conservative
19 @nickhurdmp Nick Hurd MP 441 28 6% Conservative
20 @TobiasEllwoodMP Tobias Ellwood 474 30 6% Conservative
21 @StocktonNorth Alex Cunningham 166 10 6% Labour
22 @George_Osborne George Osborne 871 52 6% Conservative
23 @AlunCairns Alun Cairns 388 23 6% Conservative
24 @MingCampbellMP Ming Campbell 17 1 6% Lib Dem
25 @karlmccartney Karl McCartney 210 12 6% Conservative
26 @pauluppalmp Paul Uppal 623 35 6% Conservative
27 @PhilipDavies422 Philip Davies 109 6 6% Conservative
28 @GBirtwistle_MP Gordon Birtwistle 656 36 5% Lib Dem
29 @JustineGreening Justine Greening 604 33 5% Conservative
30 @cj_dinenage caroline dinenage mp 386 21 5% Conservative
Alex Cunningham looking a little lonely as literally the only Labour MP in the top 30.
Which is bizarre, improbable, and fascinating. I can’t imagine that anyone would change their vote on the basis of this but I have to wonder why it occurs?   The most obvious option is that this is the textbook example taught in Conservative Social Media Training, and what we are seeing in people tweeting the party line as hard as they can…
Any ideas?

The list of US Senators most likely to be making up facts.

My article on Monday: “The list of UK politicians most likely to be making up facts.” was pretty popular, which was nice.  It was actually popular enough that I decided to repeat the process, except instead of using UK MPs I’ve used members of the US senate.  I’m publishing the results in full here.

For those new to the post – I’ve writen some code that fetches a Twitter account’s recent tweets, strips out the retweets and also anything that doesn’t contain a number.  I then compare the number of times the account tweets a number without a link.  I’m NOT interested in who is right on any given issue, I’m interested in raising the level of the debate a little bit.  I’m aware there are some weaknesses in the methodology and I discus them in this post.

Before I give the table, a couple of things surprised me. One was that US senators are, despite their reputation for partisan bickering as much much more likely to cite sources than the UK MPs.  I had 15 MPs who hadn’t cited a single figure in the data on Monday. Today I have none.  Plus the averages are a lot higher – all but two Senators cite more often than a full half of UK MPs.  From a quick perusal it appears likely that this is due to US Senators being much more likely to have a dedicated press office, but that’s a bit of a wild guess.

In any case, here is the full list of US senators and how likely they are to reference figures they tweet about:

Rank Twitter ID Name Tweets in Sample Tweets with numbers Tweets with numbers and links Ratio
1 @SenShelby Richard Shelby 228 21 20 95%
2 @TomCoburn Sen. Tom Coburn M.D. 752 101 96 95%
3 @Mike_Johanns Senator Mike Johanns 575 40 38 95%
4 @SenThadCochran Senator Thad Cochran 797 78 72 92%
5 @SenJohnMcCain John McCain 800 52 48 92%
6 @SenTedCruz Senator Ted Cruz 838 57 52 91%
7 @SenatorSessions Sen. Jeff Sessions 768 34 31 91%
8 @SenStabenow Sen. Debbie Stabenow 790 44 40 91%
9 @TomUdallPress Tom Udall Press 191 54 45 83%
10 @SenatorBaldwin Sen. Tammy Baldwin 766 88 73 83%
11 @SenAlexander Sen. Lamar Alexander 657 86 71 83%
12 @SenBlumenthal Richard Blumenthal 551 40 33 83%
13 @MarkWarner Mark Warner 477 38 31 82%
14 @SenDonnelly Senator Joe Donnelly 789 92 75 82%
15 @MarkeyMemo Ed Markey 711 60 48 80%
16 @SenatorEnzi Mike Enzi 834 90 71 79%
17 @JohnBoozman Senator John Boozman 865 71 56 79%
18 @SenatorCollins SenatorSusanCollins 660 27 21 78%
19 @JerryMoran Jerry Moran 724 88 67 76%
20 @SenDanCoats Senator Dan Coats 610 29 22 76%
21 @SenatorHarkin Tom Harkin 780 95 72 76%
22 @SenatorKirk Mark Kirk 747 115 87 76%
23 @SenMikeLee Mike Lee 749 44 33 75%
24 @SenOrrinHatch Senator Hatch Office 871 106 79 75%
25 @SenToomey Senator Pat Toomey 806 80 59 74%
26 @lisamurkowski Sen. Lisa Murkowski 824 80 59 74%
27 @SenBobCorker Senator Bob Corker 825 38 28 74%
28 @SenJohnThune Senator John Thune 849 60 44 73%
29 @SenSanders Bernie Sanders 857 37 27 73%
30 @Sen_JoeManchin Senator Joe Manchin 784 166 120 72%
31 @MikeCrapo Senator Mike Crapo 790 41 29 71%
32 @MarkUdall Mark Udall 840 61 43 70%
33 @SenAngusKing Senator Angus King 806 60 42 70%
34 @SenatorFischer Senator Deb Fischer 839 79 55 70%
35 @SenJohnBarrasso Sen. John Barrasso 607 92 64 70%
36 @SenJohnHoeven Senator John Hoeven 742 55 38 69%
37 @SenDeanHeller Dean Heller 670 44 30 68%
38 @maziehirono Senator Mazie Hirono 694 69 47 68%
39 @McConnellPress Sen. McConnell Press 703 25 17 68%
40 @PattyMurray Senator Patty Murray 424 40 27 68%
41 @SenFeinstein Sen Dianne Feinstein 844 92 62 67%
42 @SenRonJohnson Senator Ron Johnson 785 46 31 67%
43 @CantwellPress Sen. Maria Cantwell 802 72 48 67%
44 @senrobportman Rob Portman 891 74 49 66%
45 @RoyBlunt Senator Roy Blunt 774 50 33 66%
46 @JohnCornyn JohnCornyn 534 58 38 66%
47 @ChrisCoons Senator Chris Coons 744 55 36 65%
48 @SenatorBurr Richard Burr 835 81 53 65%
49 @SenatorTomUdall Tom Udall 866 435 284 65%
50 @SenatorIsakson Johnny Isakson 689 72 47 65%
51 @SenBennetCO Michael Bennet 744 80 52 65%
52 @SenSherrodBrown Sherrod Brown 874 97 63 65%
53 @SenatorTimScott Tim Scott 747 56 35 63%
54 @RonWyden Ron Wyden 796 50 31 62%
55 @SenGillibrand Kirsten Gillibrand 784 62 38 61%
56 @SenRandPaul Senator Rand Paul 696 31 19 61%
57 @SenCarlLevin Senator Carl Levin 690 54 33 61%
58 @SenatorWicker Senator Roger Wicker 837 64 39 61%
59 @SenPatRoberts Pat Roberts 720 74 45 61%
60 @SenatorMenendez Sen. Robert Menendez 828 61 37 61%
61 @SenJackReed Senator Jack Reed 787 98 59 60%
62 @KellyAyotte Kelly Ayotte 763 70 42 60%
63 @SaxbyChambliss Saxby Chambliss 723 66 39 59%
64 @SenJohnsonSD Senator Tim Johnson 809 84 49 58%
65 @SenatorCardin Senator Ben Cardin 639 33 19 58%
66 @SenWhitehouse Sheldon Whitehouse 812 56 32 57%
67 @SenMarkPryor Senator Mark Pryor 695 95 53 56%
68 @JeffFlake Jeff Flake 839 52 29 56%
69 @timkaine Senator Tim Kaine 804 59 32 54%
70 @InhofePress Inhofe Press Office 783 85 46 54%
71 @SenatorCarper Senator Tom Carper 784 138 73 53%
72 @SenatorLeahy Sen. Patrick Leahy 591 53 28 53%
73 @SenatorBoxer Sen. Barbara Boxer 744 103 54 52%
74 @SenLandrieu Senator Landrieu 787 90 47 52%
75 @SenadorReid Senador Harry Reid 423 54 28 52%
76 @SenBillNelson Bill Nelson 472 45 23 51%
77 @SenBobCasey Senator Bob Casey 723 67 34 51%
78 @MartinHeinrich Martin Heinrich 838 46 23 50%
79 @SenBrianSchatz Senator Brian Schatz 131 6 3 50%
80 @SenatorBarb Barbara Mikulski 797 71 34 48%
81 @SenWalshOffice Sen. Walsh’s Office 172 15 7 47%
82 @SenatorBegich Senator Mark Begich 849 95 44 46%
83 @ChrisMurphyCT Chris Murphy 726 84 38 45%
84 @DavidVitter David Vitter 746 51 23 45%
85 @SenRockefeller Jay Rockefeller 891 80 33 41%
86 @SenJeffMerkley Senator Jeff Merkley 806 49 20 41%
87 @marcorubio Marco Rubio 892 103 42 41%
88 @SenatorDurbin Senator Dick Durbin 845 80 30 38%
89 @SenSchumer Chuck Schumer 768 107 40 37%
90 @GrahamBlog Lindsey Graham 797 51 19 37%
91 @SenatorRisch Senator Jim Risch 279 30 11 37%
92 @SenatorReid Senator Harry Reid 645 42 15 36%
93 @amyklobuchar Amy Klobuchar 737 150 52 35%
94 @clairecmc Claire McCaskill 855 70 24 34%
95 @SenatorHagan Senator Kay Hagan 882 140 45 32%
96 @CoryBooker Cory Booker 790 81 10 12%
97 @SenWarren Elizabeth Warren 257 11 1 9%
98 @ChuckGrassley ChuckGrassley 897 281 6 2%

 

I should say, in defence of Senator Grassley, he has a lot of Tweets of this form:

where he shortens things like ‘for’ into ‘4’ and a very large number of tweets that appear to be reporting volleyball scores, which take down his average somewhat… still the odd link would be useful to see from him.

 

The 16 UK politicians most likely to be making up facts.

This article ranks UK politicians by how likely they are to back up their factual assertions with references. I use their Twitter streams as a starting point.  The method is somewhat noisey and it’s certainly not the sort of thing that I’d call science, but it illustrates some points nicely. As soon as someone comes up with a better method I’ll use that instead. This is a very rough first attempt –  There will be a proper full version around January, with plans to repeat on a monthly basis until the election in May.

I’m going to first give my motivation in terms of Wikipedia.  Then I’m going to describe my methodology such as it is (I’m including the source code), and then give the results.  This post clocks in at 1,500 words so it’s one of the longer ones…

Motivation: Wikipedia cites its sources, MPs should do the same

I like Wikipedia… I genuinely think it’s one of humankind’s greatest achievements…  Many politicians, however, do not…

Shabana Mahmood: …. Gentleman says that I had a career in the accounting industry, but I did not— I was a barrister specialising in professional indemnity litigation. I hope he did not get his information from my Wikipedia entry, which also has me down as two years younger than I am. http://www.theyworkforyou.com/pbc/2013-14/National_Insurance_Contributions_Bill/04-0_2013-11-21a.9.0?s=wikipedia#g9.10

Murdo Fraser: …with the duty, they must understand its meaning. During the evidence-taking sessions, we heard many different definitions of sustainable economic growth—somebody even suggested the one from Wikipedia, although I am not sure that that is helpful to the law-making process. http://www.theyworkforyou.com/sp/?id=2013-11-12.6.0&s=wikipedia#g6.12

Stewart Stevenson: …however one looks at it. Ken Macintosh referred to Fort Augustus and the first hydro power station that was built there. In 1896, the aluminium factory had what is described as—at least in Wikipedia, so it must be true— “the first large-scale commercial hydro-electric” generation. http://www.theyworkforyou.com/sp/?id=2013-06-18.3.0&s=wikipedia#g3.29

Patricia Ferguson: …in the form of a national tree. I sincerely hope that the Scottish Government will agree to formally recognise such an iconic image for our country. While doing some research for the debate, I noted Wikipedia’s bold assertion that the Scots pine is the “national tree of Scotland”. We know that that is not quite true, but it is interesting that such an assumption has been… http://www.theyworkforyou.com/sp/?id=2013-05-22.21.0&s=wikipedia#g21.4

Christopher Pincher: …on both Front Benches. It is also a pleasure to follow the hon. Member for Streatham (Mr Umunna), the shadow Secretary of State, who made a typically assured and polished speech—as I am sure his Wikipedia page will shortly remind us. http://www.theyworkforyou.com/debates/?id=2013-05-10a.259.3&s=wikipedia#g280.2

 

There’s also some small cases of people treating Wikipedia properly…

Paul Flynn: …of the English monarch as de facto Head of State. I want to clear up one point. There is a belief that the hon. Member for Bridgwater and West Somerset (Mr Liddell-Grainger) is 246th in line to the throne, and according to Wikipedia, the authority for that claim is the blog of “Mr Paul Flynn”. I advise anyone who wishes to repeat that claim to treat it with some caution, http://www.theyworkforyou.com/debates/?id=2013-01-28c.695.2&s=wikipedia#g712.5

However, people in glass houses shouldn’t throw stones.   Wikipedia is well known for requiring citations. It’s untrue that ‘every’ statement needs to be cited (although a sizeable proportion of Wikipedians would like it to be true) but in practice every statement that is likely to be challenged should be cited. In many articles this boils down to the same thing.

Politicians, on the other hand, have NO such restriction. When they quote a statistic, or fact, they are under NO obligation to provide a source.  And one of the things that depresses me about politics is that the electorate appear content with this situation.  Facts are important. Context is important. Both are especially important when you are making decisions that affect many many thousands of people.

I appreciate that during a speech, or particularly a debate, it’s a bit tricky to keep saying things like “If you look at goo.gl/8239 you’ll see” , but in the written word, there should be NO hiding.

 

But were can we find a large number of statements made by MPs in such a way that the prevailing culture is to provide links with statements?

 Methodology: Twitter as a proxy

In the UK our MPs are fond of political point scoring on the Microblogging site Twitter. Some of the points are sensible, some are rabble rousing of the worst sort.

Here’s what I did.  I took the set of MPs on Twitter (thank you to @tweetminister for providing the list) and for each member I pulled out all of their tweets over the last little while.  Checking every tweet by hand would be ridiculous, but we can put together some proxies and make the process more accurate.  First of all I want to pull out those tweets that are definitely from the MPs – so we remove the retweets. Next we want the tweets that are definitely statistics.  Examples might be:

Screen Shot 2014-11-23 at 12.25.05Which is definitely a statistic, but we have every reason to suspect that Brandon made it up.  If he’d given a reference, we’d know both the source of the statement and the context, we’d be able to have an informed debate on the subject (to keep this party neutral I also have a go at Rachal Reeves later on)

So what I’m going to do is just extract those tweets that contain the digits ‘0’ to ‘9’. I’m aware that I lose a lot of facts this way, but I certainly improve the fact:tweet ratio.  This is a weakness in the methodology but as I’m going to be comparing MP against MP we can cope with a little bit of noise in the signal.  This leaves us with a relatively small number of tweets (it varies by MP actually, some MPs tweet a lot without ever mentioning a number, something I find very odd).   Some have links in them and some do not. I want to know how many come with links and which are the politicians most likely to pick a fact out of the air.  So I write a little bit of Java code, and gather some results.

All the code is in this github repository.  The repositry contains both the code, and every tweet downloaded from every MP so you can have a look at how fair the approach is for any given MP (and have a sneaky look at how your MP did, every MPs file ends with a line giving their results).

 

Now, let’s be clear – I’m aware that this is rough mertic. I’m aware that a tweet like “If you want to see the 9 unicorns that the Tories are killing go to www.google.co.uk” counts positively.  I’m aware that having a link doesn’t make something true – as XKCD points out much better than I could:
https://i2.wp.com/imgs.xkcd.com/comics/citations.png?w=1000

But what I’m looking for here is the set of politicians that are willing to back up their assertions with something like this:

Screen Shot 2014-11-23 at 12.42.57

…compared to the set of politicians that are willing to just pull figures out of the air (Sorry Rachel, you get to be the good and the bad example).

Screen Shot 2014-11-23 at 12.42.46

 

Results: Cite your sources guys…

So… let’s see what the data tells us.  Here’s the top 20:

Rank TwitterId Name Tweets Tweets with figures Figures and links Percentage
1 @johnredwood John Redwood 890 78 78 100%
2 @michaelmeacher Michael Meacher 691 41 41 100%
3 @CliveBettsMP Clive Betts 233 25 25 100%
4 @MalcolmRifkind Sir Malcolm Rifkind 68 3 3 100%
5 @HazelBlearsMP Hazel Blears 81 2 2 100%
6 @glendajacksonmp Glenda Jackson 102 1 1 100%
7 @edvaizey Ed Vaizey 424 86 81 94%
8 @SMcPartlandMP Stephen McPartland 636 47 41 87%
9 @TonyBaldry Sir Tony Baldry MP 761 15 13 87%
10 @TogetherDarling Alistair Darling 130 7 6 86%
11 @RoryStewartUK Rory Stewart 798 201 170 85%
12 @GarethThomasMP Gareth Thomas MP 745 89 75 84%
13 @mike_weatherley Mike Weatherley MP 589 36 30 83%
14 @Owen_PatersonMP Owen Paterson MP 74 6 5 83%
15 @DanJarvisMP Dan Jarvis 575 53 44 83%
16 @ChrisRuaneMP Chris Ruane MP 360 35 29 83%
17 @nicolablackwood Nicola Blackwood 399 38 31 82%
18 @tcunninghammp1 Tony Cunningham MP 240 26 21 81%
19 @JonCruddasMP Jon Cruddas 602 39 31 79%
20 @JeremyLefroyMP Jeremy Lefroy 816 62 49 79%

Which is pretty impressive… from those 20 MPs… John Redwood storming out ahead and I’ll admit that Glenda Jackson appears to have been a touch lucky with happening to have a link in her single tweeted figure – I may have to introduce a cut off point in future iterations. I’ve limited it to 20 because these are the ones where I’ve a chance to look thought and check I’ve not done something crazy.

…and, more controversially, the bottom 16, and the reason it’s bottom 16, is because I found 16MPs who, in the time period I was looking at, didn’t cite a single figure…   (there were also about 15 who didn’t mention a single figure, which is alarming in it’s own right…)

@AlbertOwenMP Albert Owen MP 303 16 0 0%
@AnneMiltonMP Anne Milton MP 791 64 0 0%
@Craig_Whittaker Craig Whittaker 64 12 0 0%
@GordonBanksMP Gordon Banks MP 257 14 0 0%
@KeeleyMP Barbara Keeley 177 9 0 0%
@Keith_VazMP Keith Vaz MP 741 27 0 0%
@LindsayRoyMP Lindsay Roy MP 81 3 0 0%
@MPritchardMP Mark Pritchard 769 53 0 0%
@PhilipDaviesMP Philip Davies 320 16 0 0%
@RebeccaHarrisMP Rebecca Harris 21 1 0 0%
@SHammondMP Stephen Hammond MP 239 17 0 0%
@Valerie_VazMP Valerie Vaz MP 83 7 0 0%
@damiangreenmp Damian Green 424 29 0 0%
@jreedmp Jamie Reed 361 5 0 0%
@pamela_nash Pamela Nash MP 284 9 0 0%

I should give @AnneMiltonMP a bit of a pass on this.  It turns out that Anne keeps writing things like:

and about once a day:

which, of course, mean that she does undeservedly badly by this metric…

Pamela Nash is relatively blameless but one of her nine is this sort of one:

…all of these MPs might be exactly right with their statistics, but unless they back them up with the links, they are going to keep lowering the level of the debate…

 

 

 

 

Friday Requiem: If you would like to clean up the internet, do it properly.

Quick note for readers.  I think it’s important that I consider my back catalogue of posts to be part of the site and that they get maintained, looked after and followed up on.  So each Friday I’ll be picking a post I did from that week last year, and see if my opinions have changed, or find out how the story develops.

So last year I wrote this:

I used to work for a forensics analysis company, processing hard drives of people that the police had arrested for various horrific child-related things. I lasted about two months and it was deeply deeply awful.

The area (enforcement of the law around making indecent images of children) is massively understaffed, and it’s massively underfunded. Particularly because it’s one of those ‘invisible’ crimes. Public outcries happen at the wrong time – when someone is caught, rather than when the crime is committed and there is never the money to do proactive enforcement of the law.

So if someone wanted to ‘clean up dark corners of the internet’, then the thing to do is to properly enforce the existing laws. To fund the teams that can protect children, to get those teams enough staff that they have time to arrest people. That would make a genuine difference.

Or, if you wanted to save some money, you could announce that you are making something else illegal as well. Yes I’d like to see *that* vanish entirely. But it’s an empty law. There simply isn’t the police staff to enforce it. Yes, a tiny fraction of people who distribute rape porn are going to be arrested (which is a good thing, no question) but for every one of them who gets arrested, someone who distributes child porn won’t be arrested.

If you want to deal with problems like rape porn and child porn then the thing to do is to fund the agencies that deal with it. If, on the other hand, you want votes from people who don’t understand the internet, then keep going as you are.

For those interested in following up on such things – the legislation is now before parliament (until I started doing this Friday Requiem posts I had NO idea how long it took to make a thing illegal. You can read the particular relevant clause of the bill here.  Simultaneously you can also read here about the one in six police jobs that are threatened. Sigh. I think we should protect children, rather than pretend to.

MPs with constituencies that undersupply AAC

So we are all familiar with Stephen Hawking and the machine that allows him to speak.  What you may not know is that there almost 21,000 people in the UK alone who need such a machine – commonly known as Augmentative and Alternative Communication  (AAC) devices.  And the main reason they don’t have one is cost.  Much of the time they are dependent on the heath services to even speak.

In 2012 I made freedom of information requests to every Primary Care Trust  (as they then were) to find out what the AAC landscape looked like.  All the information went into the Domesday Dataset. Dates and details, makes and manufactures, prices and provenance.

The Domesday Dataset, which lists every AAC device that the NHS purchased between 2006 and 2012, remains the richest source in the world of real data about Augmentative Communication at all levels.

When I released the dataset there were some things that particularly shocked me.

It was quite an emotional project… responses ranged from heart-warming (South Tees Hospitals NHS Foundation Trust lists the funders of a laptop used for speech therapy as “Provided by IT Department”) to chilling (Norfolk PCT stated “many patients have chosen to be

reliant on care staff to interpret their needs via the use of closed questions” (https://www.whatdotheyknow.com/request/92957/response/253036/attach/html/2/Communication%20aids.xls.html)

But the shocking thing was finding out that although, there were many areas with good provision (Bristol springs to mind) the NHS Trusts of County Durham, Hull, Surrey, Trafford, Hertfordshire, Kingston, Mid Essex, Newham, Nottingham City, South Tyneside, Tower Hamlets, Wandsworth, Wirral, City and Hackney, Richmond and Twickenham, purchased no aids at all in the years requested.

None at all.  I have close friends grew up in those areas and needed AAC.

So who might be willing to take responsibility for this? Well we should certainly find out.

With the aid of Twitter we worked out the set of Westminister constituencies that are covered by each of those Primary Care Trusts (PCT), Thank you everyone who helped with that.

The line I’ve been getting from a lot of people is that the CCGs that replaced the PCTs are a complete reorganisation of the system – but my thing is that it is the same SLPs and the same managers and the same people making the decisions and I think it’s worth checking…

So let’s look at the MPs constituencies that are in those PCTs.  If your MP appears in this list then feel free to contact them using the link provided 🙂

PCT Constiuency MP Side
  County Durham Bishop Auckland Helen Goodman Lab
  County Durham City of Durham Roberta Blackman-Woods Lab
  County Durham Easington Grahame Morris Lab
  County Durham North Durham Kevan Jones Lab
  County Durham North West Durham Pat Glass Lab
  County Durham Sedgefield Phil Wilson Lab
  Hull Teaching Kingston upon Hull East Karl Turner Lab
  Hull Teaching Kingston upon Hull North Diana Johnson Lab
  Hull Teaching Kingston upon Hull West and Hessle Alan Johnson Lab
  Surrey East Surrey Sam Gyimah Con
  Surrey Epsom and Ewell Chris Grayling Lab
  Surrey Esher and Walton Dominic Raab Con
  Surrey Guildford Anne Milton Con
  Surrey Mole Valley Paul Beresford Con
  Surrey Reigate Crispin Blunt Con
  Surrey Runnymede and Weybridge Philip Hammond Con
  Surrey South West Surrey Jeremy Hunt Con
  Surrey Spelthorne Kwasi Kwarteng Con
  Surrey Surrey Heath Michael Gove Con
  Surrey Woking Jonathan Lord Con
  Trafford Altrincham and Sale West Graham Brady Con
  Trafford Stretford and Urmston Kate Green Lab
  Trafford Wythenshawe and Sale East Mike Kane Lab
  Hertfordshire Broxbourne Charles Walker Con
  Hertfordshire Hemel Hempstead Michael Penning Con
  Hertfordshire Hertford and Stortford Mark Prisk Con
  Hertfordshire Hertsmere James Clappison Con
  Hertfordshire Hitchin and Harpenden Peter Lilley Con
  Hertfordshire North East Hertfordshire Oliver Heald Con
  Hertfordshire South West Hertfordshire David Gauke Con
  Hertfordshire St Albans Anne Main Con
  Hertfordshire Stevenage Stephen McPartland Con
  Hertfordshire Watford Richard Harrington Con
  Hertfordshire Welwyn Hatfield Grant Shapps Con
  Kingston Kingston and Surbiton Edward Davey Lib
  Kingston Richmond Park Zac Goldsmith Con
  Richmond and Twickenham Richmond Park Zac Goldsmith Con
  Richmond and Twickenham Twickenham Vincent Cable Lib
  Mid Essex Braintree Brooks Newmark Con
  Mid Essex Chelmsford Simon Burns Con
  Mid Essex Maldon John Whittingdale Con
  Mid Essex Saffron Walden Alan Haselhurst Con
  Mid Essex Witham Priti Patel Con
  Newham East Ham Stephen Timms Lab
  Newham West Ham Lyn Brown Lab
  Nottingham City Nottingham East Christopher Leslie Lab
  Nottingham City Nottingham North Graham Allen Lab
  Nottingham City Nottingham South Lilian Greenwood Lab
  South Tyneside Jarrow Stephen Hepburn Lab
  South Tyneside South Shields Emma Lewell-Buck Lab
  Tower Hamlets Bethnal Green and Bow Rushanara Ali Lab
  Tower Hamlets Poplar and Limehouse Jim Fitzpatrick Lab
  Wandsworth Battersea Jane Ellison Con
  Wandsworth Putney Justine Greening Con
  Wandsworth Tooting Sadiq Khan Lab
  Wirral Birkenhead Frank Field Lab
  Wirral Wallasey Angela Eagle Lab
  Wirral Wirral South Alison McGovern Lab
  Wirral Wirral West Esther McVey Con
  City and Hackney Teaching Cities of London and Westminster Mark Field Con
  City and Hackney Teaching Hackney North and Stoke Newington Diane Abbott Lab
  City and Hackney Teaching Hackney South and Shoreditch Meg Hillier Lab

That’s 63 constituencies, almost 10%  of the total seats in the parliament.    In those 63 constituencies the NHS provided not one single item of equipment for AAC.

As I said above… I’ve been hearing people say that the CCGs that replaced the PCTs are a complete reorganisation of the system – but my thing is that it’s the same SLPs and the same managers and the same people making the decisions and I think it’s worth checking.

 

 

Friday Requiem: Senator Wants Tracking Devices For Kids With Autism…..

Quick note for readers.  I think it’s important that I consider my back catalogue of posts to be part of the site and that they get maintained, looked after and followed up on.  So each Friday I’ll be picking a post I did from that week last year, and see if my opinions have changed, or find out how the story develops.

Last year,  in this post, I wrote the following… 

Senator Wants Tracking Devices For Kids With Autism – Disability Scoop.

A U.S. senator is asking the Justice Department to provide tracking devices to parents who wish to monitor their children with autism and other developmental disorders who wander.

U.S. Sen. Charles Schumer, D-N.Y., wants the U.S. Department of Justice to offer grant money to local law enforcement agencies so that they can distribute tracking devices to parents who would like the technology in order to help find their children if they go missing.

Being honest, I don’t like this. Yes, I quantify self up to the eyeballs, and yes, if I had kids I’d see very sensible reasons in favor of having drones follow them around… but I’m uneasy about tracking of an individual being prescribed by outside agency. It’s very much one thing for a guardian to say “we have this issue, so I’m going to spend (£/$)100 to get a locator becon” and another for a social worker to say “For people like your ward, we recommend this tracking becon…”

If you’d like some more general discussion on the topic… I’m inclined to point you in the direction of this paper that talks about the general uses of the data, and this paper that talks about the potential problems of using the data.

Screen Shot 2014-11-06 at 20.58.00

….so now it’s time to follow up on the story.  A few things have happened.   Senator Charles Schumer went on to propose ‘Avonte’s Law’: legislation that would allocate $10 million for the program, giving interested parents free access to tracking equipment, which can be worn like a watch or even sewn into clothing.  That was in January.  In May, the bill was officially introduced on to the floor of the senate.

The opencongress site (man, I do love open democracy) has a page for the bill, officially called S. 2386. Indeed – you can read it as a pdf.

In September, the house of representatives (rather than the senate) introduced their own bill, which you can see here.

This is one of the reasons I quite like doing the Friday Requiem posts – you get to properly track the progress of issues.  I’m quite reassured by the language of the bill itself – but given that this is a Democrat Bill, and the Republications control both the Senate and the House, and that this has been one of the least productive congresses in history, I fully expect to be telling you that nothing has changed when I come back to it in 2015… 🙁

 

Solving abuse of disabled parking spaces.

Picture of a disability parking pass

By Tony Webster (Own work) [CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

I’ve written before about how I think banks could solve underage drinking,  and today’s article is on a similar subject. How insurance companies can reduce traffic crime. 

I dislike illegal parking, I particularly dislike people who aren’t disabled using disabled bays.  How about this: 

If you are illegally parked,  your insurance isn’t valid.  

Simple right? and harmless.  No moving parts are required. Just that if you are ignoring the social contract you have with the community, then the insurance company gets to ignore you.  

This works particularly well in terms of being progressive (in taxation sense – a progressive tax is one in which the richer members of society pay more in absolute terms).  While someone would think twice about parking their 1997 Cleo if their insurance was threatened, they’d definitely be parking their top-of-the-range Land Rover somewhere else.  

If we wanted to be properly effective, we can extend the idea.  Full comprehensive insurance? If you are found to be speeding that reverts to third party.  Fire and theft? Only if you’ve paid your car tax.  

Maybe that’s enough to make people think twice. It would certainly stop employers encouraging their delivery vehicles to hover on double yellows for a half hour. 

I’m slightly confused why this doesn’t happen already.  Insurance companies want to make money and are normally willing to use every trick in the book to make it.  How have they missed this one? 

There are already laws, but these are laws that seemingly many people really don’t worry about breaking. How about we get some real skin in the game.  How about we let the insurance companies say “If you walk away from the society’s rules, then society will walk away from you”. 

 

The press: let’s make a deal…

Well, “ethical”, I don’t quite know what the word means, but perhaps you’ll explain what the word means – “ethical”.

Richard Desmond, Express owner and pornographer.(link)

I hold these truths to be self-evident: 

  • That the major newspapers in the UK are, almost without exception, failing in the duty to inform the public in an unbiased way.  
  • That the press in in a position to exert far too much pressure on the government and politicians of all parties. 

But…

  • the only thing worse than a government worried by the press is a government that isn’t.  

So while I feel revolted by almost everything done by large sections of the press (I invite you to work down this section of Richard Littlejohn’s wikipedia article)  I’m a little worried about clipping their wings too far… 

So how about we cut a deal? 

Let’s ramp up (massively) the protection from witch-hunts, publishing made up stories, libel, stalking and all the other weapons used by the press against anyone they like, or even more chilling the individual picked on at random to make a general point (like the dark nightmare version of the national lottery).  

But at the same time let’s massively strengthen the freedom of information act – allow the press access to the documents that show how every aspect of the country is run.  Let it reach down to things like the FA (a national governing body effectively licensed by the government).  Reduce the response times, open up the information. Have automatic releases of any data that isn’t legitimately damaging to release.  

Let’s consider this extract from Tony Blair’s Autobiography

“Freedom of Information. Three harmless words. I look at those words as I write them, and feel like shaking my head till it drops off my shoulders. You idiot. You naive, foolish, irresponsible nincompoop. There is really no description of stupidity, no matter how vivid, that is adequate. I quake at the imbecility of it.
Once I appreciated the full enormity of the blunder, I used to say – more than a little unfairly – to any civil servant who would listen: Where was Sir Humphrey when I needed him? We had legislated in the first throes of power. How could you, knowing what you know have allowed us to do such a thing so utterly undermining of sensible government?”

[….]

“The truth is that the FOI Act isn’t used, for the most part, by ‘the people’. It’s used by journalists. For political leaders, it’s like saying to someone who is hitting you over the head with a stick, ‘Hey, try this instead’, and handing them a mallet. The information is neither sought because the journalist is curious to know, nor given to bestow knowledge on ‘the people’. It’s used as a weapon.”

[…]

“What I failed to realise is that we would also have our skeletons rattling around the cupboard, and while they might be different, they would be just as repulsive. Moreover, I did not at that time see the full implications of the massive increase in transparency we were planning as part of our reforms to ‘clean up politics’. For the first time, details of donors and the amounts given to political parties were going to be published. I completely missed the fact that though in Opposition millionaire donors were to be welcomed as a sign of respectability, in government they would very quickly be seen as buying influence. The Freedom of Information Act was then being debated in Cabinet Committee. It represented a quite extraordinary offer by a government to open itself and Parliament to scrutiny. Its consequences would be revolutionary; the power it handed to the tender mercy of the media was gigantic. We did it with care, but without foresight. Politicians are people and scandals will happen. There never was going to be a happy ending to that story, and sure enough there wasn’t. The irony was that far from improving our reputation, we sullied it.”

Tony Blair, A Journey, Hutchinson, September 2010

Which all, to me, appear to arguments in favour of strengthening the act – fair playing fields, reduced ability for ministers, and officials at all levels to bend the truth, a well-informed electorate. 

Shall we offer this to the press? That we have NO intention to hamstring their role in bringing important issues to the public ear, but we have every intention of stopping the bullying and the witch-hunts? 

Let’s make them the offer – we massively increase the protection of the citizens, and we also increase the transparency of government.  I’m willing to make a deal.   (As part of this deal the Daily Mail might have to also agree to stop talking about Cancer)

I would vote for a politician that knew how to use the subject line.

I asked…

Screen Shot 2014-05-29 at 15.37.15

…and you responded. Any what horrific assaults on the meaning of meaning they were. Cited claims? Not a chance. Specific pledges? Not that might make a difference. Discussion of policy? Don’t make me laugh!

I’m going to complain about one thing today and that’s bad email manners. By which I mean subject lines like:

Screen Shot 2014-05-29 at 15.10.12

and:

Screen Shot 2014-05-29 at 15.17.43

Why have a got a problem with this? It’s because it’s so obviously click-bait of the lowest form. If I get an email from an address that seems familiar with the subject line “Thank you” I’m going to assume I did something nice for someone and I click on it half expecting a nice warm glow instead of the deep disappointment that I’ve been tricked into opening a campaign email of NO validity.

“Telling you first”

Really? You are “telling me first”? I suspect not, I suspect that you are telling millions of people on your email list first, many of whom open your email expecting to be told something useful and urgent only to find that it’s standard political boilerplate.

I would have expected someone at Party HQ to have said “Hey, is it *really* a good idea to put a trivially obvious and direct lie as our subject line? I mention it only because teaching the voters to make a connection between trivially obvious and direct lies might be, you know, suboptimal…” but clearly such people were ignored.

“This just happened”

Surely this is a candidate for the least helpful piece of information ever put out? Now I find I’m associating your party with people who failed ‘basic email training’, when really I want politicians that can cut to the heart of issues like open data, Snowden, digital privacy and the right to be forgotten.

“Will you help”

Thank you Conservatives. Again – if a name that seems vaguely familiar sends me a “Will you help” message I naturally assume that someone I know is in trouble and needs me. So I open it.  When I open it I find that the email should have been titled “We’d like you to vote in three weeks”.

It’s click-bait, it’s utter appalling click-bait and I want so much more from politicians. AND IT’S NOT HARD. When you try and trick me into opening an email I would have opened anyway you treat me like a child.

It’s easy. We, the electorate, would love to be given information.  I personally would like to know what laws you are planning to pass post election. I’d like to know what you think the pros and cons are of various actions. Tell me what the Conservative Party itself is doing about Open Access. Tell me what steps the Labour party is taking for inclusion.

But if the first thing I read in a party email is designed to deceive me in such a transparent way, then I’m going to assume your only intention is to deceive. Treat me like a grown up and, I’m considerably more likely to vote for you.

I love the BBC, but let’s grow up together…

Screen Shot 2014-05-26 at 10.18.17

I love the BBC.

But just because you love something, doesn’t mean that you don’t think it should change.

One of the things I love about the BBC is the radio podcasts. I like the Friday night comedy podcast. Invariably I get around to listening to it on the train the following week and I am exactly that man on the train who randomly bursts into unrestrained laughter as a result.

But I have no idea why that’s one of  few podcasts that Radio 4 offers. It broadcasts (mostly) high quality programs 24 hours a day, free to listen to from anywhere in the world and yet I can choose from just over 100 (great) podcasts going back several years. I can understand, for example, wanting to make a little bit of money out of say, the Mitchell and Webb tapes, but really, why isn’t *everything else* online.  It’s effectively free in terms of infrastructure (If you start with an infrastructure that can support the iPlayer, podcast distribution is not going to strain you). Indeed, there’s not much stopping you making many of your shorter and more topical TV programs into video podcasts – I’d like to be able to watch the news on the train. I understand that I won’t get Have I Got News for You because they want the DVD sales, but surely nobody is suggesting that Saturday Kitchen should be protected in the same way?

More importantly for the world, I’d think journalists should show their sources. I think that when the BBC news website quotes a scientist about their breakthrough they should a) reference the paper, and b) provide links to the audio for the quotes they’ve used. If you tell me someone tweeted something awful, give me a link so that I can see what they said before and after. Build trust in journalists by showing your work. It’s harder for the print services (although I think it should be done) because their online stuff is a carbon copy of their printed stuff, but as BBC news is web based then you should use it. If you quoted four people, then I’d like at least a link to their homepages or twitter feeds because then they get an unfiltered right of reply.

Slightly more controversially. I have no idea why the BBC bids for the rights to show sports. By definition, if there is a bidding process, then the sport is definitely going to be shown on some channel, so why is the BBC driving up the price for other outlets, or worse, spending public money providing a service that the private sector was going to provide anyway? Let ITV have the football, let channel four have the cricket. Sports people are not saying to themselves, ‘I did want to win this competition and validate my life’s training, but now it’s on 5 I don’t think it’s worth it’. Sports fans are not saying ‘Well I was going to watch my childhood team attempt to win the FA cup, but I’m not watching it on the same network that is willing to show an early James Bond film in the mid-afternoon’.

Anyway… bit of a Monday Morning rant that one…

To play us out, here’s Mitch Benn…