| Some relatively-easy-to-measure statistics that will allow us to track usage of #p2. See the methodology notes below for measurement. For some earlier measurements and analysis, see #p2: statistics, with a gender perspective on Liminal States |
| Stats | for last 24 | hours | |||||||||
| Date and time (PDT) | unique contributors in the last week | oldest tweet on #p2 front page | tweets to #p2 | tweets involving 10 most frequent tweeters | crossposted to a conservative hashtag | crossposted to #fem2, #woc, #somewoc, or #women | about / to conservatives | about EFCA or unions | about Iraq, Iran, Afghanistan, or Pakistan | about torture | |
| May 10, 9:30 am | 990 | about 1 hour | 552 | 158 (28%) | 157 (28%) | 27 (5%) | 205 (36%) | ||||
| May 10, 4 pm | 0 | 7 | 10 | ||||||||
| May 11, 3 pm | 972 | 35 min | 804 | 182 (23%) | 264 (33%) | 37 (5%) | 353 (44%) | 9 | 21 | 31 | |
| May 12, 7 pm | 1019 | about 1 hour | 1290 | 246 (19%) | 608 (47%) | 61 (5%) | 670 (52%) | 13 | 23 | 28 | |
| May 13, 7:30 pm | 1084 | 19 min | 1260 | 269 (21%) | 624 (50%) | 44 (4%) | 702 (56%) | 10 | 19 | 108 | |
| May 17, noon | 1128 | about 1 hour | 640 | 270 (42%) | 377 (59%) | 8 (1%) | 386 | 8 | 9 | 29 | |
| May 20, 3 p.m. | 1059 | 22 min | 1900 (est.)* | 557 | 1000 | 47 | 73 | ||||
* twitter search cuts off after 1500 in the last in 20 hours
Participation differences: tweeting vs. hashtag discussion
There are stark differences in participation between the May 14 tweeting and general discussions on the hashtag:
- over 50% of the participants in the tweeting were women, and over 50% of the tweets came from women.
- at least 80% of the most frequent tweeters on the #p2 hashtag are male; 80% or more of the tweets come from men
- none of the most frequent participants on the hashtag attended the tweeting
| Most frequent tweeters, May 10-17, according to wthashtag* | Most frequent participants at May 14 tweeting (details here) | |||
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* wthashtag's order seems to be roughly accurate although the number of tweets they report is far higher than can be found by Twitter search. so take it with a grain of salt. still, it's a useful and easy data point.
here's a query for the tweets from the eight most frequent and tweets involving the 10 most frequent
Most frequent tweeters, May 4-10
Note from Jon: apologies in advance to anybody I overlooked. I calculated this by doing a search on Twitter for tweets from each profile to #p2; for example, the search for Tracy is #p2 from:myrnatheminx
| Overall -- sorted by # of tweets | Female-identified personas (based on avatar, name, web sites, etc; apologies for any miscategorizations) | |||
| 518: jilevin 465: Shoq + p2info 349: mikearama 267: jdp23 + p2pt0 258: buymyshirt 228: davidbadash + gaycivilrights 207: ericgrant 157: yatpundit 151: MichaelShatz 148: Mickey_X | 101: Cody_K 85: Karoli 77: cyn3matic 59: CheshireCat 59: 1txsage1957 55: rizzz 47: StephanieInCA 38: ProfChandler 32: JakeAryehMarcus 31: MadamaAmbi |
Additional notes
p2info ("official" #p2 profile): 17 tweets
myrnatheminx (#p2 hashtag creator): 26
AdrielHampton (#gov20 creator + congressional candidate): 34
drdigipol (#topprog creator): 22
GloPan (#fem2 hashtag creator): 13
JillMz (#diversiytfail/#diversitywin creator): 7
davidbadash and gaycivilrights (#firefoxx creator + his blog): 123 + 105
matttbastard and seasonothebitch (from #rebelleft): 128 and 20 respectively
AntoniaZ and AriMelber (highest-profile journalists involved with #p2, I believe): 13 and 8 respectively
longtime #p2 members baratunde, humanfolly, digitalsista, maegancarberry, sairy, problemchylde, womenwhotech and chrisemeserole were all busy with other things this week and tweeted 3 times or less
May 11-17 data from wthhashtag
- @Cody_K - 851
- @mikearama - 718
- @jilevin - 642
- @fleckman - 493
- @Shoq - 465
- @ericgrant - 416
- @matttbastard - 352
- @buymyshirt - 348
- @bradbaumn - 335
- @GStuedler - 327
Methodology
- unique contributors and from @wthashtag's #p2 page
- list of 10 most frequent tweeters originally from @wthashtag, and then checked manually. more information below
- all other data calculated manually using Twitter search. the links go to the queries we use. to get the actual numbers, i use the "older" link (or directly manipulate the page_id in the search URL) to get back to which page has the oldest tweet in the last 24 hours. you'd think there's a better way but apparently not.