what's congress doing on twitter?
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WHAT’S CONGRESS DOING ON TWITTER?Libby Hemphill (@libbyh)
Jahna Otterbacher
Matthew A. Shapiro
Illinois Institute of Technology
Congress’s Tweets/Day
0
500
1000
1500
2000
2500
3000
Descriptive Statistics
N Median S.D. Range
Overall 35,361 1,090 2,134 18-8,893
By Females 5,535 760 873 60-3,677
By Males 29,826* 1,155 2,262 18-8,893
By Republicans 21,253* 1,228 2,544 51-8,893
By Democrats 13,648 825 799 18-3,005
By Independents 460 3,372 0 3,372
By Representatives 28,834* 1,055 2,266 18-8,893
By Senators 6,527 1,219 1.396 165-5,927
* Marks groups who were significantly more active than
their counterparts.
Coding• Getting Code Agreement: 6 coders, 791 tweets, excluding
RTs• Training Data: 526 tweets with binary values for each of 5
codes• Coding Process: Mallet’s MaxEnt classifier on 30,373
tweets
Data• 380 members of Congress• 12/20/2011 – 2/29/2012
Tweets as Speech-Acts
• “in saying something, we do something” – Austin, 1962
•Performance•Goal-orientation
Speech-act Frequencies
Directing to Information
Positioning Narrating Thanking Requesting action
0
2000
4000
6000
8000
10000
12000
14000
Directing to Information – 41%
The Bureau of Labor Statistics
reports today that the US economy
added 200,000 jobs in Dec.
Unemployment falls to 8.5%.
http://t.co/WHZO7RaR
(Rep. Andre Carson, D-IN)
Positioning – 22%
President Obama is again bypassing Congress-this time to give amnesty to an untold number of illegal immigrants- http://t.co/KhqoQBCQ
(Rep. Walter Jones, R-NC)
House Republicans refused to let me speak on House floor today. GOP needs to return to work on #payrolltaxcut. Video: http://t.co/YwZFxwWb
(Rep. Jim Moran, D-VA)
Narrating – 7%
I'm talking to CNN's @randikayecnn
at 1:15pm ET and MSNBC's
@mitchellreports at 1:45pm ET
please tune in! #nhprimary #FITN
(Rep. Debbie Wasserman Schultz,
D-FL)
Thanking – 2%
Thank u Matt Strawn for the
successful leadership u gave to
IaGOP Enjoy a rest. Pls continue to
help us in someway to ur liking
(Sen. Chuck Grassley, R-IA)
Requesting Action – 1%
RSVP to my Immigration Forum
with Rep. Luis Gutierrez this
Saturday in Brooklyn
http://t.co/qTcWugs
(Rep. Yvette Clark, D-NY)
BETWEEN GROUP COMPARISONS
Directing to Information – By Party
Directing to Information – By Sex
Directing to Information – By Chamber
Positioning – By Party
Positioning – By Sex
Positioning – By Chamber
Requesting Action – By Party
Requesting Action – By Sex
Requesting Action – By Chamber
Thanking – By Party
Thanking – By Sex
Thanking – By Chamber
SPEECH-ACTS, AUDIENCE, AND VOTING
Moderated effects – Twitter-action and sub-group – upon following
Speech-Act Narrating Positioning Providing info
Requesting action
Thanking
Male GOP - - -Female GOP
- - -Male Dem - -Female Dem - -F-stat 18.47*** 17.72*** 18.10*** 17.88*** 18.52***
R2 0.02 0.02 0.02 0.02 0.02
Action tweets’ effects on audience size
Mal
e co
nser
vativ
es
Femal
e co
nser
vativ
es
Mal
e lib
eral
s
Femal
e lib
eral
s-100%
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
thanksrequest actionproviding infopositioningnarrative
Predicting Voting Behavior using Frequency of PositioningDependent variable DW-NOMINATE
Speech-act Positioning (raw)
Male GOP (baseline)
0.001*
Female GOP -0.101
Male Dem -0.206***
Female Dem -0.160***
F-stat 27.47
R2 0.27
Takeaways
•Men, Republicans, Representatives
more active
•Broadcast mechanism
• Implicitly campaigning all the time
•Effects on audience not uniform
•More positioning, more polarized
WHERE DO WE GO FROM HERE?
Ongoing Work
• Is Congress polarized like the public?• Does Twitter provide an alternate path to influence?
• How do politicians interact with their constituents?
• How do constituents interact with their politicians?
• What’s happening in the EU? South Korea?
Contact us
• Libby Hemphill (libby.hemphill@iit.edu; @libbyh)
• Jahna Otterbacher (jotterba@iit.edu)
• Matt Shapiro (mshapir2@iit.edu)
Illinois Institute of Technology
@casmlab
http://www.casmlab.org/projects/publicofficials/
https://twitter.com/CaSMLab/lists
SUPPLEMENTARY SLIDES
Why study Congress?• > 90% adoption rate• ~650 tweets per day• Reaching > 35K followers
• Plenty of hype• No traditional media corporation mediating conversation
between officials and constituents
Coding
Golbeck, Grimes, and Rogers
• Getting Code Agreement: 3 coders, 200 tweets
• Coding Process: 3 coders each coded 2/3; 4,626 tweets
• Agreement: Included only tweets with identical codes from two coders
• Codes: Tree scheme, some branches mutually exclusive
Our Study
• Getting Code Agreement: 6 coders, 791 tweets, excluding RTs
• Training Data: 526 tweets with binary values for each of 5 codes
• Coding Process: Mallet’s MaxEnt classifier on 35,361 tweets
Code DefinitionCohen’s kappa N (%)
Narrating Telling a story about their day, describing activities
0.83 2,069
(7%)
Positioning Situating one's self in relation to another politician or political issue, may be implied rather than explicit
0.87 6,728
(22%)
Directing to information
Pointing to a resource URL, telling you where you can get more info
0.70 12,468
(41%)
Requesting action Explicitly telling followers to go do something online or in person (not just visiting a link but asking them to do something like sign a petition, apply, vote) - look for action verbs
0.70 299
(1%)
Thanking Says nice things about or thanks someone else, e.g. congratulations, compliments
0.90 667
(2%)
Comparing automated classifiersClassifier Narr Posit Info ReqAc ThankBayes 0.78 0.74 0.86 0.84 0.90 -0.02 -0.08 -0.06 -0.04 -0.03No stop words 0.74 0.72 0.81 0.73 0.82 -0.05 -0.05 -0.04 -0.09 -0.05DecisionTree 0.80 0.60 0.91 0.91 0.96 -0.05 -0.07 -0.04 -0.04 -0.01No stop words 0.79 0.61 0.90 0.91 0.93 -0.06 -0.05 -0.05 -0.06 -0.04MaxEnt 0.83 0.71 0.91 0.91 0.95 -0.05 -0.06 -0.03 -0.03 -0.03No stop words 0.80 0.71 0.91 0.91 0.93 -0.07 -0.07 -0.04 -0.03 -0.04
Comparing tweet frequency
Model 1 Model 2 Model 3 Model 4
Male 943.309 597.863 593.203 585.361
Republican 1110.179 1105.994 1201.930
Senate -113.932 -134.147
Days in Office 0.077
Constant 1080.438 704.562 732.038 427.333
r2 0.026 0.087 0.088 0.101
All coefficients significant; p < 0.001
Information SourcesDomain Tweets
YouTube.com 2348
Facebook.com 1495
yfrog.com 667
speaker.gov 480
TheHill.com 391
TwitPic.com 321
politico.com 301
online.wsj.com 298
washingtonpost.com 295
Flickr.com 284
Moderated effects – Twitter-action and sub-group – upon following: U-S
Dependent variable in logs
(1)# Followers
(2)# Followers
(3)# Followers
(4)# Followers
(5)# Followers
Speech-Act Narrating Positioning Providing info
Requesting action
Thanking
Male conservatives (baseline)
0.07(0.08)
-0.05(0.04)
-0.01(0.04)
-0.29(0.16)
0.23(0.11)
Female conservatives
-0.01(0.21)
0.09(0.11)
-0.04(0.11)
-0.42(0.45)
0.26(0.36)
Male liberals
-0.22(0.13)
0.12(0.07)
0.09(0.07)
0.27(0.27)
-0.52(0.19)
Female liberals
0.34(0.18)
-0.09(0.11)
0.23(0.10)
0.35(0.48)
-0.45(0.30)
F-stat 18.47*** 17.72*** 18.10*** 17.88*** 18.52***
R2 0.02 0.02 0.02 0.02 0.02
Predicting Voting Behavior using Relative Frequency of PositioningDependent variable DW-Nom.
Speech-act Positioning
Male GOP (baseline)
0.073
Female GOP -0.109*
Male Dem -0.211***
Female Dem -0.156***
F-stat 28.9
R2 0.29
Positioning tweets’ effects on DW-NOMINATE – by subgroup
Male
con
serv
ative
s
Female
con
serv
ative
s
Male
liber
als
Female
liber
als-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
Future Work
•Government Responsiveness• Constituent lobbying efforts• @ replies from MoCs
•Civic Engagement• Voting records• Non-voting political activities
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