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