Examining Polarization in Political Social Media: A Case of Twitter and the 2011 Canadian Federal Election

Presented at the CAIS 2012

Technology Business

Examining Polarization in Political Social Media:
A Case of Twitter and the 2011 Canadian Federal Election

ANATOLIY GRUZD
SCHOOL OF INFORMATION MANAGEMENT
DALHOUSIE UNIVERSITY, HALIFAX, NS

Email: gruzd@dal.ca Twitter: @dalprof

Outline

Background: 2011 Canadian Federal Election
2011 Election & Social Media
Political Polarization
Research Question & Study Design
Results
Conclusions & Future Work

Twitter: @dalprof SocialMediaLab.ca

Background: 2011 Canadian Federal Election

March 25-26
The three opposition parties rejected the government's
proposed budget
Governor General dissolved the 40th Parliament

May 2 - Election Day

Twitter: @dalprof SocialMediaLab.ca

Background: 2011 Canadian Federal Election
Combination of election period opinion polls

Attribution: Galneweinhaw at en.wikipedia

Twitter: @dalprof SocialMediaLab.ca

2011 Election & Social Media

Twitter: @dalprof SocialMediaLab.ca

2011 Election & Social Media:
Twitter accounts of Party Leaders

Twitter: @dalprof SocialMediaLab.ca

2011 Election & Social Media:
Sample Twitter Communication Network on #elxn41
Top 10 most retweeted
users
1. wikileaks
2. acoyne
3. m_ignatieff
4. RosieBarton
5. JianGhomeshi
6. prjktdemocracy
7. democracycanada
8. can_ada
9. elizabethmay
10. wicary

Source: Twitter, #elxn41, Apr 27-30, 2011

Twitter: @dalprof SocialMediaLab.ca

2011 Election & Social Media:
Political Topics Discussed in the Twitterverse

Source: Twitter, #cdnpoli, Mar 26 - Apr 3, 2011

Twitter: @dalprof SocialMediaLab.ca

2011 Election & Social Media:
Humour in Canadian Politics

Twitter: @dalprof SocialMediaLab.ca

Political Polarization: Modern Examples

Cable news and radio talk shows (Dilliplane, 2011)
Political blogs (Adamic & Glance, 2005)
Facebook (Gilbert & Karahalios, 2009; Gaines & Mondak,
2009)

Twitter: @dalprof SocialMediaLab.ca

Research Question

Whether Canadian Twitter users are likely to cluster around
shared political interests?
Related work on Twitter (Yardi & boyd, 2010; Conover et.al.,
2011)

Twitter: @dalprof SocialMediaLab.ca

Study Design

Data Collection
http://Netlytic.org
A sample of 5,918 messages with
the “#elxn41” hashtag posted by
1,492 people between April 28-30
Social Network Analysis
ORA
UCINET

Twitter: @dalprof SocialMediaLab.ca

Study Design
Manual Classification of Twitter users in the dataset
Based on their self-declared political views and affiliations
- 256 supporters of the Liberal Party of Canada (LPC)
- 221 - New Democratic Party of Canada (NDP)
- 83 - Conservative Party of Canada (CPC)
- 48 - Green Party of Canada (GPC)

Twitter: @dalprof SocialMediaLab.ca

Study Design
Manual Classification of Twitter users in the dataset
Conservative
Spam

Left

Unknown &
Green Undecided

Bloc Liberal
NDP

Other
Twitter: @dalprof SocialMediaLab.ca

Results
Supporters of the four parties had more connections to people in other
parties than to supporters of their own party

Conservative
Liberal
NDP
Green

Twitter: @dalprof SocialMediaLab.ca

Results
Comparison with 10,000 randomly generated networks
Ratio of Observed/Expected connections among political parties’
supporters on Twitter

Conservative Liberal NDP Green
Conservative 3.71 0.79 0.34 0.79
Liberal 0.40 3.43 0.52 1.71
NDP 0.40 1.47 1.60 0.69
Green 0.26 1.71 0.40 6.99

Twitter: @dalprof SocialMediaLab.ca

Conclusions

There are some pockets of political polarization on Twitter
homophily - when people in social networks tend to group around
similar backgrounds and interests, including shared political
views.

Twitter has potential for supporting open cross-ideological
discourse
43% of the accounts in the sample did not explicitly stated their
support for any party or stated support to more than one party

Twitter: @dalprof SocialMediaLab.ca

Future Work

“Even though people are likely to be exposed to a variety of
points of views on Twitter, it is not an effective platform to
carry on meaningful discussions” (Yardi & boyd, 2010)
Content analysis of messages that form across ideological
connections on Twitter

Twitter: @dalprof SocialMediaLab.ca

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This poster presents a prototype Learning Analytics (LA) dashboard to help educators who use Twitter in their teaching. The system summarizes and visualizes information how online learners contribute to Twitter-based discussions by asking questions, sharing resources, and engaging with others. Suggested citation: Gruzd, A. & Conroy, N. (2018). Learning Analytics Dashboard for Twitter. Poster presented at the BayLan Learning Analytics Conference, February 24, 2018, San Francisco, CA, USA.

You're Hired: Examining Acceptance of Social Media Screening of Job Applicants

§ Gruzd, A., Jacobson, J., Dubois, E. (2017). You’re Hired: Examining Acceptance of Social Media Screening of Job Applicants. In Proceedings of the 23rd Americas Conference on Information Systems (AMCIS), August 10-12, 2017, Boston, MA, USA. Available at http://aisel.aisnet.org/amcis2017/DataScience/Presentations/28/ Abstract: The paper examines attitudes towards employers using social media to screen job applicants. In an online survey of 454 participants, we compare the comfort level with this practice in relation to different types of information that can be gathered from publicly accessible social media. The results revealed a nuanced nature of people’s information privacy expectations in the context of hiring practices. People’s perceptions of employers using social media to screen job applicants depends on (1) whether or not they are currently seeking employment (or plan to), (2) the type of information that is being accessed by a prospective em-ployer (if there are on the job market), and (3) their cultural background, but not gender. The findings emphasize the need for employers and recruiters who are relying on social media to screen job applicants to be aware of the types of information that may be perceived to be more sensitive by applicants, such as social network-related information.

Studying Online & Offline Communities through the Prism of Social Media Data

Examining Sentiments and Popularity of Pro- and Anti-Vaccination Videos on Yo...

Abul-Fottouh, D., Song, M. Y., & Gruzd, A. (2020). Examining algorithmic biases in YouTube’s recommendations of vaccine videos. International Journal of Medical Informatics, 104175. Read our follow-up study at https://doi.org/10.1016/j.ijmedinf.2020.104175 ================================= Song, M.Y. & Gruzd, A. (2017). Examining Sentiments and Popularity of Pro- and Anti-Vaccination Videos on YouTube. In Proceedings of the 8th International Conference on Social Media & Society (#SMSociety17). ACM, New York, NY, USA, Article 17, 8 pages. DOI: https://doi.org/10.1145/3097286.3097303

Sampling and recruiting on Facebook

With the rise of new media and social media, a new era of big data has emerged, which has brought about various methodological and theoretical challenges for conducting social research. With over a billion daily active users, Facebook is widely recognized as the leading social media platform in the world. Beyond the use of Facebook to connect people from around the world, Facebook affords various opportunities for academics to conduct research. In this presentation, we will discuss our approach to integrate Facebook data as part of an online survey to study people’s privacy concerns, with a particular focus on methodological challenges associated with sampling and recruiting participants on Facebook. Sampling: Considering, there is no easy searchable directory of Facebook Groups or Pages, how do researchers identify and sample Facebook Groups or Pages? Problematically, it is difficult to systematically sample across Facebook to get a “representative” sample of Facebook users. Facebook Group Directory, algorithmically-filtered search, and custom-curated directories can be used to sample; however, each approach introduces biases and challenges. Recruitment: After the selection of the Group/Page of study, how can researchers invite people to participate in the study? Facebook’s Terms of Service does not allow contacting users directly unless you have conducted “business” with them. We outline the various options for recruitment, including buying an ad, posting directly to the group/page, and contacting the moderator. Ethics: As more Canadians are joining and contributing to Facebook, their automatically recorded data are rapidly becoming available to third parties to mine for both commercial and academic purposes. Ethical questions need to be considered throughout the entire research process. This is particularly true of social media research, which presents unique ethical and personal considerations. In this part of the presentation, we will outline the Social API Terms of Service online guide created by the Social Media Lab at Ryerson University that Internet researchers can use to learn what they can or cannot do with social media collected from sites like Facebook. The presentation will conclude with the discussion of our plans to develop a unique Facebook app that will allow any Facebook user to review their publicly available social media data and participate in our survey on Social Media Privacy Concerns.

Who are We Studying: Humans or Bots?

Social Media Data Collection & Network Analysis with Netlytic and R

Workshop on Data Collection & Network Analysis with @Netlytic & the iGraph R ...