3  Week 3: Web tracking and networks

Last week we discussed the question of online “echo chambers” or “filter bubbles.” For this, we encountered a number of articles employing new computational tools to study these dynamics.

One of these tools was web-tracking—a subject we will discuss in detail this week. We will walk through the steps for generating web-tracking data and also unpack what web-tracking data looks like. We will then be thinking about what problems these techniques solve and which they fail to answer.

A new type of analysis, which we didn’t cover in the articles last week, is so-called “network analysis” where we look at the structure of information flow, human-to-human or user-to-user connections. Here, we will discuss the basic structure of network data as well as how it can be used to tell us something about selective exposure online.

The replication task for this week will give you an example of web tracking network data, what it looks like, and how we can manipulate it to speak to some of the questions in the echo chambers literature.

3.1 Essential reading:

• Conover et al. (2011)

• STIER et al. (2021)

• Flaxman, Goel, and Rao (2016)

3.2 Additional reading:

• Halberstam and Knight (2016)

• Bakshy, Messing, and Adamic (2015)

• Mosleh et al. (2021)

• Chen et al. (2021)

3.3 Slides

Slides for this week are available here

Bakshy, Eytan, Solomon Messing, and Lada A. Adamic. 2015. “Exposure to Ideologically Diverse News and Opinion on Facebook.” Science 348 (6239): 1130–32. https://doi.org/10.1126/science.aaa1160.

Chen, Wen, Diogo Pacheco, Kai-Cheng Yang, and Filippo Menczer. 2021. “Neutral Bots Probe Political Bias on Social Media.” Nature Communications 12 (1). https://doi.org/10.1038/s41467-021-25738-6.

Conover, Michael, Jacob Ratkiewicz, Matthew Francisco, Bruno Goncalves, Filippo Menczer, and Alessandro Flammini. 2011. “Political Polarization on Twitter.” Proceedings of the International AAAI Conference on Web and Social Media 5 (1): 89–96. https://ojs.aaai.org/index.php/ICWSM/article/view/14126.

Flaxman, Seth, Sharad Goel, and Justin M. Rao. 2016. “Filter Bubbles, Echo Chambers, and Online News Consumption.” Public Opinion Quarterly 80 (S1): 298–320. https://doi.org/10.1093/poq/nfw006.

Halberstam, Yosh, and Brian Knight. 2016. “Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter.” Journal of Public Economics 143 (November): 73–88. https://doi.org/10.1016/j.jpubeco.2016.08.011.

Mosleh, Mohsen, Cameron Martel, Dean Eckles, and David G. Rand. 2021. “Shared Partisanship Dramatically Increases Social Tie Formation in a Twitter Field Experiment.” Proceedings of the National Academy of Sciences 118 (7). https://doi.org/10.1073/pnas.2022761118.

STIER, SEBASTIAN, FRANK MANGOLD, MICHAEL SCHARKOW, and JOHANNES BREUER. 2021. “Post Post-Broadcast Democracy? News Exposure in the Age of Online Intermediaries.” American Political Science Review 116 (2): 768–74. https://doi.org/10.1017/s0003055421001222.