Quantifying & Characterizing Information Diets of Social Media Users

While the area of media studies has traditionally focused mostly on broadcast mass media, with the changing environment it's important to study the news and information consumption of social media users and to also audit how the automated algorithms are modifying what the social media users consume.
In this thesis, we fulfill this high-level goal by following a two-fold approach. First, we propose the concept of information diets -- which is the composition of information being produced or consumed -- to measure and reason about the bias and diversity in information production and consumption on social media platforms. We then quantify the diversity and bias in the information diets that social media users consume via the three main consumption channels on social media platforms: (a) word of mouth channels that users select for themselves by creating social links, (b) recommendations that the social media platform providers give to the users, and (c) the search systems that users use to find interesting information on these platforms. We measure the information diets of social media users along three different dimensions of topics, geographic source diversity, and political perspectives.

Our work is aimed at making social media users more aware of the potential biases in their consumed diets, and at encouraging the development of novel mechanisms for mitigating the effect of these biases through better information discovery and exchange systems on social media.