Socio-cognitive and group-based processes in adherence to online misinformation

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My research will utilize methods from computational social science, being driven predominantly by cognitive and social psychological theory but applying these insights in a research context using large-scale computational analyses. Occasionally, I also aim to use more traditional, experimental social science research methods to analyse concepts which require a finer granularity and quality of data.Misinformation is a heavily researched topic at the moment. Due to its broadness, my thesis will focus on the role of attitude and demographic-based polarization in amplifying misinformation. I will examine this both from the lens of individual information processing types (socio-cognitive), but also from the perspective of how users' social identities and group memberships can affect the people and information they interact with online (group-based).My research will utilize different sub-projects, two of which are currently in progress. The first project aims to address a research gap around the interplay between online echo-chambers and hostile cross-cutting (intergroup) interactions. Using data from the Reddit platform, I will first cluster together similar forums (called subreddits) on the platform based on shared user-base. I will restrict this to political subreddits to ensure domain relevance and mutual exclusivity between clusters. Using similarity metrics between the subreddits, I will be able to detect distinct communities (for example groups of right-leaning subreddits, left-leaning subreddits, neutral subreddits, etc). Moving to the user level, I will then detect each user's "home" community (based on where they tend to post the most), and derive distribution metrics for their interaction tendencies - for example, some users may post on their home community 100% of the time, while others may only post there 85% of the time (and 15% of the time on other communities). I will utilize Natural Language Processing (NLP)-derived metrics to assess the attributes of user posts, such as toxicity and identity attack, and aggregate these metrics for each user. Having derived this data, I will then be able to regress the NLP metrics (split across home communities and non-home communities) on the user interaction tendencies. The resulting pattern we observe will be the first attempt at quantifying the interplay between echo chambers and wider interaction tendencies, and it will inform future research on thresholds which may need to be met before intergroup interactions become non-hostile on social media.The second ongoing project aims to capture the role of political social identities in adherence to misinformation. It will be a smaller-sample but higher-granularity research than the first, following a social experimental approach. Across three studies, I will 1) manipulate different levels of identity attack on individuals, 2) manipulate the political identity of accounts which share fact-checks to misinformation, and 3) manipulate the sociality of cues which promote analytical thinking in processing information (i.e. whether there is no cue, an individual-level cue, or a group-level cue). For each of these manipulations, I will observe their role on processing style (analytical or intuitive) and subsequently on believability in true or false information. This project is directly relevant to the first, as the results I observe here will allow me to further situate the hostility and identity attack patterns of the first study.