Online Exposure to Political and Ideological Content

Online Exposure to Political and Ideological Content

Evidence from Surveys, Web Browsing Histories and Social Media Data

Iulia Cioroianu, University of Bath

In order to understand change (and stability) in political opinions and behaviour, it is necessary to measure the information individuals are exposed to. The internet and social media allow users to interact, collaborate, create and share information in virtual spaces and communities, and have radically changed the political information environment, including the types of content the public is exposed to as well as the exposure process itself. Individuals are faced with a wider range of options (from social and traditional media), new patterns of exposure (socially mediated and selective) and alternate modes of content production (e.g. user-generated content). This talk provides an overview of the main data collection, processing and text analysis methods which can be used to measure and analyse the political information consumed and shared in this dynamic and interconnected online environment.

The methods presented were used by the ExpoNet project team to study online information exposure over the course of the Brexit Referendum campaign. By linking three types of data (surveys, individual web browsing histories and social media data), we were able to: a. evaluate the popularity of different topics and issues during the campaign; b. examine whether online news exposure exhibits signs of segregation and selectivity by capturing exposure to both traditional news sources and news shared via social media platforms; c. examine what types of individuals are more likely to exhibit selective tendencies; d. compare the topics and ideological leanings of articles read during in referendum campaign with those of articles shared on Twitter.

The presentation provides an overview of the ways in which various methods (web scraping,
social media data collection, storage and processing, keyword and dictionary methods, cosine similarity, supervised classification and topic modelling) were combined in a large-scale research project. Moving closer to a causal identification strategy, I also present ongoing work on a web application which informs users about the ideological leaning of the articles they read and allows researchers to account for self-selection effects in information exposure.

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