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This paper studies the dynamics of opinion formation and polarization in social media. We investigate whether users stance concerning contentious subjects is influenced by the online discussions they are exposed to and interactions with users supporting different stances. We set up a series of predictive exercises based on machine learning models. Users are described using several posting activities features capturing their overall activity levels, posting success, the reactions their posts attract from users of different stances, and the types of discussions in which they engage. Given the user description at present, the purpose is to predict their stance in the future. Using a dataset of Brexit discussions on the Reddit platform, we show that the activity features regularly outperform the textual baseline, confirming the link between exposure to discussion and opinion. We find that the most informative features relate to the stance composition of the discussion in which users prefer to engage.
In an increasingly polarized world, demagogues who reduce complexity down to simple arguments based on emotion are gaining in popularity. Are opinions and online discussions falling into demagoguery? In this work, we aim to provide computational tool
The novel coronavirus pandemic continues to ravage communities across the US. Opinion surveys identified importance of political ideology in shaping perceptions of the pandemic and compliance with preventive measures. Here, we use social media data t
In online debates individual arguments support or attack each other, leading to some subset of arguments being considered more relevant than others. However, in large discussions readers are often forced to sample a subset of the arguments being put
As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two new communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the ve
Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and may be overly focused and narro