Investigating the mechanism political polarization on social media

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My research investigates affective polarization by focusing on the expressed hostility in cross-cutting interactions. Affective polarization is rooted in social group identity theory, which suggests that group membership can trigger more positive emotional reactions toward the ingroup than the outgroup and a greater willingness to cooperate with members of the ingroup. Taking such ingroup favoritism and outgroup animosity into consideration can offer a more complete picture of political polarization. I employ NLP techniques to detect the expressed sentiment and opinion in the tweet content of interactions, then measure affective polarization by examining how expressed sentiments and opinions vary between in-group interaction and cross-cutting interaction.