On Left and Right: Understanding the Discourse of Presidential Election in Social Media Communities

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dc.contributor.authorZhou, Lina
dc.contributor.authorTao, Jie
dc.contributor.authorWang, Kanlun
dc.date.accessioned2022-12-27T18:52:48Z
dc.date.available2022-12-27T18:52:48Z
dc.date.issued2023-01-03
dc.description.abstractAs a promising platform for political discourse, social media becomes a battleground for presidential candidates as well as their supporters and opponents. Stance detection is one of the key tasks in the understanding of political discourse. However, existing methods are dominated by supervised techniques, which require labeled data. Previous work on stance detection is largely conducted at the post or user level. Despite that some studies have considered online political communities, they either only select a few communities or assume the stance coherence of these communities. Political party extraction has rarely been addressed explicitly. To address the limitations, we developed an unsupervised learning approach to political party extraction and stance detection from social media discourse. We also analyzed and compared (sub)communities with respect to their characteristics of political stances and parties. We further explored (sub)communities’ shift in political stance after the 2020 US presidential election.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.049
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other5964891e-43da-44d6-b3db-e3876cd799fc
dc.identifier.urihttps://hdl.handle.net/10125/102677
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Science for Digital Collaboration
dc.subjectensemble learning
dc.subjectpolitical party
dc.subjectpresidential election
dc.subjectstance
dc.subjectzero-shot learning
dc.titleOn Left and Right: Understanding the Discourse of Presidential Election in Social Media Communities
dc.type.dcmitext
prism.startingpage396

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