Modeling Polarization on Social Media Posts: A Heuristic Approach Using Media Bias

Kamal, Sadia; Gullic, Jade; Bagavathi, Arunkumar

doi:10.1007/978-3-031-16564-1_4

Cited by 1 publication

(1 citation statement)

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“…Chen et al (2017) proposed the creation of an opinion-aware knowledge graph for inferring political ideologies. Kamal, Gullic, and Bagavathi (2022) investigated various text representation learning frameworks and supervised machine learning models to analyze the political leanings expressed in social media posts. Ng and Carley (2022) explored several crucial aspects related to cross-dataset model generalization in stance detection, providing practical guidance on effectively generalizing models to new data in real-world applications.…”

Section: Related Workmentioning

confidence: 99%

Online Social Behavior Enhanced Detection of Political Stances in Tweets

Peng,

Zhou,

Zhang

et al. 2024

ICWSM

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Public opinion plays a pivotal role in politics, influencing political leaders' decisions, shaping election outcomes, and impacting policy-making processes. In today's digital age, the abundance of political discourse available on social media platforms has become an invaluable resource for analyzing public opinion. This paper focuses on the task of detecting political stances in the context of the 2020 US presidential election. To facilitate this research, we curate a substantial dataset sourced from Twitter, annotated using hashtags as indicators of political polarity. In our approach, we construct a bipartite graph that explicitly models user-tweet interactions, which provides a comprehensive contextual understanding of the election. To effectively leverage the wealth of user behavioral information encoded in this graph, we adopt graph convolution and introduce a novel skip aggregation mechanism. This mechanism enables tweet nodes to aggregate information from their second-order neighbors, which are also tweet nodes due to the graph's bipartite nature. Our experimental results demonstrate that our proposed model outperforms a range of competitive baseline models. Furthermore, our in-depth analyses highlight the importance of user behavioral information and the effectiveness of skip aggregation.

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Section: Related Workmentioning

confidence: 99%