Predicting User's Political Party using Ideological Stances

• Main Authors: GOTTOPATI, Swapna, QIU, Minghui, YANG, Liu, ZHU, Feida, JIANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Collaborative Filtering; Ideological Stances; Memory-based CF; Model-based CF; Probabilistic Matrix Factorization; Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Influence and Political Communication; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/2097; https://ink.library.smu.edu.sg/context/sis_research/article/3096/viewcontent/SOCINFO_13_45.pdf
• Institution: Singapore Management University

Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties’ ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users’ ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task on debate.org dataset. The experiments show that using ideological stances with Probabilistic Matrix Factorization (PMF) technique achieves a high accuracy of 88.9% at 22.9% data sparsity rate and 80.5% at 70% data sparsity rate on users’ party prediction task.