Abstract
Social media debate sites provide a rich collection of public opinions on various controversial issues. In an online debate, participants express their stances by replying directly to the main topic or indirectly to other participants. We observe that the majority of the posts in online debates are interaction posts. In this paper, we propose a new method for the task of stance classification of interaction posts in online debates. We mine the historical activities and textual content of posts to learn the relationships between participants in online debates. Then we build the interaction graph and develop a greedy algorithm to classify participants by stance. Empirical evaluation shows that our method performs better than the baseline methods.
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Acknowledgement
This paper is partially supported by the National Natural Science Foundation of China (NSFC Grant Number 61472006), the Doctoral Program of Higher Education of China (Grant No. 20130001110032) and the National Basic Research Program (973Â Program No. 2014CB340405).
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Rong, X., Wang, Z., Wang, Z., Zhang, M. (2015). Classifying Stances of Interaction Posts in Social Media Debate Sites. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_26
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DOI: https://doi.org/10.1007/978-3-319-22186-1_26
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