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    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/111627
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111627


    Title: Predicting political affiliation of posts on facebook
    Authors: Chang, Che-Chia;Chiu, Shu-I;Hsu, Kuo-Wei
    徐國偉
    Contributors: 資訊科學系
    Keywords: Classification (of information);Data mining;Forecasting;Information management;Social aspects;Social sciences;Surveys;Text processing;Design features;Facebook;Information exchanging;Interaction features;Nearest Neighbor classifier;Political affiliation;Public opinion polls;Text mining;Social networking (online)
    Date: 2017-01
    Issue Date: 2017-08-03 14:13:44 (UTC+8)
    Abstract: Recently, social media such as Facebook has been more popular. Receiving information from Facebook and generating or spreading information on Facebook every day has become a general lifestyle. This new information-exchanging platform contains a lot of meaningful messages including users' emotions and preferences. Using messages on Facebook or in general social media to predict the election result and political affiliation has been a trend. In Taiwan, for example, almost every politician tries to have public opinion polls by using social media; almost every politician has his or her own fan page on Facebook, and so do the parties. We make an effort to predict to what party, DPP or KMT, two major parties in Taiwan, a post would be related or affiliated. We design features and models for the prediction, and we evaluate as well as compare them with the data collected from several political fan pages on Facebook. The results show that we can obtain accuracy higher than 90% when the text and interaction features are used with a nearest neighbor classifier. © 2017 ACM.
    Relation: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017,
    11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017; Beppu; Japan; 5 January 2017 到 7 January 2017; 代碼 126221
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1145/3022227.3022283
    DOI: 10.1145/3022227.3022283
    Appears in Collections:[資訊科學系] 會議論文

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