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| Title: | 以社群媒體為考量之選民政治傾向探索
Predicting the Political Preference of Plurk Users |
| Authors: | 江家榕
Chiang, Chia Jung |
| Contributors: | 陳良弼
Chen, L. P.
江家榕
Chiang, Chia Jung |
| Keywords: | 社群媒體
政治傾向 |
| Date: | 2014 |
| Issue Date: | 2015-03-02 10:13:50 (UTC+8) |
| Abstract: | 近年來，社群媒體的廣為使用，讓人們可以輕易地在社群網站中發表想法
或是接收感興趣的資訊，促使許多研究專注於探究這些大量的個人化資訊
所提供之預測力。
本研究擬從社群媒體著手，以臺灣 2012 總統大選為背景，收集投票日
前六個月選民資料，進而透過文字訊息以及互動結構特徵達成選民政治傾
向分析。實驗結果發現，預測政治熱衷使用者之政治傾向準確度可達
94.08%。
此外，因游離選民通常為選舉致勝關鍵點，本研究不僅僅將選民分為兩
黨，並依據其於選舉前之熱門政治討論議題之立場變化，將其細分為五個
族群(深藍、淺藍、中立、淺綠、深綠)，以拓展應用於其他實務，如競選
策略等，使其更具有實用性。而熱門政治討論議題之選擇可透過以日為單
位，擷取政治新聞關鍵字，並計算其於噗浪上的討論程度決定。最終，可
將 275 名使用者細分為五個群體，並選擇淺藍、中立、淺綠等 208 名為主
要宣傳目標，以提升競選策略成效。
Nowadays, the use of social media is increasingly popular all over the world.
People can easily express their thoughts or receive information that they are
interested in via social media. Many studies have focused on exploring the
predictive power of the large amount of data generated from social media.
In this thesis, we address the problem of predicting the political preference
of social media users given the data of their past activities on Plurk and
evaluating our approach on the Taiwan 2012 presidential election. We first
collected Plurk messages posted six months before the election day. By building
predicting models based on a variety of contextual and behavioral features, we
find that predicting political preference of active users achieved up to 94.08%
classification accuracy. In the meanwhile, in order to extend the usability of our
work, we further use our models to analyze the change of user political
preference based on political events which happened before the election.
Identifying people who change their political preference frequently or stay
neutrally allows a candidate to design strategies to affect these people. All of the
political events are automatically selected by the popularity of political
keywords used in Plurk, and keywords can be extracted from daily political
news. In the end, we get 208 swing voters from 275 voters, who become the
main targets for enhancing the effectiveness of the campaign strategy. |
| Reference: | [AH10] S. Asur and B. A. Huberman, “Predicting the future with social media,” International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’10), 2010, Vol. 1, pp.492-499.
[BF10] L. Barbosa and J. Feng, “Robust sentiment detection on Twitter from biased and noisy data,” Proceedings of the International Conference on Computational Linguistics (COLING`10), 2010, pp. 36-44.
[BK12] A. Boutet, H. Kim, and E. Yoneki, “What’s in Your Tweets? I Know Who You Supported in the UK 2010 General Election,” Proceedings of the International Conference on Weblogs and Social Media (ICWSM’12), 2012.
[BM11] J. Bollen, H. Mao and X. Zeng, “Twitter mood predicts the stock market,” Journal of Computational Science, 2011,2(1), pp.1-8.
[CG11] M. D. Conver, B. Goncalves, J. Ratkiweicz, A. Flammini, F. Menczer, “Predicting the Political Alignment of Twitter Users,” Proceedings of the IEEE Conference on Social Computing (SocialCom’11), 2011.
[CL11] C. C. Chang and C. J. Lin, “LIBSVM : a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, 2011,2:27:1--27:27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[CW13] C. Chen, K. Wu, V. Srinivasan and X. Zhang, “Battling the Internet Water Army: Detection of Hidden Paid Posters,” Proceedings of the International Conference on Advances in Social Network Analysis and Mining (ASONAM’13), 2013.
[CZ12] A. Cui, H. Zhang, Y. Liu, M. Zhang, and S. Ma, “Lexicon-Based Sentiment Analysis on Topical Chinese Microblog Messages,” Proceedings of the the Joint Conference of the Chinese Semantic Web Symposium (CSWS’12) and the Chinese Web Science Conference (CWSC`12), 2012.
[DD06] Z. Dong and Q. Dong, “HowNet and the Computation of Meaning,” World Scientific Publishing Co., Inc., River Edge, NJ, 2006.
[ES06] A. Esuli and F. Sebastiani, “SentiWordNet: A publicly available lexical resource for opinion mining,” Proceedings of the Conference on Language Resources and Evaluation (LREC’06), 2006, pp. 417–422.
[FB13] Clay Fink, Nathan Bos, Alexander Perrone, Edwina Liu, and Jonathon Kopcky, “Twitter, Public Opinion, and the 2011 Nigerian Presidential Election,” Proceedings of the IEEE Conference on Social Computing (SocialCom’13), 2013.
[GB09] Go. A, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” Technical Report, Stanford Digital Library Technologies Project, 2009.
[GM11] D. Gayo-Avello, P. T. Metaxas and E. Mustafaraj, “Limits of Electoral Predictions using Twitter,” Proceedings of the International Conference on Weblogs and Social Media (ICWSM’11), 2011.
[JC13] F. Jiang, A. Cui, Y. Liu, M. Zhang and S. Ma, “Every Term Has Sentiment: Learning from Emoticon Evidences for Chinese Microblog Sentiment Analysis,” Proceedings of the Conference on Natural Language Processing and Chinese Computing (NLP&CC’13), 2013.
[KC07] L. W. Ku and H. H. Chen, "Mining Opinions from the Web: Beyond Relevance Retrieval," Journal of American Society for Information Science and Technology, Special Issue on Mining Web Resources for Enhancing Information Retrieval, 2007, Volume 58 Issue 12, pp.1838-1850.
[LK77] J. R. Landis and G. G. Koch, "An Application of Hierarchical Kappa-type Statistics in the Assessment of Majority Agreement among Multiple Observers," Biometrics, 1977, Vol. 33, No. 2, pp. 363-374.
[LM11] C. Lui, P. T. Metaxas, and E. Mustafaraj, ”On the predictability of the US elections through search volume activity,” Proceedings of the IADIS International Conference on e-Society, 2011.
[LW12] H. C. Liu and J. H. Wang, “Social Influence Estimation for Short Texts in Plurk,” Proceedings of the International Conference on Advances in Social Network Analysis and Mining (ASONAM’12), 2012.
[MR13] A. Makazhanov and D. Rafiel, “Predicting Political Preference of Twitter Users,” Proceedings of the International Conference on Advances in Social Network Analysis and Mining (ASONAM’13), 2013.
[OB10] B. O’Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, “From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series,” Proceedings of the International Conference on Weblogs and Social Media (ICWSM’10), 2010.
[OB13] S. O’Banion and L. Birnbaum, “Using Explicit Linguistic Expressions of Preference in Social Media to Predict Voting Behavior,” Proceedings of the International Conference on Advances in Social Network Analysis and Mining (ASONAM’13), 2013.
[PL08] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundations and Trends in Information Retrieval, 2008, Vol.2, No. 1-2, pp. 1-135.
[PP11] Marco Pennacchiotti, Ana-Maria Popescu,” Democrats, Republicans and Starbucks Afficionados: User Classification in Twitter,” Proceedings of the 17th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11), 2011.
[S10] F. Santo, “Community detection in graphs,” Physics Reports, Vol.486, 2010, pp.75-174.
[TS10] A. Tumasjan, T. O. Sprenger, P. G. Sandner and I. M. Welpe, “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment,” Proceedings of the International Conference on Weblogs and Social Media (ICWSM’10), 2010.
[李游12] 李政儒, 游基鑫, 和陳信希, “廣義知網詞彙意見極性的預測,” 中華民國計算語言學學會, 2012.
[陳黃04] 陳克健, 黃淑齡, 施悅音, 和陳怡君, “多層次概念定義與複雜關係表達－繁體字知網的新增架構,” 漢語詞彙語義研究的現狀與發展趨勢國際學術研討會, 2004.
[孫陳10] 孫瑛澤, 陳建良, 劉峻杰, 劉昭麟, 和蘇豐文, “中文短句之情緒分類,” Proceedings of the Conference on Computational Linguistics and Speech Processing (ROCLING’10), 2010. |
| Description: | 碩士
國立政治大學
資訊科學學系
101753008
103 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G1017530081 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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