請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17521完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊裕澤(Yuh-Jzer Joung) | |
| dc.contributor.author | Wen-Sheng Lan | en |
| dc.contributor.author | 藍文聖 | zh_TW |
| dc.date.accessioned | 2021-06-08T00:18:14Z | - |
| dc.date.copyright | 2020-08-07 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-05 | |
| dc.identifier.citation | Baly, R., et al. (2018). Predicting factuality of reporting and bias of news media sources. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Baly, R., et al. (2019). Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Bouma, G. J. P. o. G. (2009). 'Normalized (pointwise) mutual information in collocation extraction.' 31-40. Chomsky, N. and D. W. Lightfoot (2002). Syntactic structures, Walter de Gruyter. D'Alessio, D. and M. Allen (2000). 'Media bias in presidential elections: A meta‐analysis.' Journal of communication 50(4): 133-156. Dallmann, A., et al. (2015). Media bias in german online newspapers. Proceedings of the 26th ACM Conference on Hypertext Social Media, ACM. DeMarzo, P. M., et al. (2003). 'Persuasion bias, social influence, and unidimensional opinions.' The Quarterly Journal of Economics 118(3): 909-968. Druckman, J. N. and M. Parkin (2005). 'The impact of media bias: How editorial slant affects voters.' The Journal of Politics 67(4): 1030-1049. Eberl, J.-M., et al. (2017). 'One bias fits all? Three types of media bias and their effects on party preferences.' 44(8): 1125-1148. Eberl, J.-M., et al. (2018). 'Party Advertising in Newspapers: A source of media bias?' Journalism Studies 19(6): 782-802. FAIR, F. A. i. R. (2011). 'What’s Wrong With the News?'. from https://fair.org/about-fair/whats-wrong-with-the-news/. Gentzkow, M. and J. M. Shapiro (2006). 'Media bias and reputation.' Journal of political Economy 114(2): 280-316. Gentzkow, M. and J. M. Shapiro (2010). 'What drives media slant? Evidence from US daily newspapers.' Econometrica 78(1): 35-71. Hamborg, F., et al. (2019). 'Automated identification of media bias in news articles: an interdisciplinary literature review.' International Journal on Digital Libraries 20(4): 391-415. Haselmayer, M., et al. (2017). 'Partisan bias in message selection: Media gatekeeping of party press releases.' 34(3): 367-384. Hopmann, D. N., et al. (2012). 'Party media agenda-setting: How parties influence election news coverage.' 18(2): 173-191. Iyyer, M., et al. (2014). Political ideology detection using recursive neural networks. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Kim, Y. (2014). Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Kulkarni, V., et al. (2018). Multi-view models for political ideology detection of news articles. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Li, J., et al. (2018). A Survey on Deep Learning for Named Entity Recognition. Li, P.-H., et al. (2020). Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER. AAAI. Liu, B. (2012). Sentiment analysis and opinion mining. Lu, B. (2010). Identifying opinion holders and targets with dependency parser in Chinese news texts. Proceedings of the NAACL HLT 2010 student research workshop. Morstatter, F., et al. (2018). 'Identifying Framing Bias in Online News.' ACM Transactions on Social Computing 1(2): 5. Mullainathan, S. and A. Shleifer (2002). Media bias, National Bureau of Economic Research. Newman, N., et al. (2019). Reuters institute digital news report 2019, Reuters Institute for the Study of Journalism. Niculae, V., et al. (2015). Quotus: The structure of political media coverage as revealed by quoting patterns. Proceedings of the 24th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee. Pang, B., et al. (2002). Thumbs up?: sentiment classification using machine learning techniques. Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, Association for Computational Linguistics. Patricia Aires, V., et al. (2019). A Link-based Approach to Detect Media Bias in News Websites. Companion Proceedings of The 2019 World Wide Web Conference, ACM. Rabiner, L. R. (1989). 'A tutorial on hidden Markov models and selected applications in speech recognition.' Proceedings of the IEEE 77(2): 257-286. Sim, J. and C. C. J. P. t. Wright (2005). 'The kappa statistic in reliability studies: use, interpretation, and sample size requirements.' 85(3): 257-268. Smirnova, A. V. J. D. and Communication (2009). 'Reported speech as an element of argumentative newspaper discourse.' 3(1): 79-103. Stefanov, P., et al. (2019). Predicting the Topical Stance of Media and Popular Twitter Users. Taboada, M., et al. (2011). 'Lexicon-based methods for sentiment analysis.' Computational linguistics 37(2): 267-307. Tang, D., et al. (2015). Document modeling with gated recurrent neural network for sentiment classification. Proceedings of the 2015 conference on empirical methods in natural language processing. Wallach, H. M. (2004). Conditional random fields: An introduction: 22. Wang, S.-M. and L.-W. Ku (2016). ANTUSD: A large Chinese sentiment dictionary. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16). Wanxiang Che, Z. L., Ting Liu (2010). LTP: A Chinese Language Technology Platform. Proceedings of the Coling 2010:Demonstrations. Yanagizawa-Drott, D. (2014). 'Propaganda and conflict: Evidence from the Rwandan genocide.' The Quarterly Journal of Economics 129(4): 1947-1994. Zhang, L., et al. (2018). 'Deep learning for sentiment analysis: A survey.' Wiley Interdisciplinary Reviews: Data Mining Knowledge Discovery 8(4): e1253. 张伟, et al. (2004). 学生褒贬义词典, 中国大百科全书出版社. 林裕展 and 羅文輝 (2010). '臺灣電視公司四屆總統選舉新聞報導政黨偏差研究.' 選舉研究 17(1): 55-90. 徐琳宏, et al. (2008). 情感词汇本体的构造. 國家通訊傳播委員會 (2019). 107 年地方公職人員選舉競選期間電視新聞報導觀察統計委託研究案. 許瓊文, 唐. (2019). 2019台灣新聞媒體可信度研究, 財團法人台灣媒體觀察教育基金會. 陳文俊 (2003). '藍與綠—台灣選民的政治意識型態初探.' 選舉研究 10(1): 41-80. 黃怡嘉 (2008). 2008 年電視總統選舉新聞的政治偏差: 1-122. 蕭怡靖 and 鄭夙芬 (2014). '台灣民眾對左右意識型態的認知: 以統獨議題取代左右意識型態檢測台灣的政黨極化.' 台灣政治學刊 18(2): 79-138. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17521 | - |
| dc.description.abstract | 網路新聞是大眾重要的信息來源,至少已經部分替代了電視或印刷媒體等傳統媒體。但與其他媒體一樣,網路新聞媒體也可能受到媒體偏差的影響。媒體偏差是指記者或新聞編輯在選擇要報導的事件與報導的方式時造成的偏差,代表與新聞報導的標準產生了偏差。媒體偏差可能使媒體無法精準傳達事實並造成大眾對於媒體的不信任感,因此媒體偏差的認知與偵測是十分重要的議題。本研究使用既有研究中最具代表性的媒體偏差架構針對2020大選前三個月網路新聞媒體的政治新聞進行媒體偏差分析,為了分析這一龐大的資料集並比較不同網路新聞媒體的潛在政治傾向,我們提出了各種可以揭示媒體偏差的衡量指標。此外,本研究提出了一個可以從新聞文章中擷取出政治人物發言的方法與特徵級別情緒分析做法,兩者皆取得了不俗的準確度。最後根據分析結果,本研究發現台灣網路新聞媒體確實存在媒體偏差且大部分都擁有其潛在的政治傾向偏好。 | zh_TW |
| dc.description.abstract | Internet news is an important source of information for the public, and has at least partially replaced traditional media such as television or print media. However just like other media, online news media may also be affected by media bias. Media bias refers to the bias caused by the reporter or news editor in choosing the event to be reported and the reporting method, which represents a deviation from the standard of news reporting. Media bias may prevent the media from fairly and faithfully communicating facts and cause the public to distrust the media. Therefore, the recognition and detection of media bias is a very crucial issue. This study uses the most representative media bias framework in existing research to conduct media bias analysis on the political news of online news media three months before the 2020 presidential election in Taiwan. In order to analyze this huge data set and compare the potential political tendency of different online news media, we have put forward various metrics that can reveal media bias. In addition, this research proposes a method that can extract politicians' speeches from news articles and a feature-level sentiment analysis method, both of which have achieved satisfying accuracy. Finally, based on the analysis results, this study found that Taiwan’s online news media do exist media biases and most of them have potential political preferences. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T00:18:14Z (GMT). No. of bitstreams: 1 U0001-0508202008522300.pdf: 2144858 bytes, checksum: 27c87cfffe636d6f7a279f39961414eb (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 第一章 緒論 7 第二章 文獻探討 9 一. 媒體偏差定義 9 二. 台灣媒體與政治環境 10 三. 自然語言處理與情緒分析 11 四. 媒體偏差偵測 16 第三章 研究流程 21 一. 資料集建構 21 二. 資料前處理 22 三. 政治人物發言擷取 23 四. 情緒分析 27 第四章 媒體偏差分析與結果 34 一. 媒體偏差分析 34 二. 媒體偏差分析結果 38 第五章 結論 48 參考文獻 50 | |
| dc.language.iso | zh-TW | |
| dc.title | 運用自然語言處理偵測台灣網路新聞媒體偏差 | zh_TW |
| dc.title | Detecting media bias in online news in Taiwan using natural language processing | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 曹承礎(Seng-Cho Chou),盧信銘(Hsin-Min Lu),孔令傑(Ling-Chieh Kung) | |
| dc.subject.keyword | 媒體偏差,自然語言處理,網路政治新聞, | zh_TW |
| dc.subject.keyword | Media Bias,Natural Language Processing,Internet Political News, | en |
| dc.relation.page | 53 | |
| dc.identifier.doi | 10.6342/NTU202002429 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-05 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位: | 資訊管理學系 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-0508202008522300.pdf 未授權公開取用 | 2.09 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
