整合深度學習技術與SOR理論之資訊情緒傳遞性探索：新聞生成特質與資訊情感傳播行為

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：93

、訪客IP：3.147.66.250

姓名

董欣澄(Xin-Cheng Dong) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

整合深度學習技術與SOR理論之資訊情緒傳遞性探索：新聞生成特質與資訊情感傳播行為
(Integrating Deep Learning Technique And SOR Theory to Explore The Transmission of Information Emotion: News Generation Characteristics And Information Emotion Dissemination Behavior)

相關論文

★ 以機器學習技術為基礎建構新生兒孕育健康狀態預測模型	★ 電子病歷縮寫消歧與一對多分類任務
★ 使用文字探勘與深度學習技術建置中風後肺炎之預測模型	★ 精準社群廣告投資策略：以機器學習技術為基礎之社會影響力管理模式
★ 智慧活躍老化之實現：以資料驅動為基礎之AI長者在地交友推薦模式	★ 基於混合過濾的電影推薦系統
★ 以 Reddit 使用者生成內容探討糖尿病照護社會支持	★ 防範於未然：基於機器學習技術之網路入侵偵測系統
★ 可靠度驗證實驗室導入人工智慧技術的可行性探討 -以A公司為例	★ 智慧共同照護之實現: 以資料驅動為基礎之 AI 糖尿病個案管理模式
★ Non-Touch Cooperation: An Interactive Mechanism Design Based on Mid-Air Gestures	★ 基於 UGC 的反脆弱社交口碑策略:以臺灣飯店業為例
★ 基於機器學習技術之誘導式評論過濾機制：以餐廳評論為例	★ 資料驅動的球隊經營：NBA球隊競爭力與抱團策略之研究
★ 基於顧客評論觀點揭示組織調整策略和顧客體驗優化：以 Covid-19 餐飲業為例	★ 網路購物詐欺偵測系統：結合社群網路行為的方法

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-9-26以後開放)

摘要(中)

近年來社交媒體興起，閱聽者消費新聞的方式發生變化，傳統新聞媒體也朝向網路發展，新聞媒體間的競爭日漸加劇，競爭的環境衍伸出許多問題，新聞報導理應中立且客觀，然而在資訊爆炸的時代下，新聞媒體傾向在撰寫標題時加入了許多會激發閱聽者情緒的詞彙，利用誇大且聳動的新聞標題吸引閱聽人點擊新聞以賺取更多的廣告收入，新聞媒體此種寫作手法違背自身所應遵守的原則，最終失去民眾對於新聞媒體的信任與尊重，同時社群媒體將新聞傳播得更快更廣，用戶更容易暴露於負面的新聞環境中，長期下來除了誤導閱聽人外更會影響其心理健康和情緒。因此，本研究目的為探討新聞文本內容的生成特質對於資訊情感傳播的連鎖效應。本研究蒐集台灣主流媒體新聞，並訓練BERT模型分類出影響閱聽人情緒的新聞標題，另外透過以刺激-有機體-反應(Stimuli-Organism-Response, SOR)理論進行統計分析，藉此了解網路新聞特質對於閱聽人於社群媒體傳播影響力和刺激反應。結果顯示，新聞標題情緒會顯著影響閱聽人傳播行為和傳播後的情緒反應。首次將SOR理論引入社交媒體新聞分享，除了為未來相關研究提供基礎，也提供新聞媒體與閱聽人了解數位新聞環境所面臨的問題。

摘要(英)

In recent years, with the rise of social media, the way readers consume news has changed, and traditional news media have also developed online. The competition among news media has intensified. Many problems have arisen from the competitive environment. News reporting should be neutral and objective. In the era of information explosion, news media tend to add a lot of words that will arouse the emotions of readers when writing headlines, and use exaggerated and sensational news headlines to attract readers to click on news to earn more advertising revenue, this kind of writing style violates the principles that one should abide by, and ultimately loses the public’s trust and respect for the news media. At the same time, social media spreads news faster and more widely, and users are more likely to be exposed to a negative news environment. Misleading readers can even affect their mental health and mood. Therefore, the purpose of this study is to explore the chain effect of the generative characteristics of news text content on the emotional dissemination of information. This study collects news from mainstream media in Taiwan, and trains the BERT model to classify news headlines that affect readers′ emotions. In addition, it conducts statistical analysis based on the Stimuli-Organism-Response(SOR) theory to understand the online News characteristics are influential and stimulative for audiences to spread on social media. The results show that news headline emotions can significantly affect readers′ dissemination behavior and emotional responses after dissemination. The introduction of SOR theory into social media news sharing for the first time not only provides a basis for future related research, but also provides news media and readers with an understanding of the problems faced by the digital news environment.

關鍵字(中)

★ BERT
★ 情緒分析
★ SOR model
★ 新聞媒體
★ 社交媒體新聞分享

關鍵字(英)

★ BERT
★ Sentiment Analysis
★ SOR model
★ news media
★ social media news sharing

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章緒論 1
1-1、研究背景 1
1-2、研究目的 3
1-3、論文架構 4
第二章文獻探討 6
2-1、新聞媒體特質與產業生態 6
2-2、新聞傳播與社群資訊分享 9
2-3、情感傳播 11
2-4、SOR理論 13
2-5、新聞情感分析與應用 14
第三章研究方法 18
3-1、研究架構 18
3-2、資料蒐集：媒體與資料集介紹 22
3-3、資料前處理 26
3-4、資料分析 28
3-5、模型評估與驗證 32
第四章研究結果與分析 33
4-1、數位新聞標題之情緒特質分析結果 33
4-2、數位新聞生態敘述性統計分析結果 37
4-3、數位新聞生成特質對閱聽人傳播影響力及其刺激性連鎖反應 41
第五章結論 45
5-1、結論 45
5-2、研究限制 46
5-3、未來研究與建議 46
參考文獻 47

參考文獻

Agarwal, S., & Chowdary, C. R. (2021). Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19. Expert Systems with Applications, 185, 115632. https://doi.org/https://doi.org/10.1016/j.eswa.2021.115632
Ajao, O., Bhowmik, D., & Zargari, S. (2019, 12-17 May 2019). Sentiment Aware Fake News Detection on Online Social Networks. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Alonso, M. A., Vilares, D., Gómez-Rodríguez, C., & Vilares, J. (2021). Sentiment Analysis for Fake News Detection. Electronics, 10(11), 1348. https://www.mdpi.com/2079-9292/10/11/1348
Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
Arceneaux, K., Johnson, M., & Murphy, C. (2012). Polarized political communication, oppositional media hostility, and selective exposure. The Journal of Politics, 74(1), 174-186.
Aslam, F., Awan, T. M., Syed, J. H., Kashif, A., & Parveen, M. (2020). Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications, 7(1), 23. https://doi.org/10.1057/s41599-020-0523-3
Bösch, K., Müller, O., & Schneider, J. (2018). Emotional Contagion Through Online Newspapers. ECIS,
Barsade, S. G. (2002). The Ripple Effect: Emotional Contagion and its Influence on Group Behavior. Administrative Science Quarterly, 47(4), 644-675. https://doi.org/10.2307/3094912
Behnke, R. R., Sawyer, C. R., & King, P. E. (1994). Contagion theory and the communication of public speaking state anxiety. Communication Education, 43(3), 246-251. https://doi.org/10.1080/03634529409378981
Bellovary, A. K., Young, N. A., & Goldenberg, A. (2021). Left- and Right-Leaning News Organizations Use Negative Emotional Content and Elicit User Engagement Similarly. Affective Science, 2(4), 391-396. https://doi.org/10.1007/s42761-021-00046-w
Berger, J., & Milkman, K. (2010). Social transmission, emotion, and the virality of online content. Wharton research paper, 106, 1-52.
Berger, J., & Milkman, K. L. (2012). What Makes Online Content Viral? Journal of Marketing Research, 49(2), 192-205. https://doi.org/10.1509/jmr.10.0353
Bhutani, B., Rastogi, N., Sehgal, P., & Purwar, A. (2019, 8-10 Aug. 2019). Fake News Detection Using Sentiment Analysis. 2019 Twelfth International Conference on Contemporary Computing (IC3),
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295-298. https://doi.org/10.1038/nature11421
Center, P. R. (2019). For local news, Americans embrace digital but still want strong community connection. Pew Research Center.
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118
Coviello, L., Sohn, Y., Kramer, A. D. I., Marlow, C., Franceschetti, M., Christakis, N. A., & Fowler, J. H. (2014). Detecting Emotional Contagion in Massive Social Networks. Plos One, 9(3), 6, Article e90315. https://doi.org/10.1371/journal.pone.0090315
Coyne, J. C. (1976). Depression and the response of others. Journal of abnormal psychology, 85(2), 186.
Day, M., & Lee, C. (2016, 18-21 Aug. 2016). Deep learning for financial sentiment analysis on finance news providers. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),
Deng, B. J., & Chau, M. (2021). The Effect of the Expressed Anger and Sadness on Online News Believability. Journal of Management Information Systems, 38(4), 959-988. https://doi.org/10.1080/07421222.2021.1990607
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Fan, R., Xu, K., & Zhao, J. (2016). Higher contagion and weaker ties mean anger spreads faster than joy in social media. arXiv preprint arXiv:1608.03656.
Ferrara, E., & Yang, Z. Y. (2015). Measuring Emotional Contagion in Social Media. Plos One, 10(11), 14, Article e0142390. https://doi.org/10.1371/journal.pone.0142390
Fisher, C. (2016). The trouble with ‘trust’ in news media. Communication Research and Practice, 2(4), 451-465. https://doi.org/10.1080/22041451.2016.1261251
Fletcher, R., & Park, S. (2017). The Impact of Trust in the News Media on Online News Consumption and Participation. Digital Journalism, 5(10), 1281-1299. https://doi.org/10.1080/21670811.2017.1279979
Fowler, J. H., & Christakis, N. A. (2008). Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. BMJ, 337, a2338. https://doi.org/10.1136/bmj.a2338
Franklin, B. (1997). Newszak and news media. Arnold.
Gatautis, R., Vitkauskaite, E., Gadeikiene, A., & Piligrimiene, Z. (2016). Gamification as a mean of driving online consumer behaviour: SOR model perspective. Engineering Economics, 27(1), 90-97.
Goggin, G., Martin, F., & Dwyer, T. (2015). Locative News Mobile media, place informatics, and digital news. Journalism Studies, 16(1), 41-59. https://doi.org/10.1080/1461670x.2014.890329
Goldenberg, A., & Gross, J. J. (2020). Digital Emotion Contagion. Trends in Cognitive Sciences, 24(4), 316-328. https://doi.org/10.1016/j.tics.2020.01.009
Greer, J. D., & Yan, Y. (2010). New ways of connecting with readers: How community newspapers are using Facebook, Twitter and other tools to deliver the news. Grassroots Editor, 51(4), 1-7.
Gronke, P., & Cook, T. E. (2007). Disdaining the media: The American public′s changing attitudes toward the news.
Gurtman, M. B., Martin, K. M., & Hintzman, N. M. (1990). Interpersonal reactions to displays of depression and anxiety. Journal of Social and Clinical Psychology, 9(2), 256.
Han, L., Sun, R., Gao, F. Q., Zhou, Y. C., & Jou, M. (2019). The effect of negative energy news on social trust and helping behavior. Computers in Human Behavior, 92, 128-138. https://doi.org/10.1016/j.chb.2018.11.012
Hermida, A., Fletcher, F., Korell, D., & Logan, D. (2012). SHARE, LIKE, RECOMMEND. Journalism Studies, 13(5-6), 815-824. https://doi.org/10.1080/1461670X.2012.664430
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining,
Hussein, D. M. E.-D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University - Engineering Sciences, 30(4), 330-338. https://doi.org/https://doi.org/10.1016/j.jksues.2016.04.002
Iwendi, C., Mohan, S., Ibeke, E., Ahmadian, A., & Ciano, T. (2022). Covid-19 fake news sentiment analysis. Computers and electrical engineering, 101, 107967.
Joseph, N. L. (2011). CORRECTING THE RECORD. Journalism Practice, 5(6), 704-718. https://doi.org/10.1080/17512786.2011.587670
Karimi, S., Shakery, A., & Verma, R. (2020). Online news media website ranking using user-generated content. Journal of Information Science, 47(3), 340-358. https://doi.org/10.1177/0165551519894928
Kawaf, F., & Tagg, S. (2012). Online shopping environments in fashion shopping: An SOR based review. The marketing review, 12(2), 161-180.
Khoo, C. S. G., & Johnkhan, S. B. (2018). Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons. Journal of Information Science, 44(4), 491-511. https://doi.org/10.1177/0165551517703514
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences of the United States of America, 111(24), 8788-8790. https://doi.org/10.1073/pnas.1320040111
Kumar, S., Jayant, R., & Charagulla, N. (2021, 8-10 Oct. 2021). Sentiment Analysis on the News to Improve Mental Health. 2021 IEEE MIT Undergraduate Research Technology Conference (URTC),
Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 28(2), 331-339. https://doi.org/10.1016/j.chb.2011.10.002
Li, X. D., Wu, P. J., & Wang, W. P. (2020). Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong. Information Processing & Management, 57(5), 19, Article 102212. https://doi.org/10.1016/j.ipm.2020.102212
Li, X. D., Xie, H. R., Chen, L., Wang, J. P., & Deng, X. T. (2014). News impact on stock price return via sentiment analysis. Knowledge-Based Systems, 69, 14-23. https://doi.org/10.1016/j.knosys.2014.04.022
Lin, C.-C. (2013). Convergence of new and old media: new media representation in traditional news. Chinese Journal of Communication, 6(2), 183-201.
Lin, S.-Y., Kung, Y.-C., & Leu, F.-Y. (2022). Predictive intelligence in harmful news identification by BERT-based ensemble learning model with text sentiment analysis. Information Processing & Management, 59(2), 102872. https://doi.org/https://doi.org/10.1016/j.ipm.2022.102872
Lottridge, D., & Bentley, F. R. (2018). Let′s Hate Together: How People Share News in Messaging, Social, and Public Networks Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal QC, Canada. https://doi.org/10.1145/3173574.3173634
Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017). Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Computers in Human Behavior, 70, 544-555. https://doi.org/https://doi.org/10.1016/j.chb.2017.01.020
Maier, S. R. (2010). Newspapers Offer More News than Do Major Online Sites. Newspaper Research Journal, 31(1), 6-19. https://doi.org/10.1177/073953291003100102
Masrom, M. B., Busalim, A. H., Abuhassna, H., & Mahmood, N. H. N. (2021). Understanding students’ behavior in online social networks: a systematic literature review. International Journal of Educational Technology in Higher Education, 18(1), 6. https://doi.org/10.1186/s41239-021-00240-7
McManus, J. (2012). Why Americans hate the media and how it matters [Book Review]. Journal of Mass Media Ethics, 27(4), 294-296. https://doi.org/10.1080/08900523.2012.746130
McManus, J. H. (1992). What Kind of Commodity Is News. Communication Research, 19(6), 787-805. https://doi.org/10.1177/009365092019006007
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. The MIT Press.
Mishev, K., Gjorgjevikj, A., Vodenska, I., Chitkushev, L., Souma, W., & Trajanov, D. (2019, 8-10 Dec. 2019). Forecasting Corporate Revenue by Using Deep-Learning Methodologies. 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO),
Nelson, J. L., & Kim, S. J. (2021). Improve Trust, Increase Loyalty? Analyzing the Relationship Between News Credibility and Consumption. Journalism Practice, 15(3), 348-365. https://doi.org/10.1080/17512786.2020.1719874
Neumann, R., & Strack, F. (2000). " Mood contagion": the automatic transfer of mood between persons. Journal of Personality and Social psychology, 79(2), 211.
Newman, N., Fletcher, R., Schulz, A., Andi, S., Robertson, C. T., & Nielsen, R. K. (2021). Reuters Institute digital news report 2021. Reuters Institute for the study of Journalism.
Ng, C. Y., Law, K. M. Y., & Ip, A. W. H. (2021). Assessing Public Opinions of Products Through Sentiment Analysis: Product Satisfaction Assessment by Sentiment Analysis. Journal of Organizational and End User Computing, 33(4), 125-141. https://doi.org/10.4018/JOEUC.20210701.oa6
Oeldorf-Hirsch, A., & Sundar, S. S. (2015). Posting, commenting, and tagging: Effects of sharing news stories on Facebook. Computers in Human Behavior, 44, 240-249. https://doi.org/https://doi.org/10.1016/j.chb.2014.11.024
Prior, M. (2007). Post-broadcast democracy: How media choice increases inequality in political involvement and polarizes elections. Cambridge University Press.
Prollochs, N., Feuerriegel, S., & Neumann, D. (2016). Negation scope detection in sentiment analysis: Decision support for news-driven trading. Decision Support Systems, 88, 67-75. https://doi.org/10.1016/j.dss.2016.05.009
Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves, M., & Benevenuto, F. (2016). SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(1), 23. https://doi.org/10.1140/epjds/s13688-016-0085-1
Rieis, J., de Souza, F., de Melo, P. V., Prates, R., Kwak, H., & An, J. (2015). Breaking the news: First impressions matter on online news. Proceedings of the International AAAI Conference on Web and Social Media,
Rieis, J., de Souza, F., Vaz de Melo, P., Prates, R., Kwak, H., & An, J. (2021). Breaking the News: First Impressions Matter on Online News. Proceedings of the International AAAI Conference on Web and Social Media, 9(1), 357-366. https://ojs.aaai.org/index.php/ICWSM/article/view/14619
Rosengard, D., Tucker-McLaughlin, M., & Brown, T. (2014). Students and Social News: How College Students Share News Through Social Media. Electronic News, 8(2), 120-137. https://doi.org/10.1177/1931243114546448
Schmitz Weiss, A. (2020). Journalists and Their Perceptions of Location: Making Meaning in the Community. Journalism Studies, 21(3), 352-369. https://doi.org/10.1080/1461670X.2019.1664315
Slattery, K., Doremus, M., & Marcus, L. (2001). Shifts in public affairs reporting on the network evening news: A move toward the sensational. Journal of Broadcasting & Electronic Media, 45(2), 290-302.
Soroka, S., Young, L., & Balmas, M. (2015). Bad News or Mad News? Sentiment Scoring of Negativity, Fear, and Anger in News Content. Annals of the American Academy of Political and Social Science, 659(1), 108-121. https://doi.org/10.1177/0002716215569217
Soroya, S. H., Farooq, A., Mahmood, K., Isoaho, J., & Zara, S. E. (2021). From information seeking to information avoidance: Understanding the health information behavior during a global health crisis. Information Processing & Management, 58(2), 16, Article 102440. https://doi.org/10.1016/j.ipm.2020.102440
Souma, W., Vodenska, I., & Aoyama, H. (2019). Enhanced news sentiment analysis using deep learning methods. Journal of Computational Social Science, 2(1), 33-46. https://doi.org/10.1007/s42001-019-00035-x
Strack, S., & Coyne, J. C. (1983). Social confirmation of dysphoria: shared and private reactions to depression. Journal of Personality and Social psychology, 44(4).
Tewksbury, D., & Althaus, S. L. (2000). Differences in Knowledge Acquisition among Readers of the Paper and Online Versions of a National Newspaper. Journalism & Mass Communication Quarterly, 77(3), 457-479. https://doi.org/10.1177/107769900007700301
Winer, D. L., Bonner, T. O., Blaney, P. H., & Murray, E. J. (1981). Depression and social attraction. Motivation and Emotion, 5(2), 153-166.
Young, L., & Soroka, S. (2012). Affective News: The Automated Coding of Sentiment in Political Texts. Political Communication, 29(2), 205-231. https://doi.org/10.1080/10584609.2012.671234
Zaeem, R. N., Li, C., & Barber, K. S. (2020, 7-10 Dec. 2020). On Sentiment of Online Fake News. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM),
Zhao, N., & Zhou, G. Y. (2020). Social Media Use and Mental Health during the COVID-19 Pandemic: Moderator Role of Disaster Stressor and Mediator Role of Negative Affect. Applied Psychology-Health and Well Being, 12(4), 1019-1038. https://doi.org/10.1111/aphw.12226

指導教授

曾筱珽(Hsiao-Ting Tseng)

審核日期

2022-9-26

推文