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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳玲玲 | zh_TW |
| dc.contributor.advisor | Ling-Ling Wu | en |
| dc.contributor.author | 蘇奕丞 | zh_TW |
| dc.contributor.author | Yi-Cheng Su | en |
| dc.date.accessioned | 2025-02-26T16:15:03Z | - |
| dc.date.available | 2025-02-27 | - |
| dc.date.copyright | 2025-02-26 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-02-07 | - |
| dc.identifier.citation | Allcott, H., & Gentzkow, M. (2017). Social Media and Fake News in the 2016 Election. Journal of Economic Perspectives, 31(2), 211-235. https://doi.org/10.1257/jep.31.2.211
Andrews, J. C., & Shimp, T. A. (1990). Effects of involvement, argument strength, and source characteristics on central and peripheral processing of advertising. Psychology & Marketing, 7(3), 195-214. https://doi.org/10.1002/mar.4220070305 Austin, E. W., & Dong, Q. (1994). Source v. content effects on judgments of news believability. Journalism quarterly, 71(4), 973-983. https://doi.org/10.1177/107769909407100420 Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173 Bitner, M. J., & Obermiller, C. (1985). The elaboration likelihood model: Limitations and extensions in marketing. Advances in consumer research, 12(1), 420-425. Blumenstock, J. E. (2008). Size matters: word count as a measure of quality on wikipedia. Proceedings of the 17th international conference on World Wide Web, 1095-1096. https://doi.org/10.1145/1367497.1367673 Borah, P., & Xiao, X. (2018). The importance of ‘likes’: The interplay of message framing, source, and social endorsement on credibility perceptions of health information on Facebook. Journal of health communication, 23(4), 399-411. https://doi.org/10.1080/10810730.2018.1455770 Chauhan, K., & Pillai, A. (2013). Role of content strategy in social media brand communities: a case of higher education institutes in India. Journal of Product & Brand Management, 22(1), 40-51. https://doi.org/10.1108/10610421311298687 Chen, Y. P., Chen, Y. Y., Yang, K. C., Lai, F. P., Huang, C. H., Chen, Y. N., & Tu, Y. C. (2022). The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media. Journal of Medical Internet Research, 24(4), Article e36830. https://doi.org/10.2196/36830 Cheung, C. M.-Y., Sia, C.-L., & Kuan, K. K. (2012). Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 13(8), 2. https://doi.org/10.17705/1jais.00305 Chu, S.-C., & Kamal, S. (2008). The effect of perceived blogger credibility and argument quality on message elaboration and brand attitudes: An exploratory study. Journal of interactive Advertising, 8(2), 26-37. https://doi.org/10.1080/15252019.2008.10722140 El Hedhli, K., & Zourrig, H. (2023). Dual routes or a one-way to persuasion? The elaboration likelihood model versus the unimodel. Journal of Marketing Communications, 29(5), 433-454. https://doi.org/10.1080/13527266.2022.2034033 Falagas, M. E., Zarkali, A., Karageorgopoulos, D. E., Bardakas, V., & Mavros, M. N. (2013). The impact of article length on the number of future citations: a bibliometric analysis of general medicine journals. PLoS One, 8(2), e49476. https://doi.org/10.1371/journal.pone.0049476 Fischer, E., & Reuber, A. R. (2011). Social interaction via new social media:(How) can interactions on Twitter affect effectual thinking and behavior? Journal of business venturing, 26(1), 1-18. https://doi.org/10.1016/j.jbusvent.2010.09.002 Flaxman, S., Goel, S., & Rao, J. M. (2013). Ideological segregation and the effects of social media on news consumption. SSRN Electronic Journal, 2363701. https://doi.org/10.2139/ssrn.2363701 Gimpel, H., Heger, S., Olenberger, C., & Utz, L. (2021). The Effectiveness of Social Norms in Fighting Fake News on Social Media. Journal of Management Information Systems, 38(1), 196-221. https://doi.org/10.1080/07421222.2021.1870389 Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 US presidential election. science, 363(6425), 374-378. https://doi.org/10.1126/science.aau2706 Harmon, R. R., & Coney, K. A. (1982). The persuasive effects of source credibility in buy and lease situations. Journal of Marketing research, 19(2), 255-260. https://doi.org/10.1177/002224378201900209 J. Kitchen, P., Kerr, G., E. Schultz, D., McColl, R., & Pals, H. (2014). The elaboration likelihood model: review, critique and research agenda. European Journal of marketing, 48(11/12), 2033-2050. https://doi.org/10.1108/EJM-12-2011-0776 Johnson, T. J., & Kaye, B. K. (2015). Reasons to believe: Influence of credibility on motivations for using social networks. Computers in human behavior, 50, 544-555. https://doi.org/10.1016/j.chb.2015.04.002 Jones, L. W., Sinclair, R. C., & Courneya, K. S. (2003). The effects of source credibility and message framing on exercise intentions, behaviors, and attitudes: An integration of the elaboration likelihood model and prospect theory 1. Journal of applied social psychology, 33(1), 179-196. https://doi.org/10.1111/j.1559-1816.2003.tb02078.x Kaliyar, R. K., Goswami, A., & Narang, P. (2021). FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia tools and applications, 80(8), 11765-11788. https://doi.org/10.1007/s11042-020-10183-2 Kim, A., & Dennis, A. R. (2019). Says who? The effects of presentation format and source rating on fake news in social media. MIS quarterly, 43(3), 1025-1039. https://doi.org/10.2139/ssrn.2987866 Kim, A., Moravec, P. L., & Dennis, A. R. (2019). Combating Fake News on Social Media with Source Ratings: The Effects of User and Expert Reputation Ratings. Journal of Management Information Systems, 36(3), 931-968. https://doi.org/10.1080/07421222.2019.1628921 Kim, C., & Yang, S.-U. (2017). Like, comment, and share on Facebook: How each behavior differs from the other. Public relations review, 43(2), 441-449. https://doi.org/10.1016/j.pubrev.2017.02.006 King, K. K., Wang, B., Escobari, D., & Oraby, T. (2021). Dynamic Effects of Falsehoods and Corrections on Social Media: A Theoretical Modeling and Empirical Evidence. Journal of Management Information Systems, 38(4), 989-1010. https://doi.org/10.1080/07421222.2021.1990611 Kruglanski, A. W., & Thompson, E. P. (1999). Persuasion by a single route: A view from the unimodel. Psychological Inquiry, 10(2), 83-109. https://doi.org/10.1207/S15327965PL100201 Kunda, Z. (1990). The case for motivated reasoning. Psychological bulletin, 108(3), 480–498. https://doi.org/10.1037/0033-2909.108.3.480 Li, C.-Y. (2013). Persuasive messages on information system acceptance: A theoretical extension of elaboration likelihood model and social influence theory. Computers in human behavior, 29(1), 264-275. https://doi.org/10.1016/j.chb.2012.09.003 Monti, F., Frasca, F., Eynard, D., Mannion, D., & Bronstein, M. M. (2019). Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673. Moravec, P., Minas, R., & Dennis, A. R. (2018). Fake news on social media: People believe what they want to believe when it makes no sense at all. Kelley School of Business research paper(18-87). https://doi.org/10.2139/ssrn.3269541 Moravec, P. L., Kim, A., & Dennis, A. R. (2020). Appealing to Sense and Sensibility: System 1 and System 2 Interventions for Fake News on Social Media. Information Systems Research, 31(3), 987-1006. https://doi.org/10.1287/isre.2020.0927 Moravec, P. L., Kim, A., Dennis, A. R., & Minas, R. K. (2022). Do you really know if it’s true? How asking users to rate stories affects belief in fake news on social media. Information Systems Research, 33(3). https://doi.org/10.1287/isre.2021.1090 Mun, Y. Y., Yoon, J. J., Davis, J. M., & Lee, T. (2013). Untangling the antecedents of initial trust in Web-based health information: The roles of argument quality, source expertise, and user perceptions of information quality and risk. Decision support systems, 55(1), 284-295. https://doi.org/10.1016/j.dss.2013.01.029 Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid CNN-RNN based deep learning approach. International Journal of Information Management Data Insights, 1(1), 100007. https://doi.org/10.1016/j.jjimei.2020.100007 Nemeth, C. J. (1986). Differential contributions of majority and minority influence. Psychological review, 93(1), 23. https://doi.org/10.1037/0033-295X.93.1.23 Newman, N., Fletcher, R., Schulz, A., Andi, S., Robertson, C. T., & Nielsen, R. K. (2021). Reuters Institute digital news report 2021. https://ssrn.com/abstract=3873260 Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of experimental social psychology, 45(4), 867-872. https://doi.org/10.1016/j.jesp.2009.03.009 Opview. (2022). 公眾議題網路洞察報告. https://www.opview.com.tw/wp-content/file/20220714_2022publicissue.pdf Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. penguin UK. Pennycook, G., Bear, A., Collins, E. T., & Rand, D. G. (2020). The Implied Truth Effect: Attaching Warnings to a Subset of Fake News Headlines Increases Perceived Accuracy of Headlines Without Warnings. Management Science, 66(11), 4944-4957. https://doi.org/10.1287/mnsc.2019.3478 Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and persuasion (pp. 1-24). Springer. https://doi.org/10.1007/978-1-4612-4964-1_1 Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a determinant of argument-based persuasion. Journal of personality and social psychology, 41(5), 847. https://doi.org/10.1037/0022-3514.41.5.847 Petty, R. E., Cacioppo, J. T., & Heesacker, M. (1981). Effects of rhetorical questions on persuasion: A cognitive response analysis. Journal of personality and social psychology, 40(3), 432. https://doi.org/10.1037/0022-3514.40.3.432 Petty, R. E., Kasmer, J. A., Haugtvedt, C. P., & Cacioppo, J. T. (1987). Source and message factors in persuasion: A reply to Stiff's critique of the elaboration likelihood model. Communication Monographs(54(3)), 233–249. https://doi.org/10.1080/03637758709390229 Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1), 22-36. https://doi.org/10.1145/3137597.3137600 Stiff, J. B. (1986). Cognitive processing of persuasive message cues: A meta‐analytic review of the effects of supporting information on attitudes. Communications Monographs, 53(1), 75-89. https://doi.org/10.1080/03637758609376128 Taiwan Network Information Center. (2022). 2022台灣網路報告. https://www.twnic.tw/doc/twrp/202207e.pdf Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. science, 359(6380), 1146-1151. https://doi.org/10.1126/science.aap9559 Yaqub, W., Kakhidze, O., Brockman, M. L., Memon, N., & Patil, S. (2020). Effects of credibility indicators on social media news sharing intent. Proceedings of the 2020 chi conference on human factors in computing systems, 1-14. https://doi.org/10.1145/3313831.3376213 Zhao, S., Grasmuck, S., & Martin, J. (2008). Identity construction on Facebook: Digital empowerment in anchored relationships. Computers in human behavior, 24(5), 1816-1836. https://doi.org/10.1016/j.chb.2008.02.012. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97056 | - |
| dc.description.abstract | 社群媒體上的假新聞傳播已經成為一個嚴重的問題。雖然社群媒體讓任何人都可以輕易地創作並分享新聞，但這樣的過程缺乏了傳統新聞媒體的品質管控機制，因而導致新聞品質下降，使人們更容易相信並傳播假新聞。
為了理解人們為何會相信假新聞，進一步預防假新聞的傳播，我們利用推敲可行性模型（Elaboration likelihood model）來解釋影響社群媒體上假新聞可信度的因素。我們假設中央線索（central cue）和周邊線索（peripheral cue）都會對新聞的感知可信度（news perceived believability）產生正向影響，進而影響用戶在社群媒體上的參與行為。另外我們也探討了社群媒體使用者的動機，如何調節這些線索與新聞可信度。 我們的研究結果顯示，中央線索和周邊線索都顯著影響新聞的感知可信度，且用戶的動機也會對新聞的可信度有直接影響。然而，與我們的假設不同，動機並未調節中央線索和周邊線索對新聞感知可信度的影響。這表明用戶在評估新聞感知可信度時，會依據內容、動機對新聞可信度做出判斷。最後，研究發現感知可信度在中央線索與社群媒體行為之間起到了中介作用，並在周邊線索與部分社群媒體行為(點讚、正面評論)之間起到了中介作用。 | zh_TW |
| dc.description.abstract | The spread of fake news on social media has become a serious problem. Social media allows anyone to create and share news easily without traditional quality control by journalism, leading to a decrease in news quality and making fake news more believable and widely shared.
To address this issue, we apply the well-known Elaboration Likelihood Model (ELM) to explain the factors influencing the perceived believability of fake news on social media. We hypothesize that both central and peripheral cues positively impact news believability, which in turn affects user behavior on social media. Additionally, we investigate how user motivation moderates the relationship between these cues and the believability of news. Our findings indicate that both central and peripheral cues significantly influence news perceived believability, and user motivation has an impact on believability. However, motivation does not moderate the effects of central and peripheral cues on believability as hypothesized. This suggests that users rely more on their motivation rather than just the content when evaluating news believability. The study also revealed that perceived believability serves as a mediator in the relationship between central cues and social media behaviors, including liking, positive commenting, sharing, and negative commenting. Additionally, perceived believability mediates the relationship between peripheral cues and social media behaviors such as liking and posting positive comments. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-26T16:15:03Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-26T16:15:03Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 中文摘要 III Abstract IV Table of Contents VI List of Figures VIII List of Tables IX Chapter 1 Introduction 1 Chapter 2 Literature Review 3 2.1 Previous research on fake news 3 2.2 A news post on social media 5 2.3 Elaboration Likelihood Model (ELM) 6 2.4 Central cue 9 2.5 Peripheral cue 10 2.6 Motivation 12 2.7 Mediating effect of perceived believability 14 Chapter 3 Methodology 16 3.1 Participants 16 3.2 Procedures 17 3.3 Treatment 18 3.4 Independent variable 19 3.5 Moderating variable 22 3.6 Dependent variable 23 Chapter 4 Result 24 4.1 Data cleaning 24 4.2 Manipulation check 24 4.3 Measurement reliability and validity 26 4.4 Test of hypothesis 29 Chapter 5 Discussion and Conclusion 41 5.1 Discussion 41 5.2 Managerial implication 43 5.3 Limitation and future work 44 Reference 46 Appendix A Pilot test of experiment material 51 Appendix B Experiment material 53 Appendix C Sample cleaning 58 | - |
| dc.language.iso | en | - |
| dc.subject | 新聞感知可信度 | zh_TW |
| dc.subject | 社群媒體行為 | zh_TW |
| dc.subject | 推敲可行性模型 | zh_TW |
| dc.subject | 社群媒體 | zh_TW |
| dc.subject | 假新聞 | zh_TW |
| dc.subject | social media behavior | en |
| dc.subject | Fake news | en |
| dc.subject | social media | en |
| dc.subject | Elaboration Likelihood Model (ELM) | en |
| dc.subject | news perceived believability | en |
| dc.title | 社群媒體上假新聞的感知可信度 | zh_TW |
| dc.title | Perceived believability of fake news on social media | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 畢南怡;簡怡雯 | zh_TW |
| dc.contributor.oralexamcommittee | Nan-Yi Bi;Yi-Wen Chien | en |
| dc.subject.keyword | 假新聞,社群媒體,推敲可行性模型,新聞感知可信度,社群媒體行為, | zh_TW |
| dc.subject.keyword | Fake news,social media,Elaboration Likelihood Model (ELM),news perceived believability,social media behavior, | en |
| dc.relation.page | 60 | - |
| dc.identifier.doi | 10.6342/NTU202500509 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-02-08 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 資訊管理學系 | - |
| dc.date.embargo-lift | 2025-02-27 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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