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    Title: 探討資訊過載與社群新聞涉入的關係:信任感與資訊迷失度的中介調節作用
    Unpacking the relationship between information overload and user engagement with news on social media: The role of trust and disorientation
    Authors: 殷芮涵
    Aviles, Maria Regina Incer
    Contributors: 施琮仁
    Shih, Tsung-Jen
    殷芮涵
    Maria Regina Incer Aviles
    Keywords: 連結強度
    社交媒體
    過量的資訊
    信息的參與度的潛
    social media
    user engagement
    disorientation
    information overload
    tie strength
    Date: 2022
    Issue Date: 2022-09-02 15:12:46 (UTC+8)
    Abstract: 當受眾在其社交媒體散布資訊時,他們會增加可觸及更多人的資訊,並能使其他社交媒體用戶參與其內容。這是突發公共衛生事件中的一個關鍵。然而,給予用戶過量的資訊可能會對用戶的線上參與產生不利的影響。本研究旨在調查內容混淆失向到什麼程度會成為降低用戶在社交網站信息的參與度的潛在因素。內容混淆失向可能有助於解釋資訊超載而產生影響的機制,以及如何對用戶參與產生負面影響。借鑒同質性理論,本研究還探討了連結強度的調節作用.

    研究結果顯示,儘管資訊超載似乎沒有阻止用戶參與,但資訊超載仍是內容混淆失向的一個非常重要的預測因子。研究分析發現連結強度會調節資訊超載和內容混淆失向之間的關係。連結強度,特別是緊密連結,可以幫助用戶在資訊超載的環境中減少混淆失向的感覺。本研究可能帶給媒體從業者在理解社交媒體用戶如何互動和處理資訊時的影響,以創造更多與受眾產生共鳴的豐富內容.
    When audiences disseminate information on their social media, they increase information to reach and can drive other social media users to engage with the content, which is key in the midst of a public health emergency. However, perceived information overload by users can have detrimental effects on engagement with online information. This study aims to investigate to what extent disorientation works as a potential factor to decrease user engagement with information on Social Network Sites. Disorientation might help explain the mechanism through which information overload exerts its impacts and how it might negatively affect users’ engagement. Drawing upon the homophily theory, this study also explores the moderating role of tie strength.

    Finding suggests that information overload is a highly significant predictor of disorientation, though none of them seem to stop user engagement. The analysis found that tie strength moderates the relationship between information overload and disorientation. Tie strength, specifically close ties, can help users alleviate feelings of disorientation when they are exposed to an environment overloaded with information. This study might have implications for media practitioners that aim to comprehend how social media users interact and approach information, in order to create more fruitful content that resonates with their audiences.
    Reference: Ahuja, J., & Webster, J. (2001). Perceived disorientation: An examination of a new measure to assess web design effectiveness. Interacting with Computers, 14(1), 15–29. https://doi.org/10.1016/s0953-5438(01)00048-0
    Aiello, L., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., & Menczer, F. (2012). Friendship prediction and homophily in social media. ACM Transactions on the Web, 6(2), 1–33. https://doi.org/10.1145/2180861.2180866
    Ajovalasit, S., Dorgali, V., Mazza, A., D’Onofrio, A., & Manfredi, P. (2021). Evidence of disorientation towards immunization on online social media after contrasting political communication on vaccines. Results from an analysis of Twitter data in Italy. PLOS ONE, 16(7), e0253569. https://doi.org/10.1371/journal.pone.0253569
    Anspach, N. (2017). The new personal influence: How our Facebook friends influence the news we read. Political Communication, 34(4), 590–606. https://doi.org/10.1080/10584609.2017.1316329
    Aral, S., & Walker, D. (2014). Tie strength, embeddedness, and social influence: A large-scale networked experiment. Management Science, 60(6), 1352–1370. https://doi.org/10.1287/mnsc.2014.1936b
    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, 1173–1182.
    Beaudoin, C. (2008). Explaining the relationship between internet use and interpersonal trust: Taking into account motivation and information overload. Journal of Computer-Mediated Communication, 13(3), 550–568. https://doi.org/10.1111/j.1083-6101.2008.00410.x
    Bontcheva, K., Gorell, G., & Wessels, B. (2013). Social media and information overload: Survey results. Cornell University Library. Published.
    Boutyline, A., & Willer, R. (2016). The social structure of political echo chambers: variation in ideological homophily in online networks. Political Psychology, 38(3), 551–569. https://doi.org/10.1111/pops.12337
    Cao, Q., Lu, Y., Dong, D., Tang, Z., & Li, Y. (2013). The roles of bridging and bonding in social media communities. Journal of the American Society for Information Science and Technology, 64(8), 1671–1681. https://doi.org/10.1002/asi.22866
    Chang, S., & Ley, K. (2006). A learning strategy to compensate for cognitive overload in online learning: Learner use of printed online materials. Journal of Interactive Learning, 5(1), 104–116.
    Chen, V. (2020). Examining news engagement on Facebook: Effects of news content and social networks on news engagement. Mass Communication and Society, 23(6), 833–857. https://doi.org/10.1080/15205436.2020.1798462
    Choi, J. (2016). News internalizing and externalizing. Journalism & Mass Communication Quarterly, 93(4), 816–835. https://doi.org/10.1177/1077699016628812
    Chopik, W., Bremmer, R., Johnson, D., & Giasson, L. (2018). Age differences in age perceptions and development transitions. Personality and Social Psychology https://doi.org/10.3389/fpsyg.2018.00067
    de Salve, A., Guidi, B., Ricci, L., & Mori, P. (2018). Discovering homophily in online social networks. Mobile Networks and Applications, 23(6), 1715–1726. https://doi.org/10.1007/s11036-018-1067-2
    Comrey, A., & Lee, H. (1992). A first course in factor analysis. Hillsdale, NJ: Erlbaum
    Dahiru, T., (2008). P-value: A true test pf statistical significance? A cautionary noute. PMC PubMed Central. https://doi.org/10.4314/aipm.v6i1.64038
    Dearing, J., & Rogers, E. (1996). Agenda-Setting (communication concepts). SAGE Publications, Inc.
    di Gangi, P., & Wasko, M. (2016). Social media engagement theory. Journal of Organizational and End User Computing, 28(2), 53–73. https://doi.org/10.4018/joeuc.2016040104
    Dolan, R., Conduit, J., Fahy, J., & Goodman, S. (2015). Social media engagement behaviour: A uses and gratifications perspective. Journal of Strategic Marketing, 24(3–4), 261–277. https://doi.org/10.1080/0965254x.2015.1095222
    Eryilmaz, E., Thoms, B., Ahmed, Z., & Lee, K. H. (2019). Affordances of recommender systems for disorientation in large online conversations. Journal of Computer Information Systems, 61(3), 229–239. https://doi.org/10.1080/08874417.2019.1590165
    Eveland, W., & Dunwoody, S. (2001). User control and structural isomorphism or disorientation and cognitive load? Communication Research, 28(1), 48–78. https://doi.org/10.1177/009365001028001002
    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
    Fisher RA. Nig J Paediatr. London: Oliver and Boyd; 1950. Statistical methods for research workers; p. 80.
    Flanagin, A., Hocevar, K., & Samahito, S. (2013). Connecting with the user-generated web: How group identification impacts online information sharing and evaluation. Information, Communication & Society, 17(6), 683–694. https://doi.org/10.1080/1369118x.2013.808361
    Friedkin, N. (1982). Information flow through strong and weak ties in intraorganizational social networks. Social Networks, 3(4), 273–285. https://doi.org/10.1016/0378-8733(82)90003x
    Fu, S., Li, H., Liu, Y., Pirkkalainen, H., & Salo, M. (2020). Social media overload, exhaustion, and use discontinuance: Examining the effects of information overload, system feature overload, and social overload. Information Processing & Management, 57(6), 102307. https://doi.org/10.1016/j.ipm.2020.102307
    Gao, J., Zhang, C., Wang, K., & Ba, S. (2012). Understanding online purchase decision making: The effects of unconscious thought, information quality, and information quantity. Decision Support Systems, 53(4), 772–781. https://doi.org/10.1016/j.dss.2012.05.011
    Gil de Zúñiga, H., & Valenzuela, S. (2011). The mediating path to a stronger citizenship: Online and offline networks, weak ties, and civic engagement. Communication Research, 38(3), 397-421.
    Gil De Zúñiga, H., Weeks, B., & Ardèvol-Abreu, A. (2017). Effects of the news-finds-me perception in communication: Social media use implications for news seeking and learning about politics. Journal of Computer-Mediated Communication, 22(3), 105–123. https://doi.org/10.1111/jcc4.12185
    Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. CHI ’09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 211–220. https://doi.org/10.1145/1518701.1518736
    Global Web Index (2020). Coronavirus Research Multi-market research https://www.gwi.com/hubfs/1.%20Coronavirus%20Research%20PDFs/GWI%20coronavirus%20findings%20March%202020%20-%20Multi-Market%20data%20(Release%203).pdf
    Golbeck, J. (2009). Trust and nuanced profile similarity in online social networks. ACM Transactions on the Web, 3(4), 1–33. https://doi.org/10.1145/1594173.1594174
    Haselhuhn, P., Kennedy, J., & Kray, L. (2015). Gender difference in trust dynamics: Women trust more than men following a trust violation. Journal of Experimental Psychology, 56(104–109).
    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
    Hocevar, K. P., Flanagin, A. J., & Metzger, M. J. (2014). Social media self-efficacy and information evaluation online. Computers in Human Behavior, 39, 254–262. https://doi.org/10.1016/j.chb.2014.07.020
    Holton, A., & Chyi, H. (2012). News and the overloaded consumer: Factors influencing information overload among news consumers. Cyberpsychology, Behavior, and Social Networking, 15(11), 619–624. https://doi.org/10.1089/cyber.2011.0610
    Hussain, W. (2020). Role of social media in Covid-19 pandemic. The International Journal of Frontier Sciences, 4(2), 59–60. https://doi.org/10.37978/tijfs.v4i2.144
    Ji, Q., Ha, L., & Sypher, U. (2014). The role of news nedia use and demographic characteristics in the prediction of information overload. International Journal of Communication. Published.
    Jones, S. L., & Kelly, R. (2017). Dealing with information overload in multifaceted personal informatics systems. Human–Computer Interaction, 33(1), 1–48. https://doi.org/10.1080/07370024.2017.1302334
    Kaakinen, M., Sirola, A., Savolainen, I., & Oksanen, A. (2018). Shared identity and shared information in social media: Development and validation of the identity bubble reinforcement scale. Media Psychology, 23(1), 25–51. https://doi.org/10.1080/15213269.2018.1544910
    Kaiser, J., Keller, T. R., & Kleinen-von Königslöw, K. (2018). Incidental news exposure on Facebook as a social experience: The influence of recommender and media cues on news selection. Communication Research, 48(1), 77–99. https://doi.org/10.1177/0093650218803529
    Kemp, S. (2020). Digital 2020: July Global Statshot. DataReportal https://datareportal.com/reports/digital-2020-july-global-statshot
    Khan, M. L. (2017). Social media engagement: What motivates user participation and consumption on YouTube? Computers in Human Behavior, 66, 236–247. https://doi.org/10.1016/j.chb.2016.09.024
    Kirsh, D. (2000). A few thoughts on cognitive overload. Intellectica. Revue de l’Association Pour La Recherche Cognitive, 30(1), 19–51. https://doi.org/10.3406/intel.2000.1592
    Koroleva, K., Krasnova, H., & Günther, O. (2010). `STOP SPAMMING ME!` - Exploring information overload on Facebook In M. Santana & J. Luftman (Eds.). Proceedings 2010 Americas Conference on Information Systems. Association for Information Systems. https://aisel.aisnet.org/amcis2010/?utm_source=aisel.aisnet.org%2Famcis2010%2F447&utm_medium=PDF&utm_campaign=PDFCoverPages
    Koroleva, K., Stimac, V., Krasnova, H., & Kunze, D. (2011). I like IT because I(`m) like you - measuring user attitudes towards information on Facebook. In D. Galletta & T. Liang (Eds.). Proceedings of the International Conference on Information Systems. Association for Information Systems. https://www.semanticscholar.org/paper/I-like-IT-Because-I-(`M)-like-You-Measuring-User-on-Koroleva-Stimac/77d001397ba0befb9c73d79b84b2ab6b3fd32168
    Koroleva, K., & Stimac, V. (2012). Tie strength vs. network overlap: Why Information from lovers is more valuable than from close friends on social network sites? Proceeding Thirty Third International Conference on Information Systems. AIS Library. https://aisel.aisnet.org/icis2012/proceedings/DigitalNetworks/15/
    Koroleva, K., & Bolufé, A. (2012). Reducing information overload: Design and evaluation of filtering & ranking algorithms for social networking sites. Proceedings 2012 European Conference on Information Systems. AIS Library. https://aisel.aisnet.org/ecis2012/12/
    Kümpel, A. S. (2020). The Matthew effect in social media news use: Assessing inequalities in news exposure and news engagement on social network sites (SNS). Journalism, 21(8), 1083–1098. https://doi.org/10.1177/1464884920915374
    Lee, S., Lindsey, N., & Kim, K. (2017). The effects of news consumption via social media and news information overload on perceptions of journalistic norms and practices. Computers in Human Behavior, 75, 254–263. https://doi.org/10.1016/j.chb.2017.05.007
    Lehmann, J., Lalmas, M., Yom-Tov, E., & Dupret, G. (2012). Models of user engagement. User Modeling, Adaptation, and Personalization, 164–175. https://doi.org/10.1007/978-3-642-31454-4_14
    Leonard, R., & Onyx, J. (2003). Networking through loose and strong ties: An Australian qualitative study. International Journal of Voluntary and Nonprofit Organizations, 14(2), 189–203. https://doi.org/10.1023/a:1023900111271
    Levin, D., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management Science, 50(11), 1477–1490. https://doi.org/10.1287/mnsc.1030.0136
    Liang, H., & Fu, K. W. (2016). Information overload, similarity, and redundancy: Unsubscribing information sources on Twitter. Journal of Computer-Mediated Communication, 22(1), 1–17. https://doi.org/10.1111/jcc4.12178
    Liu, H., Lim, E. P., Lauw, H. W., Le, M. T., Sun, A., Srivastava, J., & Kim, Y. A. (2008). Predicting trusts among users of online communities. Proceedings of the 9th ACM Conference on Electronic Commerce - EC ’08. Computer Science. https://www.semanticscholar.org/paper/Predicting-trusts-among-users-of-online-an-epinions-Liu-Lim/a651f82b86a8833551ac5d389a15127c050c8a80
    Liu, J., Rau, P., & Wendler, N. (2014). Trust and online information-sharing in close relationships: a cross-cultural perspective. Behaviour & Information Technology, 34(4), 363–374. https://doi.org/10.1080/0144929x.2014.937458
    Ma, L., Lee, C., & Hoe-Lian, D. (2014). Understanding news sharing in social media. Online Information Review, 38(5), 598–615. https://doi.org/10.1108/oir-10-2013-0239
    McCay, L., & Quan, A. (2016). A model of social media engagement: User profiles, gratifications, and experiences. Why Engagement Matters, 199–217. https://doi.org/10.1007/978-3-319-27446-1_9
    McPherson, M., Smith-Lovin, L., & Cook, J. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444. https://doi.org/10.1146/annurev.soc.27.1.415
    Metzger, M., & Flanagin, A. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59, 210–220. https://doi.org/10.1016/j.pragma.2013.07.012
    Metzger, M., Flanagin, A., & Medders, R. (2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60(3), 413–439. https://doi.org/10.1111/j.1460-2466.2010.01488.x
    Mitchell, T., Chen, S., & Macredie, R. (2005). Cognitive styles and adaptive web-based learning. Psychology of Education Review, 34–42.
    Mitchell, V., & Papavassiliou, V. (1999). Marketing causes and implications of consumer confusion. Journal of Product & Brand Management, 8(4), 319–342. https://doi.org/10.1108/10610429910284300
    Mohammed, M., Sha’aban, A., Jatau, A. I., Yunusa, I., Isa, A. M., Wada, A. S., Obamiro, K., Zainal, H., & Ibrahim, B. (2021). Assessment of Covid-19 information overload among the general public. Journal of Racial and Ethnic Health Disparities. Published. https://doi.org/10.1007/s40615-020-00942-0
    Morris, M., Teevan, J., & Panovich, K. (2010). What do people ask their social networks, and why?: A survey study of status message Q&A behavior. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
    O’Brien, H. L. (2011). Exploring user engagement in online news interactions. Proceedings of the American Society for Information Science and Technology, 48(1), 1–10. https://doi.org/10.1002/meet.2011.14504801088
    Oeldorf-Hirsch, A. (2017). The role of engagement in learning from active and incidental news exposure on social media. Mass Communication and Society, 21(2), 225–247. https://doi.org/10.1080/15205436.2017.1384022
    Özkan, E., & Tolon, M. (2015). The effects of information overload on consumer confusion: An examination on user generated content. Bogazici Journal, 29(1), 27–51. https://doi.org/10.21773/boun.29.1.2
    Pentina, I., & Tarafdar, M. (2014). From “information” to “knowing”: Exploring the role of social media in contemporary news consumption. Computers in Human Behavior, 35, 211–223. https://doi.org/10.1016/j.chb.2014.02.045
    Olmstead, K., Mitchell, A., & Rosentiel, T. (2011). Where people go, how they get there and what lures them away. Pew Research Center https://www.pewresearch.org/journalism/2011/05/09/navigating-news-online/
    Smith, A. (2015, April). U.S. Smartphone Use in 2015. Pew Research Center https://www.pewresearch.org/internet/2015/04/01/us-smartphone-use-in-2015/
    Qiu, M., & McDougall, D. (2015). Influence of group configuration on online discourse reading. Computers & Education, 87, 151–165. https://doi.org/10.1016/j.compedu.2015.04.006
    Ruttun, R., & Macredie, R. (2012). The effects of individual differences and visual instructional aids on disorientation, learning performance and attitudes in a hypermedia learning system. Computers in Human Behavior, 28(6), 2182–2198. https://doi.org/10.1016/j.chb.2012.06.026
    Schick, A., Gordon, L., & Haka, S. (1990). Information overload: A temporal approach. Accounting, Organizations and Society, 15(3), 199–220. https://doi.org/10.1016/0361-3682(90)90005-f
    Schivinski, B., Christodoulides, G., & Dabrowski, D. (2016). Measuring consumers’ engagement with brand-related social-media content. Journal of Advertising Research, 56(1), 64–80. https://doi.org/10.2501/jar-2016-004
    Schmitt, J. B., Debbelt, C. A., & Schneider, F. M. (2017). Too much information? predictors of information overload in the context of online news exposure. Information, Communication & Society, 21(8), 1151–1167. https://doi.org/10.1080/1369118x.2017.1305427
    Schweizer, M., Kotouc, A., & Wagne, T. (2006). Scale Development for consumer confusion. Advances in Consumer Research, 33, 184–188.
    Shih, Y., Huang, P., Hsu, Y., & Chen, S. (2012). A complete understanding of disorientation problems in web-based learning. The Turkish Online Journal of Educational Technology, 11(3).
    Sin, S., & Vakkari, P. (2015). Perceived outcomes of public libraries in the US. Library and Information Science Research, 37(3), 209–219. https://doi.org/10.1108/jd-02-2013-0016
    Sohn, D., & Choi, S. (2019). Social embeddedness of persuasion: Effects of cognitive social structures on information credibility assessment and sharing in social media.International Journal of Advertising, 38(6), 824–844. https://doi.org/10.1080/02650487.2018.1536507
    Sterrett, D., Malato, D., Benz, J., Kantor, L., Tompson, T., Rosenstiel, T., Sonderman, J., & Loker, K. (2019). Who shared it?: Deciding what news to trust on social media. Digital Journalism, 7(6), 783–801. https://doi.org/10.1080/21670811.2019.1623702
    Strekalova, Y. A. (2016). Health risk information engagement and amplification on social media. Health Education & Behavior, 44(2), 332–339. https://doi.org/10.1177/1090198116660310
    Tang, J., Gao, H., Hu, X., & Liu, H. (2013). Exploiting homophily effect for trust prediction. Proceedings of the Sixth ACM International Conference on Web Search and Data Mining - WSDM ’13, 53–62. https://doi.org/10.1145/2433396.2433405
    Thelwall, M. (2009). Homophily in MySpace. Journal of the American Society for Information Science and Technology, 60(2), 219–231. https://doi.org/10.1002/asi.20978
    Toon, J. (2010, September 16). News media coverage reduces pandemic impact, model shows. Georgia Tech Research. https://rh.gatech.edu/news/61014/news-media-coverage-reduces-pandemic-impact-model-shows
    Walsh. G., & Mitchell, V. (2008). The effect of consumer confusion proness on word of mouth, trust, and customer satisfaction. European Journal of Marketing, 44(6), 838-859.
    Walter, F., Battiston, S., & Schweitzer, F. (2008). Coping with information overload through trust-based networks. In Managing Complexity: Insights, Concepts, Applications (p. 273–300). Springer. https://doi.org/10.1007/978-3-540-75261-5_13
    Wang, Y., Wang, X., & Zuo, W. L. (2015). Research on trust prediction from a sociological perspective. Journal of Computer Science and Technology, 30(4), 843–858. https://doi.org/10.1007/s11390-015-1564-8
    Webster, J., & Ahuja, J. (2006). Enhancing the design of web navigation systems: The influence of user disorientation on engagement and performance. MIS Quarterly, 30(3), 661. https://doi.org/10.2307/25148744
    York, C. (2013). Overloaded by the news: Effects of news exposure and enjoyment on reporting information overload. Communication Research Reports, 30(4), 282–292. https://doi.org/10.1080/08824096.2013.836628
    Zhang, X., Ding, X., Wang, G., & Ma, L. (2018). Investigating the influences of social overload and task complexity on user engagement decrease. Total Quality Management & Business Excellence, 31(15–16), 1774–1787. https://doi.org/10.1080/14783363.2018.1509698
    Zhao, N., & Zhou, G. (2021). COVID-19 Stress and Addictive Social Media Use (SMU): Mediating Role of Active Use and Social Media Flow. Front. Psychiatry. https://doi.org/10.3389/fpsyt.2021.63554
    Description: 碩士
    國立政治大學
    國際傳播英語碩士學位學程(IMICS)
    108461016
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108461016
    Data Type: thesis
    DOI: 10.6814/NCCU202201445
    Appears in Collections:[國際傳播英語碩士學程] 學位論文

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