What You Perceive Is What You Get: Enhancing Rumor-Combating Effectiveness on Social Media Based on Elaboration Likelihood Model

被引:0
|
作者
Zhou, Cheng [1 ]
Chang, Qian [1 ]
机构
[1] Sichuan Agr Univ, Dujiangyan, Peoples R China
来源
SOCIAL MEDIA + SOCIETY | 2024年 / 10卷 / 04期
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
rumor-combating effectiveness; perceptible factors; imperceptible factors; temporal distance; elaboration likelihood model; social media; TEMPORAL DISTANCE; IMPACT; USERS; MICROBLOGS; RICHNESS; SELF;
D O I
10.1177/20563051241288809
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
Rumors spread on social media overshadow the truth and trigger public panic. One effective countermeasure to address this issue is online rumor-combating. However, its effectiveness on social media has not been fully verified. In this study, drawing on construal level theory, we use temporal distance-the time interval between a rumor-combating post being released and receiving responses from social media users-to measure the effectiveness of rumor-combating. We also adopt elaboration likelihood model to explore the factors that could enhance this effectiveness. The empirical results show that perceptible (central route) factors, including the author's authoritative combating methods, media richness, and positive emotions, are negatively related to temporal distance and are more effective for enhancing rumor-combating effectiveness than imperceptible (peripheral route) factors, such as the author's influence and activeness. In addition, media richness exerts positive moderating effects on the relationship between perceptible route factors and rumor-combating effectiveness, implying that with the help of images or videos, rumor-combating effectiveness improves. This study addresses the need to enhance the effectiveness of rumor-combating and has practical implications for combating rumors in the social media.
引用
收藏
页数:17
相关论文
共 32 条
  • [1] You Get What You Ask For? Encountering Complexity and Performative Leadership on Social Media
    Jalonen, Harri
    ICCMB 2019 - THE 2ND INTERNATIONAL CONFERENCE ON COMPUTERS IN MANAGEMENT AND BUSINESS, 2019, : 64 - 69
  • [2] You are what you eatA social media study of food identity
    Kazutoshi Sasahara
    Journal of Computational Social Science, 2019, 2 : 103 - 117
  • [3] You are what you eat: A social media study of food identity
    Sasahara, Kazutoshi
    JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE, 2019, 2 (02): : 103 - 117
  • [4] “What are you doing, TikTok?”: How Marginalized Social Media Users Perceive, Theorize, and “Prove” Shadowbanning
    Delmonaco D.
    Mayworm S.
    Thach H.
    Guberman J.
    Augusta A.
    Haimson O.L.
    Proceedings of the ACM on Human-Computer Interaction, 2024, 8 (CSCW1)
  • [5] YOU ARE WHAT YOU TWEET...PIC! GENDER PREDICTION BASED ON SEMANTIC ANALYSIS OF SOCIAL MEDIA IMAGES
    Merler, Michele
    Cao, Liangliang
    Smith, John R.
    2015 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO (ICME), 2015,
  • [6] Social Media Analytics: What You Need to Know as a Urologist
    Teoh, Jeremy Yuen-Chun
    Mackenzie, Graham
    Tortolero, Leonardo
    Gomez Rivas, Juan
    EUROPEAN UROLOGY FOCUS, 2020, 6 (03): : 434 - 436
  • [7] Physician Social Media Abuse What Would You Do?
    Desai, Dolly G.
    Mitchell, Jordan P.
    HEALTH CARE MANAGER, 2020, 39 (01) : 12 - 17
  • [8] Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing
    Oh, Hyelim
    Goh, Khim-Yong
    Phan, Tuan Q.
    INFORMATION SYSTEMS RESEARCH, 2023, 34 (01) : 111 - 136
  • [9] "Did You See What Happened?" How Scandals are Shared via Social Media
    Soltani, Mona
    Veer, Ekant
    de Vries, Huibert Peter
    A. Kemper, Joya
    CORPORATE REPUTATION REVIEW, 2024, 27 (03) : 186 - 201
  • [10] I Like What You Like: Social Norms and Media Enjoyment
    Kryston, Kevin
    Eden, Allison
    MASS COMMUNICATION AND SOCIETY, 2022, 25 (05) : 603 - 625