Impact of Social Reference Cues on Misinformation Sharing on Social Media: Series of Experimental Studies

被引:6
|
作者
Jones, Christopher M. [1 ,2 ,4 ]
Diethei, Daniel [1 ,2 ]
Schoening, Johannes [2 ,3 ]
Shrestha, Rehana [1 ,2 ]
Jahnel, Tina [1 ,2 ]
Schuez, Benjamin [1 ,2 ]
机构
[1] Univ Bremen, Inst Publ Hlth & Nursing Res, Bremen, Germany
[2] Leibniz ScienceCampus Digital Publ Hlth, Bremen, Germany
[3] Univ St Gallen, Sch Comp Sci, St Gallen, Switzerland
[4] Univ Bremen, Inst Publ Hlth & Nursing Res, Grazer Str 4, D-28359 Bremen, Germany
关键词
misinformation; social media; health literacy; COVID-19; fake news; Twitter; tweet; infodemiology; information behavior; information sharing; sharing behavior; behavior change; social cue; social reference; flag; LINEAR GROWTH; FAKE NEWS; POWER;
D O I
10.2196/45583
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Health-related misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information, but they have not yet targeted social processes. Current approaches focus on increasing scrutiny, providing corrections to misinformation (debunking), or alerting users prospectively about future misinformation (prebunking and inoculation). Here, we provide a test of a complementary strategy that focuses on the social processes inherent in social media use, in particular, social reinforcement, social identity, and injunctive norms. Objective: This study aimed to examine whether providing balanced social reference cues (ie, cues that provide information on users sharing and, more importantly, not sharing specific content) in addition to flagging COVID-19-related misinformation leads to reductions in sharing behavior and improvement in overall sharing quality. Methods: A total of 3 field experiments were conducted on Twitter's native social media feed (via a newly developed browser extension). Participants'feed was augmented to include misleading and control information, resulting in 4 groups: no-information control, Twitter's own misinformation warning (misinformation flag), social cue only, and combined misinformation flag and social cue. We tracked the content shared or liked by participants. Participants were provided with social information by referencing either their personal network on Twitter or all Twitter users. Results: A total of 1424 Twitter users participated in 3 studies (n=824, n=322, and n=278). Across all 3 studies, we found that social cues that reference users' personal network combined with a misinformation flag reduced the sharing of misleading but not control information and improved overall sharing quality. We show that this improvement could be driven by a change in injunctive social norms (study 2) but not social identity (study 3). Conclusions: Social reference cues combined with misinformation flags can significantly and meaningfully reduce the amount of COVID-19-related misinformation shared and improve overall sharing quality. They are a feasible and scalable way to effectively curb the sharing of COVID-19-related misinformation on social media.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] Fact or fake: information, misinformation and disinformation via social media
    Lim, Xin-Jean
    Quach, Sara
    Thaichon, Park
    Cheah, Jun-Hwa
    Ting, Hiram
    JOURNAL OF STRATEGIC MARKETING, 2024, : 659 - 664
  • [32] Persuasion strategies of misinformation-containing posts in the social media
    Chen, Sijing
    Xiao, Lu
    Mao, Jin
    INFORMATION PROCESSING & MANAGEMENT, 2021, 58 (05)
  • [33] Social Media: An Exploratory Study of Information, Misinformation, Disinformation, and Malinformation
    Hussain, Mumtaz
    Soomro, Tariq Rahim
    APPLIED COMPUTER SYSTEMS, 2023, 28 (01) : 13 - 20
  • [34] The diffusion of misinformation on social media: Temporal pattern, message, and source
    Shin, Jieun
    Jian, Lian
    Driscoll, Kevin
    Bar, Francois
    COMPUTERS IN HUMAN BEHAVIOR, 2018, 83 : 278 - 287
  • [35] COVID-19 Misinformation on Social Media: A Scoping Review
    Joseph, Andrew M.
    Fernandez, Virginia
    Kritzman, Sophia
    Eaddy, Isabel
    Cook, Olivia M.
    Lambros, Sarah
    Silva, Cesar E. Jara
    Arguelles, Daryl
    Abraham, Christy
    Dorgham, Noelle
    Gilbert, Zachary A.
    Chacko, Lindsey
    Hirpara, Ram J.
    Mayi, Bindu S.
    Jacobs, Robin J.
    CUREUS JOURNAL OF MEDICAL SCIENCE, 2022, 14 (04)
  • [36] Misinformation Correction across Social Media Platforms
    Zhao, Wenqing
    2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2019), 2019, : 1371 - 1376
  • [37] Spread of misinformation on social media: What contributes to it and how to combat it
    Chen, Sijing
    Xiao, Lu
    Kumar, Akit
    COMPUTERS IN HUMAN BEHAVIOR, 2023, 141
  • [38] Media Trust Under Threat: Antecedents and Consequences of Misinformation Perceptions on Social Media
    Stubenvoll, Marlis
    Heiss, Raffael
    Matthes, Joerg
    INTERNATIONAL JOURNAL OF COMMUNICATION, 2021, 15 : 2765 - 2786
  • [39] Spread of Misinformation Online: Simulation Impact of Social Media Newsgroups
    Safieddine, Fadi
    Dordevic, Milan
    Pourghomi, Pardis
    2017 COMPUTING CONFERENCE, 2017, : 899 - 906
  • [40] Social media, misinformation and fake news in the pandemic: the dominant gaps and future research avenues
    Nutsugah, Noel
    Mensah, Kobby
    Odoom, Raphael
    Ayarnah, Amin
    ONLINE INFORMATION REVIEW, 2025, 49 (02) : 335 - 352