The Role of Trust in Rumor Suppression on Social Media: A Multi-Method Approach Applying the Trust Scores in Social Media (TSM) Algorithm
2019-08
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The Role of Trust in Rumor Suppression on Social Media: A Multi-Method Approach Applying the Trust Scores in Social Media (TSM) Algorithm
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2019-08
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Commercial rumor about an organization or brand, especially on social media, presents a special challenge for marketers and communication practitioners because of the fast flow and exchange of information among peers. Despite the importance of refuting rumors quickly and effectively, research on the effects and effectiveness of refuting rumor messages has been limited. To advance the literature in this emerging research area, the current project examines the impact of an interpersonal relational factor on the dissemination and effectiveness of rumor-refutation communication. The role of interpersonal relational factors is particularly important given that rumors spread through word-of-mouth (WOM) communication. Rumor-refutation communication should also utilize the same communication conduit for rapid and effective refutation. To advance rumor-refutation research and address the rising problem of commercial rumors, this dissertation project aimed to examine 1) how interpersonal influence among peers affects the belief and retransmission of rumors and rumor-refutation messages, and 2) potentially influential message characteristics that could help enhance interpersonal influence on readers’ belief of the rumors and rumor refutation messages, and the retransmission of rumor-refutation messages. To achieve these research goals, this study adopted a multi-method approach. Study 1 is a between-subjects repeated-measures experiment with a 2 (high-trustworthy vs. low-trustworthy source) x 2 (presence vs. absence of a trust cue) design that examines how the trustworthiness of the message source and a trust cue designed to induce message trustworthiness affect the belief and retransmission intention of rumors and rumor-refutation messages. Study 2 utilizes a computational research approach using the Trust Scores in Social Media (TSM) algorithm to test how mathematically captured trustworthiness scores of the sources of rumor-refutation messages influence actual message retransmission and how such an effect is moderated by the presence of trust cues included in the messages. The key findings in Study 1 revealed that a high-trustworthy source compared to a low-trustworthy source led to higher levels of rumor-refutation message belief and retransmission intention, but source trustworthiness did not affect the extent of reduction in the rumor belief and retransmission intention. Inclusion of a trust cue also did not moderate the impact of source trustworthiness on the belief and retransmission intention for both the rumor and rumor-refutation messages. Instead, it showed main effects in increasing the rumor-refutation message belief and retransmission intention, and a decreasing rumor belief and retransmission intention. The findings in Study 2 further confirmed the effects of the trustworthiness of the source on rumor-refutation message retransmission. However, inclusion of trust cues did not augment the source trustworthiness effects. The results indicated that the influence of source trustworthiness was stronger when the rumor-refutation message had no embedded trust cue. This study advances the rumor and electronic word-of-mouth research field by revealing how interpersonal influence among peer social media users can contribute to the effects and effectiveness of rumor-suppression communication. The findings also offer useful practical implications for identifying effective rumor-refutation dissemination hubs and refutation message-crafting strategies for a successful rumor-suppression campaign.
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University of Minnesota Ph.D. dissertation. August 2019. Major: Mass Communication. Advisor: Jisu Huh. 1 computer file (PDF); viii, 196 pages.
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Kim, Hyejin. (2019). The Role of Trust in Rumor Suppression on Social Media: A Multi-Method Approach Applying the Trust Scores in Social Media (TSM) Algorithm. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/209091.
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