TY - GEN
T1 - Misinformation in the chinese weibo
AU - Xiao, Lu
AU - Chen, Sijing
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Social media users are increasingly influenced by misinformation and disinformation as the techniques offer affordances to rapidly spread information to large groups of people. Most of the existing studies about misinformation and disinformation are in the context of Western cultures, the influence of misinformation in Chinese context is underexplored. To fill this research gap, this study analyzed 26,138 Weibo posts that are marked as containing misinformation. We performed a frequency analysis of these posts’ metadata and the top 50 frequent nouns, verbs, and adjectives in the dataset, and examined the sentiment in the content. Our results show that many posts that contain misinformation tactically target topics that Chinese people are already concerned about. The persuasion literature implies that these characteristics increase the persuasive power of the posts. With the forward-asking verbs are frequently used in the posts, one behavior that the receivers are persuaded to perform is to share these posts with the others, which can contribute to the virality of the misinformation. Another alarming finding is that a large proportion of our collected posts asked the receivers for help and the posts showed gratitude to acknowledge the forwarding and helping behavior. Based on the trust literature and the notion that trust as a social reality, we discuss the potentially severe negative impact these posts can impose on the society as they undermine Weibo users’ trustfulness to others and to the social media platform.
AB - Social media users are increasingly influenced by misinformation and disinformation as the techniques offer affordances to rapidly spread information to large groups of people. Most of the existing studies about misinformation and disinformation are in the context of Western cultures, the influence of misinformation in Chinese context is underexplored. To fill this research gap, this study analyzed 26,138 Weibo posts that are marked as containing misinformation. We performed a frequency analysis of these posts’ metadata and the top 50 frequent nouns, verbs, and adjectives in the dataset, and examined the sentiment in the content. Our results show that many posts that contain misinformation tactically target topics that Chinese people are already concerned about. The persuasion literature implies that these characteristics increase the persuasive power of the posts. With the forward-asking verbs are frequently used in the posts, one behavior that the receivers are persuaded to perform is to share these posts with the others, which can contribute to the virality of the misinformation. Another alarming finding is that a large proportion of our collected posts asked the receivers for help and the posts showed gratitude to acknowledge the forwarding and helping behavior. Based on the trust literature and the notion that trust as a social reality, we discuss the potentially severe negative impact these posts can impose on the society as they undermine Weibo users’ trustfulness to others and to the social media platform.
KW - Misinformation
KW - Persuasion
KW - Trust
KW - Weibo
UR - http://www.scopus.com/inward/record.url?scp=85088528989&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-49570-1_28
DO - 10.1007/978-3-030-49570-1_28
M3 - Conference contribution
AN - SCOPUS:85088528989
SN - 9783030495695
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 407
EP - 418
BT - Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis - 12th International Conference, SCSM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Meiselwitz, Gabriele
PB - Springer
T2 - 12th International Conference on Social Computing and Social Media, SCSM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
Y2 - 19 July 2020 through 24 July 2020
ER -