Disaster Misinformation and Its Corrections on Social Media: Spatiotemporal Proximity, Social Network, and Sentiment Contagion

Abstract

Misinformation disseminated via online social networks can cause social confusion and result in inadequate responses during disasters and emergencies. To contribute to social media-based disaster resilience, we aim to decipher the spread of disaster misinformation and its correction through the case study of the disaster rumor during Hurricane Sandy (2012) on Twitter. We first leveraged social network analysis to identify different types of accounts that are influential in spreading and debunking disaster misinformation. Second, we examined how the spatiotemporal proximity to the rumor event influences the sharing of misinformation and the sharing of corrections on Twitter. Third, through sentiment analysis, we went further by examining how spatiotemporal and demographic similarity between social media users affect behavioral and emotional responses to misinformation. Finally, sentiment contagion across rumor and correction networks was also examined. Our findings generate novel insights into detecting and counteracting misinformation using social media with implications for disaster management.

在线社交网络的虚假信息, 能够造成社会混乱, 导致在灾害和紧急情况下的应对不力。为了促进基于社交媒体的抗灾能力, 我们研究了飓风桑迪(2012)灾害谣言的推特案例, 解读了虚假灾害信息的传播和更正。通过社交网络分析, 我们识别了那些有影响力的传播和揭露虚假灾害信息的不同类型账户。其次, 研究了谣言事件的时空距离如何影响虚假信息及其更正的推特共享。第三, 通过情绪分析, 进一步研究了社交媒体用户之间的时空和人口统计相似性, 如何影响了对虚假信息的行为和情绪反应。最后, 我们探讨了谣言和更正网络中的情绪传染。本文为灾害管理中利用社交媒体检测和反击虚假信息, 提供了新见解。

La desinformación difundida en las redes sociales puede llevar a la confusión social y dar lugar a respuestas inadecuadas durante desastres y emergencias. Para contribuir a la resiliencia frente a los desastres a través de los medios sociales, nos enfocamos a descifrar el proceso de difundir desinformación, y a su corrección, a través de un estudio de caso sobre el rumor de desastre durante el Huracán Sandy (2012), en Twitter. Primero de todo aplicamos análisis de redes sociales para identificar los diferentes tipos de cuenta que tienen influencia tanto en la difusión como en el desmentido de la desinformación sobre desastres. En segundo lugar, examinamos cómo la proximidad espaciotemporal al evento del rumor influye en el proceso de compartir tanto la desinformación como el intercambio de correcciones, en Twitter. Tercero, por medio del análisis de sentimiento, avanzamos aún más al examinar cómo afectan la similitud espaciotemporal y demográfica entre los usuarios de los medios sociales las respuestas conductuales y emocionales a la desinformación. Finalmente, también se examinó el contagio de sentimientos a través de las redes de rumor y corrección. Nuestros hallazgos generan nuevas visiones para detectar desinformación mediante el uso de los medios sociales, con implicaciones en el manejo de desastres, y cómo contrarrestarla.

Key Words:

• disaster management
• misinformation
• sentiment contagion
• social media
• social networks

关键词:

• 虚假信息
• 情绪传染
• 社交媒体
• 社交网络

Palabras clave:

• contagio de sentimientos
• desinformación
• manejo de desastres
• medios sociales de comunicación
• redes sociales

Acknowledgments

We acknowledge the insightful comments from three anonymous reviewers. We also thank Chenfei Xiong for initial data analysis for this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental Material

Figure A.1, Table A.1, and Table A.2 can be found in the Supplemental Material. Figure A.1 illustrates some examples of rumors shared on social media. Table A.1 and Table A.2 represent the topic modeling results for rumor tweets and correction tweets, respectively. These supplemental data for this article can be accessed on the publisher’s site at: https://doi.org/10.1080/24694452.2023.2271549.

Additional information

Notes on contributors

Wei Zhai

WEI ZHAI is an Assistant Professor in Urban and Regional Planning at the University of Texas at San Antonio, San Antonio, TX 78249, USA. E-mail: [email protected]. His research investigates disaster resilience, environmental justice, urban data science, and urban artificial intelligence.

Hang Yu

HANG YU is a Master’s Student in Computer Science at Brandeis University, Waltham, MA 02453, USA. E-mail: [email protected]. His research interests include data science and machine learning.

Céline Yunya Song

CÉLINE YUNYA SONG is a Professor in the Department of Journalism and also the Director of the School of Communication’s Artificial Intelligence and Media Research Lab at Hong Kong Baptist University, Hong Kong. E-mail: [email protected]. Her research interests include global communication, social computing, computer-mediated networks, digital media, cyberpsychology, and behavior.