AbstractThis article describes a novel two‐step approach of detecting and understanding dis/misinformation events in social media that occur during disasters and crisis events. To detect false news events, we designed a deep learning‐based detection algorithm and then trained it with a transfer learning scheme so that the algorithm could decide whether a given group of rumor‐related tweets is a dis/misinformation event. For understanding how dis/misinformation was diffused in social networks and identifying those who are responsible for creating and consuming false information, we present DismisInfoVis, which consists of various visualisations, including a social network graph, a map, line charts, pie charts, and bar charts. By integrating these deep learning and multi‐view visualisation techniques, we could gain a deeper insight into dis/misinformation events in social media from multiple angles. We describe in detail the implementation, training process, and performance evaluations of the detection algorithm and the design and utilization of DismisInfoVis for dis/misinformation data analyses. We hope that this study will contribute to improving the quality of information generated and shared on social media during critical times, eventually helping both the affected and the general public recover from the impacts of disasters and crisis events.

More from our Archive

• DOI: 10.3390/su151310543 2023

  The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

  Vijendra Kumar, Hazi Md. Azamathulla, Kul Vaibhav Sharma, Darshan J. Mehta, Kiran Tota Maharaj

• DOI: 10.3390/su151410821 2023

  Microplastics as an Emerging Threat to the Global Environment and Human Health

  Shampa Ghosh, Jitendra Kumar Sinha, Soumya Ghosh, Kshitij Vashisth, Sungsoo Han, Rakesh Bhaskar

• DOI: 10.3390/su151310663 2023

  Unraveling the Interrelationship of Digitalization, Renewable Energy, and Ecological Footprints within the EKC Framework: Empirical Insights from the United States

  Najia Saqib, Ivan A. Duran, Ilhan Ozturk

• DOI: 10.3390/su151310645 2023

  Green Environmental Management System to Support Environmental Performance: What Factors Influence SMEs to Adopt Green Innovations?

  Abdalwali Lutfi, Hamza Alqudah, Mahmaod Alrawad, Ahmad Farhan Alshira’h, Malek Hamed Alshirah, Mohammed Amin Almaiah, Adi Alsyouf, Mohammed Faisal Hassan

• DOI: 10.3390/su151411065 2023

  Green Finance, Enterprise Energy Efficiency, and Green Total Factor Productivity: Evidence from China

  Hepei Li, Chen Chen, Muhammad Umair

• DOI: 10.3390/su151310729 2023

  Innovations and the CO2 Emissions Nexus in the MENA Region: A Spatial Analysis

  Haider Mahmood, Maham Furqan, Najia Saqib, Anass Hamadelneel Adow, Muzaffar Abbas

• DOI: 10.3390/su151310442 2023

  Sustainable Potato Growth under Straw Mulching Practices

  Abdul Waheed, Chuang Li, Murad Muhammad, Mushtaq Ahmad, Khalid Ali Khan, Hamed A. Ghramh, Zhongwei Wang, Daoyuan Zhang

• DOI: 10.3390/su151310329 2023

  From Neglect to Progress: Assessing Social Sustainability and Decent Work in the Tourism Sector

  Eleonora Santos

• DOI: 10.3390/su151410976 2023

  Driving Sustainable Disaster Risk Reduction: A Rapid Review of the Policies and Strategies in Saudi Arabia

  Ahmed M. Al-Wathinani, Dennis G. Barten, Marta Borowska-Stefańska, Paweł Gołda, Noora A. AlDulijan, Mohammad A. Alhallaf, Lujain O. Samarkandi, Abdullah S. Almuhaidly, Mariusz Goniewicz, Waleed O. Samarkandi, Krzysztof Goniewicz

• DOI: 10.3390/su151410940 2023

  Wastewater Treatment and Reuse for Sustainable Water Resources Management: A Systematic Literature Review

  Jorge Alejandro Silva