A two‐step approach to detect and understand dismisinformation events occurring in social media: A case study with critical times
A two‐step approach to detect and understand dismisinformation events occurring in social media: A case study with critical times
复制标题
DOI：
10.1111/1468-5973.12483
复制
发表时间：
2023-07
影响因子：
3.1
通讯作者：
Seungwon Yang;Haeyong Chung;Dipak Singh;Shayan Shams
中科院分区：
文献类型：
--
作者：
Seungwon Yang;Haeyong Chung;Dipak Singh;Shayan Shams
本文描述了一种新的两步方法，用于检测和理解在灾难和危机事件中发生的社交媒体上的disinformation / misinformation事件。为了检测假新闻事件，我们设计了一种基于深度学习的检测算法，然后使用迁移学习方案对其进行训练，以便算法可以决定一组给定的谣言相关推文是否为diss /misinformation事件。为了理解虚假信息是如何在社交网络中传播的，并识别那些负责创造和消费虚假信息的人，我们提出了DismisInfoVis，它由各种可视化组成，包括社交网络图、地图、折线图、饼图和条形图。通过整合这些深度学习和多视角可视化技术，我们可以从多个角度更深入地了解社交媒体中的disinformation /misinformation事件。我们详细描述了检测算法的实现、培训过程和性能评估，以及DismisInfoVis用于dis/ misinformation数据分析的设计和利用。我们希望这项研究将有助于提高在关键时刻在社交媒体上生成和分享的信息的质量，最终帮助受灾者和公众从灾害和危机事件的影响中恢复过来。
This 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.