DisTrack：一种用于在线社交网络中半自动错误信息追踪的新工具
DisTrack: a new Tool for Semi-automatic Misinformation Tracking in Online Social Networks
AI总结：
本文提出DisTrack工具，结合NLP、社交网络分析和图可视化，用于检测、追踪和溯源在线社交网络中的错误信息，并通过三个案例研究验证其有效性。
摘要：
引言：本文介绍了DisTrack，一种为追踪和分析在线社交网络（OSNs）中的错误信息而开发的方法论和工具。DisTrack旨在通过结合自然语言处理（NLP）、社交网络分析（SNA）和图可视化的方式来对抗错误信息的传播。其主要目标是检测错误信息、追踪其传播过程、识别其来源，并评估网络中各类行为者的影响力。方法：DisTrack的架构融合了多种方法，包括关键词搜索、语义相似度评估和图生成技术。这些方法共同促进了错误信息的监测、基于与已知虚假声明一致性的内容分类，以及通过详细图谱对传播级联进行可视化。该工具专门用于捕捉和分析数字环境中错误信息传播的动态特性。结果：DisTrack的有效性通过三个聚焦于不同主题的案例研究得到验证：诋毁/仇恨言论、反疫苗错误信息以及关于俄乌冲突的虚假叙事。这些研究表明DisTrack具备区分传播虚假信息的帖子和反驳这些信息的帖子的能力，并能追溯错误信息从其产生之初的演变过程。结论：本研究证实DisTrack是错误信息分析领域中的一个有价值的工具。它能够有效区分不同类型的错误信息，并追踪其随时间的发展。通过提供一种在数字空间中理解和对抗错误信息的综合方法，DisTrack被证明是研究人员和从业者致力于减轻在线社交环境中虚假信息影响的重要资产。
英文摘要：
Introduction: This article introduces DisTrack, a methodology and a tool developed for tracking and analyzing misinformation within Online Social Networks (OSNs). DisTrack is designed to combat the spread of misinformation through a combination of Natural Language Processing (NLP) Social Network Analysis (SNA) and graph visualization. The primary goal is to detect misinformation, track its propagation, identify its sources, and assess the influence of various actors within the network. Methods: DisTrack's architecture incorporates a variety of methodologies including keyword search, semantic similarity assessments, and graph generation techniques. These methods collectively facilitate the monitoring of misinformation, the categorization of content based on alignment with known false claims, and the visualization of dissemination cascades through detailed graphs. The tool is tailored to capture and analyze the dynamic nature of misinformation spread in digital environments. Results: The effectiveness of DisTrack is demonstrated through three case studies focused on different themes: discredit/hate speech, anti-vaccine misinformation, and false narratives about the Russia-Ukraine conflict. These studies show DisTrack's capabilities in distinguishing posts that propagate falsehoods from those that counteract them, and tracing the evolution of misinformation from its inception. Conclusions: The research confirms that DisTrack is a valuable tool in the field of misinformation analysis. It effectively distinguishes between different types of misinformation and traces their development over time. By providing a comprehensive approach to understanding and combating misinformation in digital spaces, DisTrack proves to be an essential asset for researchers and practitioners working to mitigate the impact of false information in online social environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
