用图模型分析新闻上下文，自动识别新冠假新闻。
Context-Based Fake News Detection using Graph Based Approach: ACOVID-19 Use-case
- 将新闻转为上下文图结构，挖掘内容关联模式。
- 基于最小描述长度的异常检测算法，准确识别偏离常态的假新闻。
- 结合真实疫情新闻数据，适合舆情监控与社交媒体风控场景。
在数字时代，虚假新闻传播速度极快，已成为重大挑战。本文提出一种基于上下文图的假新闻检测方法，使用Kaggle上的真实与虚假新闻数据集，并补充近期与新冠疫情相关的新闻，增强数据代表性。通过自然语言处理技术将新闻转化为上下文图结构，利用基于最小描述长度（MDL）的图异常检测（GBAD）算法进行图挖掘。该方法能发现数据中的正常模式，并识别出偏离这些模式的异常内容。图模型擅长捕捉丰富上下文信息，可发现传统统计或查询方法忽略的复杂关联，有效提升假新闻识别能力。
原文摘要 · Abstract (English)
In todayś digital world, fake news is spreading with immense speed. Its a significant concern to address. In this work, we addressed that challenge using novel graph based approach. We took dataset from Kaggle that contains real and fake news articles. To test our approach we incorporated recent covid-19 related news articles that contains both genuine and fake news that are relevant to this problem. This further enhances the dataset as well instead of relying completely on the original dataset. We propose a contextual graph-based approach to detect fake news articles. We need to convert news articles into appropriate schema, so we leverage Natural Language Processing (NLP) techniques to transform news articles into contextual graph structures. We then apply the Minimum Description Length (MDL)-based Graph-Based Anomaly Detection (GBAD) algorithm for graph mining. Graph-based methods are particularly effective for handling rich contextual data, as they enable the discovery of complex patterns that traditional query-based or statistical techniques might overlook. Our proposed approach identifies normative patterns within the dataset and subsequently uncovers anomalous patterns that deviate from these established norms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。