Fake News Early Detection

Abstract

Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theorydriven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information. CCS Concepts: • Human-centered computing → Collaborative and social computing theory, concepts and paradigms; • Computing methodologies → Natural language processing; Machine learning; • Security and privacy → Social aspects of security and privacy; • Applied computing → Sociology; Computer forensics;

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

“…The results indicated that news focused on fake health information was often considered as bad messaging and that news based on the evidence of social influence was acceptable and respected. Furthermore, the study [19] proposed a theory-based model to early distinguish fake news. Based on the news content, the model detected fake news when it was published on news sites before spreading on social media platforms.…”

Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

“…In the same vein, the work in [21] employed supervised learning techniques to detect fake news at an early stage, when no information exists about the spreading of the news on social media. Thus, the approach relies on the lexicon, syntax, semantics, and argument organization of a news item.…”

The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media

“…There is no consistent definition of fake news; its identification is complex and can sometimes be difficult to determine [ 12 , 27 , 29 - 31 ]. The definition of fake news can be as broad as improper information or stories [ 18 , 27 , 32 ], or as narrow as verifiably false articles deliberately published by the media [ 11 , 12 , 27 ], and anything in between [ 13 , 33 ].…”

“…An automatic detection method could involve knowledge base retrieval systems [ 27 ], but breakthrough knowledge may be considered misinformation. Content style analysis is another automated method, based on the assumption that there is a certain pattern in intentional news [ 31 , 35 - 37 ], but outlets may evade detection by manipulating their writing style [ 27 ]. In this study, we employed a style-based approach to fake news detection.…”

“…In this study, we employed a style-based approach to fake news detection. Generally speaking, the typical characteristics of fake news are associated with the writing style, quantity of subjective language, and sentiment lexical or incited discourse [ 26 , 27 , 31 , 35 - 37 ]. We adopted the scores of suspicion and incitement provided by the Islander news analysis system [ 28 ] in which a language model, RoBERTa [ 38 ], was trained using a supervised learning approach to analyze and score news ( Figure 2 ).…”