Beyond News Contents: The Role of Social Context for Fake News Detection (1712.07709v2)

Abstract: Social media is becoming popular for news consumption due to its fast dissemination, easy access, and low cost. However, it also enables the wide propagation of fake news, i.e., news with intentionally false information. Detecting fake news is an important task, which not only ensures users to receive authentic information but also help maintain a trustworthy news ecosystem. The majority of existing detection algorithms focus on finding clues from news contents, which are generally not effective because fake news is often intentionally written to mislead users by mimicking true news. Therefore, we need to explore auxiliary information to improve detection. The social context during news dissemination process on social media forms the inherent tri-relationship, the relationship among publishers, news pieces, and users, which has potential to improve fake news detection. For example, partisan-biased publishers are more likely to publish fake news, and low-credible users are more likely to share fake news. In this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world datasets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection.

Overview of TriFN Framework for Fake News Detection

This paper presents a paper on exploiting social context for fake news detection and introduces the TriFN framework. The pervasive nature of fake news on social media necessitates advanced detection mechanisms, given the inadequate performance of content-only detection methods. This research highlights the potential of utilizing the tri-relationship among publishers, news articles, and users' social interactions to enhance classification accuracy.

Methodology

The authors propose TriFN, a tri-relationship embedding framework, which models publisher-news relationships and user-news interactions. TriFN leverages the latent features of news content while integrating the partisan biases of publishers and the credibilities of users, reflected in their social media engagements. The key components of TriFN include:

1. News Contents Embedding: Utilizes Nonnegative Matrix Factorization (NMF) to project news content into a latent feature space.
2. User Embedding: Extracts user features based on social media relationships, capturing the homophily principle that drives social links.
3. User-News Interaction: Models user credibility and its correlation with news veracity, incorporating it into the detection model.
4. Publisher-News Relationship: Captures the partisan biases of news publishers to provide additional context for classification.
5. Semi-Supervised Classification: Integrates news features for classification, making use of both labeled and unlabeled data.

Experimental Results

The framework's performance was evaluated using two real-world datasets, BuzzFeed and PolitiFact. The results indicate that TriFN significantly surpasses baseline methods in accuracy and F1 score, underscoring the efficacy of incorporating social context into the detection process. For example, compared to traditional content-based methods like LIWC and RST, TriFN achieves an improvement in classification metrics by effectively leveraging social context.

Implications and Future Directions

The findings suggest that incorporating social context provides complementary information that enhances fake news detection, supporting broader theoretical understandings of information diffusion and mis/disinformation dynamics. As fake news continues to evolve, the authors propose several avenues for future research:

• Early Detection: Further exploration into feature extraction models that enable detecting fake news in its early stages is crucial, considering the rapid spread of information on social platforms.
• Psychological Insights: Investigating features that capture the psychological intentions behind fake news creation may reveal deeper insights into its proliferation.
• User Analysis: Identifying malicious users can play a pivotal role in mitigation strategies and improving detection accuracy.

The TriFN framework exemplifies the shift towards more comprehensive models that account for the complex interplay of content and context in fake news detection. The research underlines the principle that context-aware approaches, which include various social dimensions, hold promise in addressing the challenges posed by fake news on digital platforms.