Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the context of fake news detection, remains significantly underexplored. One of the key challenges is the scarcity of large-scale, realistic, and well-annotated datasets that can serve as reliable benchmarks for outlier detection. To bridge this gap, we introduce TAGFN, a large-scale, real-world text-attributed graph dataset for outlier detection, specifically fake news detection. TAGFN enables rigorous evaluation of both traditional and LLM-based graph outlier detection methods. Furthermore, it facilitates the development of misinformation detection capabilities in LLMs through fine-tuning. We anticipate that TAGFN will be a valuable resource for the community, fostering progress in robust graph-based outlier detection and trustworthy AI. The dataset is publicly available at https://huggingface.co/datasets/kayzliu/TAGFN and our code is available at https://github.com/kayzliu/tagfn.

Fake news detection has been a long-standing research focus in social networks. Recent studies suggest that incorporating sentiment information from both news content and user comments can enhance detection performance. However, existing approaches typically treat sentiment features as auxiliary signals, overlooking role differentiation, that is, the same sentiment polarity may originate from users with distinct roles, thereby limiting their ability to capture nuanced patterns for effective detection. To address this issue, we propose SARC, a Sentiment-Augmented Role Clustering framework which utilizes sentiment-enhanced deep clustering to identify user roles for improved fake news detection. The framework first generates user features through joint comment text representation (with BiGRU and Attention mechanism) and sentiment encoding. It then constructs a differentiable deep clustering module to automatically categorize user roles. Finally, unlike existing approaches which take fake news label as the unique supervision signal, we propose a joint optimization objective integrating role clustering and fake news detection to further improve the model performance. Experimental results on two benchmark datasets, RumourEval-19 and Weibo-comp, demonstrate that SARC achieves superior performance across all metrics compared to baseline models. The code is available at: https://github.com/jxshang/SARC.

The use of large language models (LLMs) is becoming common in political science and digital media research. While LLMs have demonstrated ability in labelling tasks, their effectiveness to classify Political Content (PC) from URLs remains underexplored. This article evaluates whether LLMs can accurately distinguish PC from non-PC using both the text and the URLs of news articles across five countries (France, Germany, Spain, the UK, and the US) and their different languages. Using cutting-edge models, we benchmark their performance against human-coded data to assess whether URL-level analysis can approximate full-text analysis. Our findings show that URLs embed relevant information and can serve as a scalable, cost-effective alternative to discern PC. However, we also uncover systematic biases: LLMs seem to overclassify centrist news as political, leading to false positives that may distort further analyses. We conclude by outlining methodological recommendations on the use of LLMs in political science research.

The growing importance of environmental, social, and governance data in regulatory and investment contexts has increased the need for accurate, interpretable, and internationally aligned representations of non-financial risks, particularly those reported in unstructured news sources. However, aligning such controversy-related data with principle-based normative frameworks, such as the United Nations Global Compact or Sustainable Development Goals, presents significant challenges. These frameworks are typically expressed in abstract language, lack standardized taxonomies, and differ from the proprietary classification systems used by commercial data providers. In this paper, we present a semi-automatic method for constructing structured knowledge representations of environmental, social, and governance events reported in the news. Our approach uses lightweight ontology design, formal pattern modeling, and large language models to convert normative principles into reusable templates expressed in the Resource Description Framework. These templates are used to extract relevant information from news content and populate a structured knowledge graph that links reported incidents to specific framework principles. The result is a scalable and transparent framework for identifying and interpreting non-compliance with international sustainability guidelines.

Most existing detection algorithms focus on analyzing news content and social context to detect fake news. However, these approaches typically detect fake news based on specific platforms, ignoring differences in propagation characteristics across platforms. In this paper, we introduce the MPPFND dataset, which captures propagation structures across multiple platforms. We also describe the commenting and propagation characteristics of different platforms to show that their social contexts have distinct features. We propose a multi-platform fake news detection model (APSL) that uses graph neural networks to extract social context features from various platforms. Experiments show that accounting for cross-platform propagation differences improves fake news detection performance.

In today's digital environment, the rapid propagation of fake news via social networks poses significant social challenges. Most existing detection methods either employ traditional classification models, which suffer from low interpretability and limited generalization capabilities, or craft specific prompts for large language models (LLMs) to produce explanations and results directly, failing to leverage LLMs' reasoning abilities fully. Inspired by the saying that "truth becomes clearer through debate," our study introduces a novel multi-agent system with LLMs named TruEDebate (TED) to enhance the interpretability and effectiveness of fake news detection. TED employs a rigorous debate process inspired by formal debate settings. Central to our approach are two innovative components: the DebateFlow Agents and the InsightFlow Agents. The DebateFlow Agents organize agents into two teams, where one supports and the other challenges the truth of the news. These agents engage in opening statements, cross-examination, rebuttal, and closing statements, simulating a rigorous debate process akin to human discourse analysis, allowing for a thorough evaluation of news content. Concurrently, the InsightFlow Agents consist of two specialized sub-agents: the Synthesis Agent and the Analysis Agent. The Synthesis Agent summarizes the debates and provides an overarching viewpoint, ensuring a coherent and comprehensive evaluation. The Analysis Agent, which includes a role-aware encoder and a debate graph, integrates role embeddings and models the interactions between debate roles and arguments using an attention mechanism, providing the final judgment.

Affective polarization, or, inter-party hostility, is increasingly recognized as a pervasive issue in democracies worldwide, posing a threat to social cohesion. The digital media ecosystem, now widely accessible and ever-present, has often been implicated in accelerating this phenomenon. However, the precise causal mechanisms responsible for driving affective polarization have been a subject of extensive debate. While the concept of echo chambers, characterized by individuals ensconced within like-minded groups, bereft of counter-attitudinal content, has long been the prevailing hypothesis, accumulating empirical evidence suggests a more nuanced picture. This study aims to contribute to the ongoing debate by employing an agent-based model to illustrate how affective polarization is either fostered or hindered by individual news consumption and dissemination patterns based on ideological alignment. To achieve this, we parameterize three key aspects: (1) The affective asymmetry of individuals' engagement with in-party versus out-party content, (2) The proportion of in-party members within one's social neighborhood, and (3) The degree of partisan bias among the elites within the population. Subsequently, we observe macro-level changes in affective polarization within the population under various conditions stipulated by these parameters. This approach allows us to explore the intricate dynamics of affective polarization within digital environments, shedding light on the interplay between individual behaviors, social networks, and information exposure.

Current approaches to emotion detection often overlook the inherent subjectivity of affective experiences, instead relying on aggregated labels that mask individual variations in emotional responses. We introduce iNews, a novel large-scale dataset explicitly capturing subjective affective responses to news headlines. Our dataset comprises annotations from 291 demographically diverse UK participants across 2,899 multimodal Facebook news posts from major UK outlets, with an average of 5.18 annotators per sample. For each post, annotators provide multifaceted labels including valence, arousal, dominance, discrete emotions, content relevance judgments, sharing likelihood, and modality importance ratings (text, image, or both). Furthermore, we collect comprehensive annotator persona information covering demographics, personality, media trust, and consumption patterns, which explain 15.2% of annotation variance - higher than existing NLP datasets. Incorporating this information yields a 7% accuracy gain in zero-shot prediction and remains beneficial even with 32-shot. iNews will enhance research in LLM personalization, subjectivity, affective computing, and individual-level behavior simulation.

Cross-domain fake news detection aims to mitigate domain shift and improve detection performance by transferring knowledge across domains. Existing approaches transfer knowledge based on news content and user engagements from a source domain to a target domain. However, these approaches face two main limitations, hindering effective knowledge transfer and optimal fake news detection performance. Firstly, from a micro perspective, they neglect the negative impact of veracity-irrelevant features in news content when transferring domain-shared features across domains. Secondly, from a macro perspective, existing approaches ignore the relationship between user engagement and news content, which reveals shared behaviors of common users across domains and can facilitate more effective knowledge transfer. To address these limitations, we propose a novel macro- and micro- hierarchical transfer learning framework (MMHT) for cross-domain fake news detection. Firstly, we propose a micro-hierarchical disentangling module to disentangle veracity-relevant and veracity-irrelevant features from news content in the source domain for improving fake news detection performance in the target domain. Secondly, we propose a macro-hierarchical transfer learning module to generate engagement features based on common users' shared behaviors in different domains for improving effectiveness of knowledge transfer. Extensive experiments on real-world datasets demonstrate that our framework significantly outperforms the state-of-the-art baselines.

The rapid proliferation of fake news on social media threatens social stability, creating an urgent demand for more effective detection methods. While many promising approaches have emerged, most rely on content analysis with limited semantic depth, leading to suboptimal comprehension of news content.To address this limitation, capturing broader-range semantics is essential yet challenging, as it introduces two primary types of noise: fully connecting sentences in news graphs often adds unnecessary structural noise, while highly similar but authenticity-irrelevant sentences introduce feature noise, complicating the detection process. To tackle these issues, we propose BREAK, a broad-range semantics model for fake news detection that leverages a fully connected graph to capture comprehensive semantics while employing dual denoising modules to minimize both structural and feature noise. The semantic structure denoising module balances the graph's connectivity by iteratively refining it between two bounds: a sequence-based structure as a lower bound and a fully connected graph as the upper bound. This refinement uncovers label-relevant semantic interrelations structures. Meanwhile, the semantic feature denoising module reduces noise from similar semantics by diversifying representations, aligning distinct outputs from the denoised graph and sequence encoders using KL-divergence to achieve feature diversification in high-dimensional space. The two modules are jointly optimized in a bi-level framework, enhancing the integration of denoised semantics into a comprehensive representation for detection. Extensive experiments across four datasets demonstrate that BREAK significantly outperforms existing methods in identifying fake news. Code is available at https://anonymous.4open.science/r/BREAK.