The rise of micro-videos has reshaped how misinformation spreads, amplifying its speed, reach, and impact on public trust. Existing benchmarks typically focus on a single deception type, overlooking the diversity of real-world cases that involve multimodal manipulation, AI-generated content, cognitive bias, and out-of-context reuse. Meanwhile, most detection models lack fine-grained attribution, limiting interpretability and practical utility. To address these gaps, we introduce WildFakeBench, a large-scale benchmark of over 10,000 real-world micro-videos covering diverse misinformation types and sources, each annotated with expert-defined attribution labels. Building on this foundation, we develop FakeAgent, a Delphi-inspired multi-agent reasoning framework that integrates multimodal understanding with external evidence for attribution-grounded analysis. FakeAgent jointly analyzes content and retrieved evidence to identify manipulation, recognize cognitive and AI-generated patterns, and detect out-of-context misinformation. Extensive experiments show that FakeAgent consistently outperforms existing MLLMs across all misinformation types, while WildFakeBench provides a realistic and challenging testbed for advancing explainable micro-video misinformation detection. Data and code are available at: https://github.com/Aiyistan/FakeAgent.

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research.

Zhi Zeng, Jiaying Wu, Minnan Luo, Herun Wan, Xiangzheng Kong, Zihan Ma, Guang Dai, Qinghua Zheng. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Social media platforms provide an ideal environment to spread misinformation, where social bots can accelerate the spread.This paper explores the interplay between social bots and misinformation on the Sina Weibo platform.We construct a large-scale dataset that includes annotations for both misinformation and social bots.From the misinformation perspective, the dataset is multimodal, containing 11,393 pieces of misinformation and 16,416 pieces of verified information.From the social bot perspective, this dataset contains 65,749 social bots and 345,886 genuine accounts, annotated using a weakly supervised annotator.Extensive experiments demonstrate the comprehensiveness of the dataset, the clear distinction between misinformation and real information, and the high quality of social bot annotations.Further analysis illustrates that: (i) social bots are deeply involved in information spread; (ii) misinformation with the same topics has similar content, providing the basis of echo chambers, and social bots would amplify this phenomenon; and (iii) social bots generate similar content aiming to manipulate public opinions.

Short videos have become a pivotal medium for news dissemination but have also accelerated the spread of fake news. Although existing detection methods have achieved significant results, they often neglect the impact of noise in multimodal data, resulting in degraded detection accuracy and limited generalization. To address these challenges, we propose the Progressive Noise-Resilient Network (PNRN), a framework designed to adaptively mitigate noise in complex scenarios. PNRN comprises two key components: a unimodal noise-resilient module and a multimodal adaptive fusion module. The unimodal noise-resistant module leverages information bottlenecks to effectively filter modality-specific noise and strengthen feature relevance within individual modalities. The multimodal adaptive fusion module employs dynamic multi-routing and a mixture of experts to dynamically prioritize informative multimodal representations while reducing cross-modal inconsistencies. Experimental results demonstrate that PNRN significantly enhances fake news video detection performance and exhibits strong generalization capabilities in diverse and noisy social environments. Main code is available at https://anonymous.4open.science/r/PNRN-F308.

Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the constant addition of entities, relations and facts. To address such dynamic nature of KGs, several continual knowledge graph embedding (CKGE) methods have been developed to efficiently update KG embeddings to accommodate new facts while maintaining learned knowledge. As KGs grow at different rates and scales in real-world scenarios, existing CKGE methods often fail to consider the varying scales of updates and lack systematic evaluation throughout the entire update process. In this paper, we propose SAGE, a scale-aware gradual evolution framework for CKGE. Specifically, SAGE firstly determine the embedding dimensions based on the update scales and expand the embedding space accordingly. The Dynamic Distillation mechanism is further employed to balance the preservation of learned knowledge and the incorporation of new facts. We conduct extensive experiments on seven benchmarks, and the results show that SAGE consistently outperforms existing baselines, with a notable improvement of 1.38% in MRR, 1.25% in H@1 and 1.6% in H@10. Furthermore, experiments comparing with fixed dimensions methods show that SAGE achieves optimal performance on every snapshot, demonstrating the importance of adaptive embedding dimensions in CKGE.

As short videos become a dominant medium for news dissemination, fake news videos pose increasing threats to public trust and information integrity. Existing methods primarily focus on learning multimodal representations to predict binary veracity labels, yet they overlook the use of external evidence, which is important for identifying more sophisticated fake news that subtly exploits psychological cues and cognitive biases. Moreover, these approaches do not provide fine-grained attribution labels, which are essential for interpretable misinformation governance. To address these limitations, we introduce EvidSV, the first comprehensive benchmark supporting evidence- and attribution-aware fake news video detection. Drawing inspiration from the human cognitive process of interpreting news-related content, we propose MUKE, a multi-view knowledge progressive enhancement learning framework. By jointly analyzing both the news content and supporting evidence, MUKE (1) facilitates the understanding of news semantics to (2) progressively refine shared domain knowledge, and (3) adaptively summarizes multi-view knowledge to assess news veracity. Extensive experiments demonstrate that MUKE consistently outperforms existing methods in both fake news detection and attribution, and generalizes effectively to previously unseen domains. Our code is available at https://github.com/zzeng1998/EvidSV.

While the proliferation of fake news poses a significant threat to information integrity, existing efforts to counter it, especially within personalized news recommendation systems, have proven inadequate.Traditional methods, which often rely on classifiers to filter out fake content, are limited by their accuracy and their inability to fully capture the diverse interests of users.To address these challenges, we proposed PRISM-Protection-enhanced Recommendation with Interest-aware Sequential Modeling-a novel framework based on diffusion models.PRISM harnesses the generative and control capabilities of diffusion models to progressively learn the implicit distribution of user interests from their reading history, thereby generating personalized recommendations that align with both their linguistic preferences and interest domains.Furthermore, PRISM incorporates pre-trained authenticity representations as constraints during content generation, ensuring the credibility of the recommended news and effectively curbing the spread of fake news.Comprehensive evaluations from multiple dimensions demonstrate the superiority of our model.

Fake news data, often sampled from the same communities, results in the veracity of news being highly correlated with certain textual and visual entities. This correlation leads fake news classification models to be prone to shortcut learning, quickly overfitting by capturing only shallow spurious correlations between labels and features. Consequently, neural networks trained on such data suffer from poor generalization and potential misclassification under distribution shifts. To address these critical challenges and enhance the robustness of fake news detection, in this paper, we propose a DIsentanglement-based Causality-awarE fake news detection method (DICE). DICE introduces a novel paradigm that moves beyond merely mitigating known correlations or relying on predefined bias categories. Specifically, DICE dynamically constructs multimodal news into a graph neural network, employing learnable node and edge mask disentanglers to effectively model and separate genuine causal relationships from spurious correlations between multimodal features and veracity labels. To reinforce this disentanglement process, we design a novel optimization framework that minimizes extrapolation risk and enforces representation orthogonality, leading to robust disentangled causal and biased representations. Extensive experiments demonstrate that DICE achieves superior performance on five large-scale fake news detection benchmarks. Additionally, our evaluation on a heavily biased fake news dataset demonstrates DICE’s strong generalization, suggesting its potential to inform a new paradigm in causal fake news detection. The code repo is available: https://github.com/mazihan880/DICE Code/.

The swift detection of multimedia fake news has emerged as a crucial task in combating malicious propaganda and safeguarding the security of the online environment.While existing methods have achieved commendable results in modeling entity-level inconsistency, addressing event-level inconsistency following the inherent subject-predicate logic of news and robustly learning news representations from poorquality news samples remain two challenges.In this paper, we propose an Event-dRiven fAke news Detection frAmewoRk (Event-Radar) based on multi-view learning, which integrates visual manipulation, textual emotion and multimodal inconsistency at event-level for fake news detection.Specifically, leveraging the capability of graph structures to capture interactions between events and parameters, Event-Radar captures event-level multimodal inconsistency by constructing an event graph that includes multimodal entity subject-predicate logic.Additionally, to mitigate the interference of poor-quality news, Event-Radar introduces a multi-view fusion mechanism, learning comprehensive and robust representations by computing the credibility of each view as a clue, thereby detecting fake news.Extensive experiments demonstrate that Event-Radar achieves outstanding performance on three large-scale fake news detection benchmarks.Our studies also confirm that Event-Radar exhibits strong robustness, providing a paradigm for detecting fake news from noisy news samples.

Detecting fake news on social media has become a crucial task in combating online misinformation and countering malicious propaganda. Existing methods rely on semantic consistency across modalities to fuse features and determine news authenticity. However, cunning fake news publisher manipulate image to ensure a high level of semantic consistency between news post and image, making it more difficult to distinguish fake news. To this end, we propose MHFFD (Mixed High-Frequency Feature Detector), a novel fake news detection framework that utilizes token-level semantic consistency evaluation to identify key elements in news content and provide guidance for discovering image manipulation and learning better news representations. Extensive experiments demonstrate that MHFFD outperforms state-of-the-art methods on two widely used fake news detection datasets. Further research also validates the effectiveness of token-level semantic alignment and manipulation detection.

Short videos turn into an important channel for public sharing, as well as they've become a fertile ground for fake news. Fake news video detection is to judge the veracity of news based on its different modal information, such as video, audio, text, image and social context information. Current detection models tend to learn the multimodal dataset biases within spurious correlations between news modalities and veracity labels as shortcuts, rather than learning how to integrate the multimodal information behind them to reason, resulting in seriously degrading their detection and generalization capabilities. To address this issues, we propose a Multimodal Multi-View Debiasing (MMVD) framework, which makes the first attempt to mitigate various multimodal biases for fake news video detection. Inspired by people's misleading situations by multimodal short videos, we summarize three cognitive biases: static, dynamic and social biases. MMVD put forward a multi-view causal reasoning strategy to learn unbiased dependencies within the cognitive biases, thus enhancing the unbiased prediction of multimodal videos. The extensive experimental results show that the MMVD could improve the detection performance of multimodal fake news video. Studies also confirm that our MMVD can mitigate multiple biases on complex real-world scenarios and improve generalization ability of fake news video detection.

Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect. However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from limited dataset scale, incomplete graph structure, as well as low annotation quality. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches. In this paper, we propose TwiBot-22, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets. In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress. To facilitate further research, we consolidate all implemented codes and datasets into the TwiBot-22 evaluation framework, where researchers could consistently evaluate new models and datasets. The TwiBot-22 Twitter bot detection benchmark and evaluation framework are publicly available at \url{https://twibot22.github.io/}.