Search Results for author:
Found 17 papers, 13 papers with code
Mining Dual Emotion for Fake News Detection
Emotion plays an important role in detecting fake news online.
Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection
Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views.
Learning to Disentangle GAN Fingerprint for Fake Image Attribution
Adopting a multi-task framework, we propose a GAN Fingerprint Disentangling Network (GFD-Net) to simultaneously disentangle the fingerprint from GAN-generated images and produce a content-irrelevant representation for fake image attribution.
Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning
To this end, we build a Preference-aware Fake News Detection Framework (Pref-FEND), which learns the respective preferences of pattern- and fact-based models for joint detection.
Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims
By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns.
DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection
However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images.
Zoom Out and Observe: News Environment Perception for Fake News Detection
To differentiate fake news from real ones, existing methods observe the language patterns of the news post and "zoom in" to verify its content with knowledge sources or check its readers' replies.
Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
In this paper, we propose an entity debiasing framework (\textbf{ENDEF}) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective.
Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world.
Memory-Guided Multi-View Multi-Domain Fake News Detection
In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^3$FEND) to address these two challenges.
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer
To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains.
Combating Online Misinformation Videos: Characterization, Detection, and Future Directions
With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem.
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection
In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data.
Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
To instantiate this proposal, we design an adaptive rationale guidance network for fake news detection (ARG), in which SLMs selectively acquire insights on news analysis from the LLMs' rationales.
Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting
To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e. g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts.
Understanding News Creation Intents: Frame, Dataset, and Method
As the disruptive changes in the media economy and the proliferation of alternative news media outlets, news intent has progressively deviated from ethical standards that serve the public interest.
Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling
With the rapidly increasing application of large language models (LLMs), their abuse has caused many undesirable societal problems such as fake news, academic dishonesty, and information pollution.
