Papers by Guang Dai
On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs (2025.acl-long)
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| Challenge: | Evidence-enhanced detectors are able to detect malicious social text, but they are prone to evidence pollution. |
| Approach: | They propose three defense strategies to mitigate evidence pollution by large language models by machine-generated text detection and a mixture of experts. |
| Outcome: | The proposed defense strategies could mitigate evidence pollution, but they faced limitations for practical employment. |
IMOL: Incomplete-Modality-Tolerant Learning for Multi-Domain Fake News Video Detection (2025.acl-long)
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| Challenge: | Existing methods for fake news video detection focus on a specific domain and assume multiple modalities. |
| Approach: | They propose an incomplete-modality-tolerant learning framework for fake news video detection . they use cross-modal consistency to reconstruct missing modalities and transferable knowledge through cross-sample reasoning . |
| Outcome: | The proposed framework improves performance and robustness of multi-domain fake news video detection while generalizing to unseen domains under incomplete modality conditions. |
From What Is Said to Why It Is Framed: Intent-Aware News Video Understanding (2026.findings-acl)
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| Challenge: | Existing verification methods for short-form news videos neglect communicative intent . stylistic presentation and factual manipulation are often intertwined, resulting in shortcut learning . |
| Approach: | They propose a theory-grounded representation of communicative intent that captures creator stance, audience need activation, and communication strategy. |
| Outcome: | The proposed framework captures creator stance, audience need activation, and communication strategy. |
How Do Social Bots Participate in Misinformation Spread? A Comprehensive Dataset and Analysis (2025.emnlp-main)
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| Challenge: | Social media platforms provide an ideal environment to spread misinformation, where social bots can accelerate the spread. |
| Approach: | They construct a large-scale dataset that includes annotations for misinformation and social bots on the Sina Weibo platform. |
| Outcome: | The proposed dataset contains 65,749 social bots and 345,886 genuine accounts, annotated using a weakly supervised annotator. |