Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media (2024.findings-emnlp)
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| Challenge: | Experimental results show improvements on Reddit and Twitter data . |
| Approach: | They propose to take advantage of Large Language Models (LLMs) to better identify user communities. |
| Outcome: | The proposed model improves on Reddit and Twitter data and tasks of community detection, bot detection, and news media profiling. |
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| Challenge: | Social media bot detection has always been an arms race between advancements in machine learning and adversarial bot strategies to evade detection. |
| Approach: | They propose a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities and propose LLM-guided manipulation of user textual and structured information to evade detection. |
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A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)
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Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng
| Challenge: | Recent advances in large language models have led to an increase in synthetic content generation . the ability to detect LLMs-generated content has become of paramount importance . |
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| Challenge: | Existing methods for identifying practices within social media are not yet available. |
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LLMs Cannot (Yet) Match the Specificity and Simplicity of Online Communities in Long Form Question Answering (2024.findings-emnlp)
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| Challenge: | Recent years have positioned Large Language Models (LLMs) as powerful question answering (QA) tools, shifting users away from interacting in communities towards discourse with AI-driven conversational interfaces. |
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
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On the Generalization of Training-based ChatGPT Detection Methods (2024.findings-emnlp)
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| Challenge: | Existing studies show that training-based methods are ineffective to detect LLM generated texts from unseen tasks or topics which are not collected during training. |
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Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data (2025.coling-main)
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| Challenge: | Existing discourse parsers do not generalize well across genres and text types. |
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