| Challenge: | a new method to support manual moderation of discussion sections is proposed. |
| Approach: | They propose to support manual moderation by proactively drawing attention of moderators to articles that most likely need their intervention. |
| Outcome: | The proposed method outperforms the current state-of-the-art methods on a 7-million-comment dataset. |
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| Challenge: | Moderation is essential for maintaining and improving the quality of online discussions. |
| Approach: | They annotate a dataset on 13 modes of discussion and use it to generate positive moderation. |
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Automatic Article Commenting: the Task and Dataset (P18-2)
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| Challenge: | Existing methods to make comments on articles are based on human-annotated subsets, but they are not suitable for online forums. |
| Approach: | They propose to use a large-scale Chinese corpus with millions of real comments and a human-annotated subset characterizing the comments’ varying quality to generalize a broad set of popular reference-based metrics. |
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BERT-Beta: A Proactive Probabilistic Approach to Text Moderation (2021.emnlp-main)
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| Challenge: | Existing approaches to text moderation are reactive and do not account for user generated content. |
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We Can Detect Your Bias: Predicting the Political Ideology of News Articles (2020.emnlp-main)
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| Challenge: | a new study examines the role of media in predicting political ideology or bias in news articles . systematic exposure to bias in the news can foster intolerance and ideological segregation . |
| Approach: | They propose an adversarial media adaptation and a specially adapted triplet loss for predicting political ideology in news articles. |
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Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations (D18-1)
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| Challenge: | Existing methods for summarizing online conversations require large amounts of training data. |
| Approach: | They propose a disjunctive model that computes the contribution of content and context separately. |
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Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing (2025.findings-acl)
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| Challenge: | Existing knowledge on how and why NLP methods make content moderation decisions is limited . authors examine how and when to use LLMs in content modeation . |
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Moderation in the Wild: Investigating User-Driven Moderation in Online Discussions (2024.eacl-long)
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| Challenge: | Effective content moderation is imperative for fostering healthy and productive discussions in online domains. |
| Approach: | They propose to document and release a dataset of comments in which users act as moderators. |
| Outcome: | The proposed dataset contains 1000 comment-reply pairs with crowdsourced annotations from a large annotator pool and fine-grained annotation schema targeting the functions of moderation, stylistic properties(aggressiveness, subjectivity, sentiment), constructiveness, and individual perspectives of the annotators on the task. |
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation (D19-1)
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| Challenge: | Existing methods for news comment generation have not been well studied. |
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The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)
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| Challenge: | Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models. |
| Approach: | They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups. |
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Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
| Approach: | They examine the progress, methods, and future directions of large language models . they examine what generative recommendation is, why RS should advance to generative recommendations . |
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