Papers with online platforms
It’s going to be okay: Measuring Access to Support in Online Communities (D18-1)
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| Challenge: | Despite substantial efforts to reduce gender disparities in online social contexts, gender gaps persist and negatively affect women through online harassment. |
| Approach: | They propose a new dataset and method for identifying supportive replies and new methods for inferring gender from text and name to examine the disparity in support across millions of online interactions. |
| Outcome: | The proposed model shows that identifying as a woman is associated with higher rates of support, but also higher rates disparagement. |
Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)
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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song
| Challenge: | Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions. |
| Approach: | They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions. |
| Outcome: | The proposed model outperforms state-of-the-art methods and shows its utility. |
Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection (2022.aacl-main)
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| Challenge: | Existing approaches to detect hate speech are expensive and time-consuming . a new approach allows for flexible learning of neighborhood information . |
| Approach: | They propose a method that allows flexible modeling of neighbors retrieved from a resource-rich corpus to learn the amount of transfer. |
| Outcome: | The proposed training strategy improves on low-resource hate speech corpora over baselines. |
Enhancing Hallucination Detection via Future Context (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. |
| Approach: | They propose a framework for detection of hallucinations in black-box generators by analyzing future contexts. |
| Outcome: | The proposed framework improves on existing methods and demonstrates that it is feasible to integrate it with other models. |
PROBES : Performance and Relevance Observation for BEtter Search (2026.eacl-industry)
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| Challenge: | Qualitative search is essential for the success of online platforms, authors say . large-scale evaluation of search systems is essential to ensure high-quality user experiences . |
| Approach: | They propose a multi-task system powered by Large Language Models for end-to-end evaluation of semantic search systems. |
| Outcome: | The proposed system provides more precise and consistent relevance assessments across query categories. |
HARALD: Augmenting Hate Speech Data Sets with Real Data (2022.findings-emnlp)
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| Challenge: | Hate speech detection depends on the availability of variable labeled data. |
| Approach: | They propose a method that uses real unlabelled data from online platforms to augment existing models by harvesting and processing it. |
| Outcome: | The proposed approach improves the classification performance of hate speech classification models. |
Detecting Continuously Evolving Scam Calls under Limited Annotation: A LLM-Augmented Expert Rule Framework (2025.findings-emnlp)
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| Challenge: | Existing methods to detect scam calls rely on labeled data and assume static distribution of scam narratives. |
| Approach: | They propose a method leveraging large language models to detect continuously evolving scam calls . scammers continuously evolve their tactics, making these methods less effective . |
| Outcome: | The proposed approach is based on large language models to detect continuously evolving scam calls. |
MentSum: A Resource for Exploring Summarization of Mental Health Online Posts (2022.lrec-1)
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| Challenge: | Mental health remains a significant challenge of public health worldwide . many use online platforms to share their mental health conditions and seek help . |
| Approach: | They analyze a dataset of over 24k user posts from Reddit and 43 mental health subreddits to generate a short summarization. |
| Outcome: | The proposed dataset compared over 24k user posts and 43 mental health subreddits . it shows that the summarization of these posts is faster and more accurate than previous studies. |
Identifying Moments of Change from Longitudinal User Text (2022.acl-long)
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| Challenge: | Identifying changes in individuals’ behaviour and mood via shared content is gaining importance given the global increase in mental health disorders and the limited access to support services. |
| Approach: | They propose a task of identifying moments of change in individuals on the basis of their shared content online. |
| Outcome: | The proposed task is based on 500 manually annotated user timelines and shows that it performs best through context aware sequential modelling. |
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions (2023.emnlp-main)
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| Challenge: | Currently, only 20% of the English comments explicitly mention content moderation policies, but as few as 2% of the German and Turkish comments. |
| Approach: | They propose to use a multilingual dataset to predict stances with existing content moderation policies and to use them to explain moderation decisions. |
| Outcome: | The proposed model predicts stances and corresponding reasons with high accuracy, adding transparency to the decision-making process. |
Scientific Fact-Checking: A Survey of Resources and Approaches (2023.findings-acl)
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| Challenge: | Fact-checking is the task of assessing the veracity of factual claims based on credible evidence and background knowledge. |
| Approach: | They propose to automate scientific fact-checking using natural language processing to assess the veracity of factual claims based on credible evidence and background knowledge. |
| Outcome: | The proposed methods can help combat the spread of misinformation and help individuals understand new scientific breakthroughs. |
Explain the Flag: Contextualizing Hate Speech Beyond Censorship (2026.findings-acl)
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Jason Liartis, Eirini Kaldeli, Lamprini Gyftokosta, Eleftherios Chelioudakis, Orfeas Menis Mastromichalakis
| Challenge: | a hybrid approach to detect and explain hate speech combines large language models with vocabularies to detect hate speech in three languages . authors: the spread of hate speech online has serious personal, social, and legal consequences . eu has launched initiatives to analyze, regulate, and counteract online hate speech, authors say . |
| Approach: | They propose a hybrid approach that combines Large Language Models with vocabularies to detect hate speech in English, French, and Greek. |
| Outcome: | The proposed approach outperforms baselines in English, French, and Greek . it uses large language models and vocabularies to detect and explain hate speech . human evaluation shows that the proposed approach is accurate and clear . |
Uncovering the Root of Hate Speech: A Dataset for Identifying Hate Instigating Speech (2023.findings-emnlp)
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| Challenge: | a lack of comprehensive datasets specifically annotated for hate instigating speech hinders research . lack of reliable models for hate triggering makes it difficult to apply off-the-shelf models to the problem. |
| Approach: | They propose to use a multilingual dataset to identify hate instigating speech . lack of comprehensive datasets specifically annotated for hate instigators hinders their work . |
| Outcome: | The proposed dataset identifies hate instigating speech across languages . lack of comprehensive datasets makes it difficult to train and evaluate models . |
Offensive Content Detection via Synthetic Code-Switched Text (2022.coling-1)
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| Challenge: | Existing methods to detect offensive content in social media platforms are limited by the availability of labeled code-switched data. |
| Approach: | They propose a method for generating synthetic code-switched offensive content data using human-generated data and a keyword classification baseline. |
| Outcome: | The proposed algorithm can be used to generate synthetic code-switched offensive content data and train it on human-generated data. |
AuriSRec: Adversarial User Intention Learning in Sequential Recommendation (2024.findings-emnlp)
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| Challenge: | Existing work focuses on capturing user implicit preferences from historical interactions and matching them with the next behavior, instead of predicting user explicit intentions. |
| Approach: | They propose an adversarial user intention learning approach for sequential recommendaiton . the approach explicitly predicts user current intentions by taking historical reviews as inputs . |
| Outcome: | The proposed approach explicitly predicts user intentions by inferring their decision-making process as explained in target reviews. |
Using Automatic Speech Recognition in Spoken Corpus Curation (2020.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) is a new way to make audio-visual data accessible. |
| Approach: | They propose to use automatic speech recognition (ASR) to make audio-visual data accessible by systematic queries. |
| Outcome: | The proposed system has higher recognition scores for the north of Germany vs. lower scores for south of the country. |
A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing (2023.emnlp-main)
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| Challenge: | Existing frameworks for dialogic discourse parsing are not suitable for contentious discussions . authors propose a model for non-convergent discourse paring that does not require label collocation . |
| Approach: | They propose a multi-label scheme for contentious dialog parsing that uses multiple labels . they propose combining embeddings of the utterance, context and the labels through GRN layers . |
| Outcome: | The proposed model achieves comparable results with SOTA without label collocation and without training a unique architecture/model for each label. |
Sheep’s Skin, Wolf’s Deeds: Are LLMs Ready for Metaphorical Implicit Hate Speech? (2025.acl-long)
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| Challenge: | specialized models fail to detect implicit hate speech due to its indirectly expressed hateful intent . advanced LLMs often misinterpret metaphorical implicit hate content, resulting in its propagation . |
| Approach: | They propose a Jailbreaking strategy and Energy-based Constrained Decoding techniques to detect implicit hate speech in large language models. |
| Outcome: | The proposed model can generate metaphorical implicit hate speech, but it fails to detect it effectively. |
Can LLMs Be Efficient Predictors of Conversational Derailment? (2025.findings-emnlp)
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| Challenge: | Conversational derailment is a common issue on online platforms due to toxic or inappropriate remarks. |
| Approach: | They prompt pre-trained large language models to predict conversational derailment without fine-tuning . they compare chain-of-thought reasoning and few-shot exemplars to predict derailments . |
| Outcome: | The proposed model predicts conversational derailment without task-specific fine-tuning without fine-cuning. |
Please note that I’m just an AI: Analysis of Behavior Patterns of LLMs in (Non-)offensive Speech Identification (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are becoming a part of our everyday lives by being used as tools for information search, content creation, writing assistance, and many more. |
| Approach: | They propose to use Large Language Models to detect offensive online language in applications with social risk, such as late-life companions and online content moderators. |
| Outcome: | The proposed models fail to detect offensive language and are therefore unsuitable for use in social applications such as late-life companions and online content moderators. |
Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch (2025.acl-long)
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| Challenge: | To meet the demands of content moderation, online platforms have resorted to automated systems. |
| Approach: | They conduct an audit of Twitch’s automated moderation tool (AutoMod) to investigate its effectiveness in flagging hateful content. |
| Outcome: | The automated moderation tool (AutoMod) is used to filter hateful content on Twitch and send 107,000 comments from 4 datasets. |
Generate First, Then Sample: Enhancing Fake News Detection with LLM-Augmented Reinforced Sampling (2025.acl-long)
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| Challenge: | Existing models have a performance gap of 20% between classifying fake news and real news, making them less suitable for practical deployment. |
| Approach: | They propose to adopt an LLM to generate fake news in three different styles, which are later incorporated into the training set to augment the representation of fake news. |
| Outcome: | The proposed model achieves state-of-the-art performance on two benchmark datasets and improves detection accuracy by 24.02% and 11.06% respectively. |
MemeDetoxNet: Balancing Toxicity Reduction and Context Preservation (2025.findings-acl)
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| Challenge: | Toxic memes spread harmful and offensive content and pose a significant challenge in online environments. |
| Approach: | They propose a framework to mitigate toxicity in toxic memes by leveraging a set of pre-trained models that can interpret the visual and textual components of memes. |
| Outcome: | The proposed framework reduces toxicity on publicly available meme datasets by 10-20% compared to the previous methods. |
SOBR: A Corpus for Stylometry, Obfuscation, and Bias on Reddit (2024.lrec-main)
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| Challenge: | Existing corpora are limited in scope and can be used to collect data on author attributes. |
| Approach: | They propose to use subreddits, flairs, and self-reports as distant labels for author attributes (age, gender, nationality, personality, and political leaning) . |
| Outcome: | The proposed method could be used to infer author attributes from public posts despite their discreetness and anonymity . |