SOCIAL MEDIA

An Interactive Framework for Profiling News Media Sources (2024.naacl-long)

Challenge:	Existing work on verifiable claims detection is focused on monolingual solutions . identifying and validating claims related to global concerns requires a fact-checking pipeline capable of processing claims written in multiple languages.
Approach:	They propose an entity-aware cross-lingual claim detection model that generalizes well to handle multilingual claims.
Outcome:	The proposed model shows consistent performance gains across 27 languages and robust knowledge transfer between languages seen and unseen during training.

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Nikhil Mehta, Dan Goldwasser

Challenge:	Existing tools for detecting fake news are difficult for automated systems . e.g., we focus on the source level, and ask: Is this source factual or politically biased?
Approach:	They propose an interactive framework for news media profiling that uses graphs and pre-trained large language models to characterize social context on social media.
Outcome:	The proposed framework can detect fake and biased news media with as little as 5 human interactions . it can scale better, as often sources publish have same factuality/political bias as source .

Semantic shift in social networks (2021.starsem-1)

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Bill Noble, Asad Sayeed, Raquel Fernández, Staffan Larsson

Text Clustering

Challenge:	lexical semantic change manifests differently across different communities, according to a new study . social network analysis is a tool of sociolinguists studying variation and change .
Approach:	They use distributional methods to quantify lexical semantic change and induce a social network on communities based on interactions between members.
Outcome:	The proposed method is based on interactions between members and the community.

Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues (D19-50)

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Or Levi, Pedram Hosseini, Mona Diab, David Broniatowski

Mapping (Dis-)Information Flow about the MH17 Plane Crash (D19-50)

Challenge:	a blurry line between fake news and protected-speech satire has been a struggle for social media platforms . purveyors of fake news have begun to masquerade as satirical sites to avoid being demoted .
Approach:	They propose to automatically classify fake news versus satire based on language differences . they hypothesize that nuances could be identified using semantic and linguistic cues .
Outcome:	The proposed method can identify nuances between fake news and satire based on language differences . the proposed method is compared to the language-based baseline and is highly scalable .

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Mareike Hartmann, Yevgeniy Golovchenko, Isabelle Augenstein

PiKGL: Leveraging Pruned Knowledge Graphs for Explainable Stance Detection (2026.tacl-1)

Challenge:	Digital media enables fast sharing of information, but also disinformation . studies on the spread of disinformation on social media focused on small, manually annotated datasets or used proxys for data annotation.
Approach:	They propose to use text classifiers to label Twitter content related to the MH17 crash to improve annotation accuracy.
Outcome:	The proposed classifier improves over a hashtag-based baseline, but still remains a challenge in labelling pro-Russian and pro-Ukrainian content with high precision.

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Bingbing Wang, Jingjie Lin, Zhixin Bai, Xintong Song, Qianlong Wang, Min Yang, Xi Zeng, Jing Li, Ruifeng Xu

Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)

Challenge:	Experimental results demonstrate that a Pruned interpretable knowledge Graph Learning framework for explainable stance detection is state-of-the-art for social media stance prediction.
Approach:	They propose a Pruned interpretable knowledge Graph Learning framework for explainable stance detection that incorporates commonsense knowledge and prunes redundant information to ensure precision and minimize noise.
Outcome:	The proposed framework achieves state-of-the-art on three public datasets.

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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang

MUDES: Multilingual Detection of Offensive Spans (2021.naacl-demos)

Challenge:	Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations.
Approach:	They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation.
Outcome:	The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement.

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Tharindu Ranasinghe, Marcos Zampieri

A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks (2021.naacl-main)

Challenge:	Identifying offensive spans in texts is the goal of the SemEval-2021 Task 5: Toxic Spans Detection . previous work focused on post level annotations, but identifying offensive span is useful in many ways.
Approach:	They propose a Python-based system to detect offensive spans in texts with pre-trained models and a user-friendly web-based interface.
Outcome:	The proposed system is based on a Python-based framework and a user-friendly web-based interface.

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Amir Hadifar, Sofie Labat, Veronique Hoste, Chris Develder, Thomas Demeester

Hate Speech and Offensive Language Detection in Bengali (2022.aacl-main)

Challenge:	In domain-specific customer service applications, many companies struggle to deploy advanced NLP models due to the limited availability of and noise in their datasets.
Approach:	They analyze customer service conversations on a multilingual social media corpus and compare different approaches to pretraining and finetuning on different end tasks.
Outcome:	The proposed model improves performance on multilingual social media data, especially in non-English settings.

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Mithun Das, Somnath Banerjee, Punyajoy Saha, Animesh Mukherjee

Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data (P19-2)

Challenge:	Existing research on hate speech detection in English does not cover low-resource languages like Bengali.
Approach:	They develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets.
Outcome:	The proposed model outperforms other models on training actual and romanized datasets by interpreting the semantic expressions better.

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Vamshi Krishna Srirangam, Appidi Abhinav Reddy, Vinay Singh, Manish Shrivastava

Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021.emnlp-main)

Challenge:	Named Entity Recognition (NER) is a subtask of Information Extraction in NLP.
Approach:	They present a Telugu-English code-mixed corpus with the corresponding named entity tags.
Outcome:	The proposed model scored 0.96, 0.94 and 0.95 on a Telugu-English code-mixed corpus.

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Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, Diyi Yang

A Case Study of Analysis of Construals in Language on Social Media Surrounding a Crisis Event (2021.acl-srw)

Challenge:	Existing studies on explicit or overt hate speech have failed to address a more pervasive form based on coded or indirect language.
Approach:	They propose a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication.
Outcome:	The proposed dataset will serve as a useful benchmark for understanding this multifaceted issue.

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Lolo Aboufoul, Khyati Mahajan, Tiffany Gallicano, Sara Levens, Samira Shaikh

Psycholinguistics

Challenge:	construal level theory (CLT) uses concreteness as covariate to analyze language around political import events.
Approach:	They propose to include psycholinguistic measures of concreteness as covariates in topic models to analyze the language around an event of political import.
Outcome:	The proposed model incorporates measures of concreteness as covariates to inform the analysis of language around the 2017 rally.

Can Community Notes Replace Professional Fact-Checkers? (2025.acl-short)

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Nadav Borenstein, Greta Warren, Desmond Elliott, Isabelle Augenstein

MIND: A Multi-agent Framework for Zero-shot Harmful Meme Detection (2025.acl-long)

Challenge:	Fact-checkers are crucial in combating misinformation on social media . however, community moderation is often employed in parallel due to the scale of misleading content shared online.
Approach:	They use language models to annotate Twitter/X community notes with attributes such as topic, cited sources, and whether they refute misinformation claims.
Outcome:	The results show that community notes cite fact-checking sources up to five times more than previously reported.

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Ziyan Liu, Chunxiao Fan, Haoran Lou, Yuexin Wu, Kaiwei Deng

The workweek is the best time to start a family – A Study of GPT-2 Based Claim Generation (2020.findings-emnlp)

Challenge:	a rapid expansion of memes on social media highlights the need for effective methods to detect harmful content.
Approach:	They propose a multi-agent framework for zero-shot harmful meme detection that does not rely on annotated data.
Outcome:	The proposed framework outperforms existing zero-shot approaches on three meme datasets.

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Shai Gretz, Yonatan Bilu, Edo Cohen-Karlik, Noam Slonim

Leakage-Aware User-Level ADHD Signal Classification from Social Media: When Graph Aggregation Helps, and When It Does Not (2026.acl-srw)

Challenge:	Argument generation is a challenging task whose impact on social media is growing . we examine how argument generation can be enhanced to provide better arguments .
Approach:	They propose a pipeline for argument generation based on GPT-2 . they examine the types of claims it produces, and their veracity .
Outcome:	The proposed pipeline improves argument generation quality and provides a clear stance on a debate topic.

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Daniel Cieślak, Władysław Średniawa

GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020.acl-main)

Challenge:	Social media data are longitudinal, usercentered, rich in spontaneous language use.
Approach:	They propose a leakage-aware evaluation framework organized around two controlled axes: evidence budget and leakage control.
Outcome:	The proposed framework compares graph aggregation with other models using psycholinguistic features and semantic tweet embeddings.

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Yi-Ju Lu, Cheng-Te Li

Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection (2020.acl-main)

Challenge:	Existing methods to detect fake news on social media are based on textual features and advanced linguistic features.
Approach:	They propose a neural network-based model to detect fake news on social media . they use a short-text tweet and a sequence of retweets without text comments to predict whether the source tweet is fake or not.
Outcome:	The proposed model outperforms state-of-the-art methods by 16% on real tweet datasets and produces reasonable explanations.

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Lei Zhong, Juan Cao, Qiang Sheng, Junbo Guo, Ziang Wang

Structured Data in NLP

Challenge:	Existing methods for identifying controversial posts on social media are limited . existing methods fail to incorporate semantic information from content-related posts .
Approach:	They propose a method to integrate the information from topics, posts, and comments . they extend their model to Disentangled TPC-GCN to disentangle topic-related features .
Outcome:	The proposed method outperforms existing methods on two real-world datasets.

Content Moderation for Evolving Policies using Binary Question Answering (2023.acl-industry)

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Sankha Subhra Mullick, Mohan Bhambhani, Suhit Sinha, Akshat Mathur, Somya Gupta, Jidnya Shah

Language Models

Challenge:	Social media platforms use content moderation to safeguard users from abuse, harassment, malicious attacks, spam, etc.
Approach:	They propose to model content moderation as a binary question answering problem where questions validate loosely coupled themes constituting a policy.
Outcome:	The proposed model improves recall at 95% precision on two proprietary datasets of social media posts and comments respectively annotated under curated Hate Speech and Commercial Spam policies.

RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media (2023.findings-eacl)

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Somin Wadhwa, Vivek Khetan, Silvio Amir, Byron Wallace

Text-Transport: Toward Learning Causal Effects of Natural Language (2023.emnlp-main)

Challenge:	Social media platforms such as Reddit are vulnerable to misinformation and disinformation.
Approach:	They propose a method to automatically derive (noisy) supervision for retrieval of trustworthy evidence relevant to a given claim made on social media.
Outcome:	The proposed method outperforms baseline models in the retrieval task performed by medical doctors.

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Victoria Lin, Louis-Philippe Morency, Eli Ben-Michael

Challenge:	Existing methods for causal inference require strong assumptions about the data, meaning the data from which one can estimate valid causal effects is not representative of the actual target domain of interest.
Approach:	They propose a method for estimation of causal effects from natural language under any text distribution using the notion of distribution shift.
Outcome:	The proposed method can be used to estimate causal effects from natural language under any text distribution.

“Why do I feel offended?” - Korean Dataset for Offensive Language Identification (2023.findings-eacl)

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San-Hee Park, Kang-Min Kim, O-Joun Lee, Youjin Kang, Jaewon Lee, Su-Min Lee, SangKeun Lee

Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media (2022.naacl-main)

Challenge:	Existing methods for detecting offensive content rely on labeled datasets, but few consider low-resource languages with relatively less data available for training.
Approach:	They propose to use Korean as a dataset for offensive language identification . they propose to perform abusive language detection and sentiment analysis to help identify offensive languages.
Outcome:	The proposed datasets improve the performance of offensive language identification in Korean, while the existing methods are limited.

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Lixing Zhu, Zheng Fang, Gabriele Pergola, Robert Procter, Yulan He

Lexical Relation Mining in Neural Word Embeddings (2020.coling-main)

Challenge:	Existing approaches to detect vaccine attitudes on social media require abundant annotations and pre-defined aspect categories.
Approach:	They propose a semi-supervised approach to detect vaccine attitudes on social media . they use an autoencoding architecture to learn from unlabelled data the topical information of the domain .
Outcome:	The proposed model outperforms existing aspect-based models on stance detection and tweet clustering.

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Aishwarya Jadhav, Yifat Amir, Zachary Pardos

Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings (2025.acl-long)

Challenge:	Conventionally, lexical relations in word vector space have been defined by collections of relatively consistent relationships, or vector offsets, between word-pairs.
Approach:	They propose to use Word2Vec space of word-pairs to find lexical relations . they also demonstrate a method for approximating the presence of syntactic and semantic relations based on word vectors extracted from word embeddings.
Outcome:	The proposed method outperforms other validated methods in the presence of noisy offsets.

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Hans William Alexander Hanley, Zakir Durumeric

The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions (D18-1)

Challenge:	Contextual large language model embeddings are often monolingual, do not scale, and struggle in multilingual settings.
Approach:	They propose a hierarchical approach to embed news articles and social media data using Matryoshka embeddings that can determine story similarity at varying levels of granularity based on which subset of dimensions is examined.
Outcome:	The proposed model achieves state-of-the-art performance on the SemEval 2022 task 8 dataset.

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Salvatore Giorgi, Daniel Preoţiuc-Pietro, Anneke Buffone, Daniel Rieman, Lyle Ungar, H. Andrew Schwartz

Challenge:	Social media data is often aggregated without regard to users in the Twitter populations of each community.
Approach:	They propose to use Twitter language to build community-level models using Twitter language aggregated by users.
Outcome:	The proposed method improves on four county-level tasks spanning demographic, health, and psychological outcomes over the standard approach of aggregating all tweets.

TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020.findings-emnlp)

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Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa Anke, Leonardo Neves

TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering (2026.findings-eacl)

Challenge:	Modern NLP systems are typically ill-equipped when applied to noisy user-generated text.
Approach:	They propose a new evaluation framework consisting of seven Twitter-specific classification tasks.
Outcome:	The proposed framework is based on seven heterogeneous Twitter-specific classification tasks.

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Mohammadamin Shafiei, Hamidreza Saffari, Mohammad Taher Pilehvar, Alessandro Raganato

Question Answering

Challenge:	Large Language Models (LLMs) are increasingly used to answer factual, information-seeking questions (ISQs).
Approach:	They propose to use a dataset to evaluate large language models to generate human-like text on ISQs in two languages, English and Farsi, and then use it to evaluate nine LLMs.
Outcome:	The proposed dataset shows that accuracy drops by 25% when models encounter misleading yet factual hints.

Mask-to-Correct+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction (2026.acl-long)

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Payel Santra, Lavisha Sharma, Madhusudan Ghosh, Partha Basuchowdhuri

An Annotated Corpus for Sexism Detection in French Tweets (2020.lrec-1)

Challenge:	Existing methods for fact correction ignore semantic faithfulness in their process.
Approach:	They propose a supervised learning approach that uses a diversity-aware masking approach to identify erroneous spans of claims and evaluate the faithfulness of corrections using retrieved evidence.
Outcome:	The proposed framework outperforms baseline frameworks on social media datasets, achieving up to 14% improvement in SARI scores, without using gold evidence.

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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully

Lifelong Learning of Hate Speech Classification on Social Media (2021.naacl-main)

Challenge:	Social media networks allow users to share opinions and sentiments, which can cause a large spreading of hatred or abusive messages.
Approach:	They propose to annotate 12,000 tweets with a sexism detection scheme in France . they propose to use deep learning to detect if a message with sexist content is really s.
Outcome:	The proposed scheme detects sexist content and identifies if it is really sexism . the proposed scheme is the first of its kind in the u.s.

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Jing Qian, Hong Wang, Mai ElSherief, Xifeng Yan

Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social Media (2025.findings-naacl)

Challenge:	Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined.
Approach:	They propose to use Variational Representation Learning and a load-balancing self-organizing inductive neural network to learn hate speech classification on social media.
Outcome:	The proposed model improves on the lifelong learning techniques on social media.

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Owen Cook, Charlie Grimshaw, Ben Peng Wu, Sophie Dillon, Jack Hicks, Luke Jones, Thomas Smith, Matyas Szert, Xingyi Song

BAN-PL: A Polish Dataset of Banned Harmful and Offensive Content from Wykop.pl Web Service (2024.lrec-main)

Challenge:	Misinformation spreads rapidly on social media, confusing the truth and targeting potentially vulnerable people.
Approach:	They propose to use inter- and intra-annotator agreement to understand the reliability of each annotator and influence the training of large language models based on annotators reliability.
Outcome:	The proposed framework utilises inter- and intra-annotator agreement to understand the reliability of each annotator and influence the training of large language models based on annotators reliability.

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Anna Kolos, Inez Okulska, Kinga Głąbińska, Agnieszka Karlinska, Emilia Wisnios, Paweł Ellerik, Andrzej Prałat

Lexical Normalization for Code-switched Data and its Effect on POS Tagging (2021.eacl-main)

Challenge:	a new dataset of offensive social media content for the Polish language is presented to address this gap . access to accurate and non-synthetic datasets of social media is limited for low-resource languages .
Approach:	They present a new open dataset of offensive social media content for the Polish language . authors propose to make the dataset publicly available to improve access .
Outcome:	The proposed dataset includes 691,662 posts and comments from the Polish Reddit . the authors describe the dataset and apply it to real-life content moderation processes .

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Rob van der Goot, Özlem Çetinoğlu

Syntactic Text Processing

Challenge:	Social media data can be used to improve natural language processing performance, but it is often overlooked by lexical normalization systems.
Approach:	They propose three lexical normalization models specifically designed to handle code-switched data and evaluate their performance on POS tags.
Outcome:	The proposed models outperform monolingual models and lead to 5.4% performance increase for POS tagging compared to unnormalized input.

SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations (2026.findings-eacl)

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Ifeoluwa Wuraola, Daniel Marciniak, Nina Dethlefs

Natural Language Interfaces

Challenge:	Standard NLP benchmarks often miss subtle, culturally-specific cues in social media . incorporating structured cultural knowledge into the retrieval process improves accuracy by up to 31% .
Approach:	They propose a retrieval-augmented framework that integrates a culture-specific slang knowledge graph into large language models via one-shot prompting.
Outcome:	The proposed framework outperforms traditional and unstructured retrieval methods in slang-based models by 31% and 28%.

Towards Intelligent Clinically-Informed Language Analyses of People with Bipolar Disorder and Schizophrenia (2022.findings-emnlp)

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Ankit Aich, Avery Quynh, Varsha Badal, Amy Pinkham, Philip Harvey, Colin Depp, Natalie Parde

AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.acl-long)

Challenge:	Existing studies on social media data have limited the extent to which they can produce meaningful or generalizable conclusions.
Approach:	They propose to use transcribed conversations with people with bipolar disorder and schizophrenia to create a large dataset of transcriptions.
Outcome:	The proposed dataset extracts 100+ temporal, sentiment, psycholinguistic, emotion, and lexical features and establishes classification validity.

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Zixin Chen, Hongzhan Lin, Kaixin Li, Ziyang Luo, Zhen Ye, Guang Chen, Zhiyong Huang, Jing Ma

Generalizable Sarcasm Detection is Just Around the Corner, of Course! (2024.naacl-long)

Challenge:	Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy.
Approach:	They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a .
Outcome:	The proposed framework systematically reveals the performance of different target mLLMs.

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Hyewon Jang, Diego Frassinelli

IndiSocialFT: Multilingual Word Representation for Indian languages in code-mixed environment (2023.findings-emnlp)

Challenge:	sarcasm can be used to hurt, criticize, or deride but also to be mocking, humorous, or to bond.
Approach:	They tested the robustness of sarcasm detection models by fine-tuning their behavior on four sarkasmatic datasets . they found that models performed better when fine- tuned with third-party labels than with author labels.
Outcome:	The proposed models performed better when fine-tuned with third-party labels than with author labels on the same dataset and across different datasets.

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Saurabh Kumar, Ranbir Sanasam, Sukumar Nandi

Representation Learning

Challenge:	Existing studies on word embeddings for Indian languages focus on monolingual corpora with limited reach to social media setups.
Approach:	They propose a generalized representation vector for diverse text characteristics . they use a FastText model to gather text from social media and well-formed sources .
Outcome:	The proposed representation vector surpasses baselines in most cases and languages, demonstrating suitability for various NLP applications.

Chain-of-Thought Embeddings for Stance Detection on Social Media (2023.findings-emnlp)

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Joseph Gatto, Omar Sharif, Sarah Preum

Predicting Stances from Social Media Posts using Factorization Machines (C18-1)

Challenge:	Stance detection on social media platforms like Twitter is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit stance labels.
Approach:	They propose to embed COT reasonings into a traditional RoBERTa-based stance detection pipeline by embedding COT stance reasonings and integrating them into slang-based models.
Outcome:	The proposed model achieves SOTA performance on multiple stance detection datasets collected from social media.

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Akira Sasaki, Kazuaki Hanawa, Naoaki Okazaki, Kentaro Inui

Challenge:	Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world.
Approach:	They propose to use factorization machines to model user preferences toward topics from social media data to predict whether a given text/user is in favor (agree), against (disagreer), or neutral toward a target topic.
Outcome:	The proposed method can predict stances of silent users based on their stance toward other topics and the social media posts of the user.

What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context (2020.acl-main)

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Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, Preslav Nakov

Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination (2025.findings-naacl)

Challenge:	a growing number of fake news reports are published online, causing a trust crisis . a new study aims to predict political bias and factuality of reporting of entire news outlets .
Approach:	They propose to profile entire news outlets and look for those that are likely to publish fake content . they also examine what was written about the target medium and who reads it .
Outcome:	The proposed method improves on the current state-of-the-art in analyzing social media and what was written about the target medium.

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Young Min Cho, Dandan Pang, Stuti Thapa, Garrick Sherman, Lyle Ungar, Louis Tay, Sharath Chandra Guntuku

That is a Known Lie: Detecting Previously Fact-Checked Claims (2020.acl-main)

Challenge:	valence and arousal are functionally equivalent across social media platforms . americans display higher emotional intensity than Chinese users .
Approach:	They compare valence and arousal on Twitter/X and Sina Weibo in China . they use the NRC-VAD lexicon to measure valance and valency .
Outcome:	The results show that the valence and arousal of the two platforms differ across cultures . the analysis also shows that the US users display higher emotional intensity than Chinese users .

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Shaden Shaar, Nikolay Babulkov, Giovanni Da San Martino, Preslav Nakov

Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event (2021.findings-emnlp)

Challenge:	a large number of fact-checked claims have been accumulated over the years . despite the importance of fact checking, it has been largely ignored by the research community .
Approach:	They propose to automate fact-checking by focusing on claims that have already been fact-tested . they propose to use specialized datasets to compare different methods .
Outcome:	The proposed task shows that it improves over state-of-the-art methods.

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Sarthak Khanal, Doina Caragea

Tracking Life’s Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis (2025.acl-long)

Challenge:	Social media is a platform for people to share their concerns and report information as eyewitnesses of events.
Approach:	They propose a multi-task learning approach to leverage available annotated data for several related tasks from the crisis domain to improve performance on a main task with limited annotation.
Outcome:	The proposed approach improves performance on a task with limited annotated data.

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Minghao Lv, Siyuan Chen, Haoan Jin, Minghao Yuan, Qianqian Ju, Yujia Peng, Kenny Q. Zhu, Mengyue Wu

Investigating Controversy Framing across Topics on Social Media (2025.findings-emnlp)

Challenge:	Existing studies have indicated that major life events can greatly impact individuals’ mental health, but shedding its light on social media data is challenging due to the complexity and ambiguity nature of life events.
Approach:	They propose to extract life events mentioned in posts on social media to uncover a social media event dataset which includes 12 major life event categories that are likely to occur in everyday life.
Outcome:	The proposed dataset includes 12 life event categories that are likely to occur in everyday life and is human-annotated under iterative procedure and boasts a high level of quality.

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Maxwell Weinzierl, Sanda M. Harabagiu

Challenge:	a novel method for discovering framings of controversial problems is proposed . framers of controversial issues can be explored across topics, the paper argues .
Approach:	This paper proposes a method for discovering and articulating framing of controversial problems . framers offer valuable insights into how and why controversial problems are discussed online .
Outcome:	The proposed method enables the investigation of how controversy is framed across topics.

STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval (2020.findings-emnlp)

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Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier

He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist (2020.acl-main)

Challenge:	a new news dataset targets both stance detection (SD) and fine-grained evidence retrieval (ER) . stance Detection (SD), which is a form of multitask learning, has gained increasing interest in recent work .
Approach:	They propose a news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER) their dataset is an expert-annotated news dataset with 3,291 articles.
Outcome:	The proposed dataset is a high-quality benchmark for future research in stance detection and evidence retrieval.

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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully

Ethical NLP

Challenge:	Sexism is prejudice or discrimination based on a person's gender.
Approach:	They propose to use a French dataset annotated for sexism detection to characterize sexist content and to train deep learning experiments on tweets.
Outcome:	The proposed dataset is the first to be used for sexism detection in France and constitutes a first step towards offensive content moderation.

Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation (2023.findings-emnlp)

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Zhen Yu, Zhou Qin, Zhenhua Chen, Meihui Lian, Haojun Fu, Weigao Wen, Hui Xue, Kun He

Tackling Social Bias against the Poor: a Dataset and a Taxonomy on Aporophobia (2025.findings-naacl)

Challenge:	Existing adversarial attacks using imperceptible perturbations are challenging to simulate . e-commerce product restrictions and hate speech monitoring are examples of such attacks .
Approach:	They propose a black-box adversarial attack that leverages sparse perturbations to simulate adversarials exhibited by illegal merchants in the black- box scenario.
Outcome:	The proposed method outperforms existing attacks and unimodal attacks by treating images and text in discrete space and outperforming existing models.

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Georgina Curto, Svetlana Kiritchenko, Muhammad Hammad Fahim Siddiqui, Isar Nejadgholi, Kathleen C. Fraser

Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient’s Perspective (2022.lrec-1)

Challenge:	Poverty is a multidimensional phenomenon that affects 712 million people worldwide .
Approach:	They propose to annotate a corpus of English tweets from five world regions for the presence of harmful beliefs and discriminative actions against poor people on social media.
Outcome:	The proposed model can be used to identify, track and mitigat aporophobia on social media at scale.

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Lisa Raithel, Philippe Thomas, Roland Roller, Oliver Sapina, Sebastian Möller, Pierre Zweigenbaum

Native Language Identification with User Generated Content (D18-1)

Challenge:	a recent study shows that the class labels of german documents containing ADRs are imbalanced . clinical trials and physicians prescribing medications cannot cover every potential use case.
Approach:	They propose to use binary annotated documents from a german patient forum to detect ADRs.
Outcome:	The proposed model achieves an F1 score of 37.52 for the positive class on the German patient forum.

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Gili Goldin, Ella Rabinovich, Shuly Wintner

Is this chart lying to me? Automating the detection of misleading visualizations (2026.acl-long)

Challenge:	Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task.
Approach:	They propose to use linguistically-motivated features and social media characteristics to obtain high accuracy on this task.
Outcome:	The proposed method is highly accurate on a social media content where authors are highly-fluent nonnative speakers.

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Jonathan Tonglet, Jan Zimny, Tinne Tuytelaars, Iryna Gurevych

The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse (2022.lrec-1)

Challenge:	Prior work has shown that both humans and MLLMs are frequently deceived by misleading visualizations.
Approach:	They propose a benchmark of 2,604 real-world visualizations annotated with 12 types of misleaders.
Outcome:	The proposed framework can detect misleading visualizations and identify specific design rules they violate . the proposed framework is based on a synthetic dataset of 81,814 visualizations .

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Ritesh Kumar, Shyam Ratan, Siddharth Singh, Enakshi Nandi, Laishram Niranjana Devi, Akash Bhagat, Yogesh Dawer, Bornini Lahiri, Akanksha Bansal, Atul Kr. Ojha

Give me your Intentions, I’ll Predict our Actions: A Two-level Classification of Speech Acts for Crisis Management in Social Media (2022.lrec-1)

Challenge:	59,152 comments are annotated with a hierarchical, fine-grained taget marking aggression and bias of various kinds on social media platforms.
Approach:	They propose to annotate a multilingual dataset with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur.
Outcome:	The proposed dataset contains 59,152 comments in four languages, mostly code-mixed with English.

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Enzo Laurenti, Nils Bourgon, Farah Benamara, Alda Mari, Véronique Moriceau, Camille Courgeon

Words are the Window to the Soul: Language-based User Representations for Fake News Detection (2020.coling-main)

Challenge:	Using social networks, social media is a vital tool for emergency management and social media has been used to generate valuable information in crisis situations.
Approach:	They propose to measure for the first time the role of SA on urgency detection in tweets . they propose to use a two-layer annotation scheme to annotate tweets for both SA and urgency .
Outcome:	The proposed scheme combines two-layer annotation scheme and deep learning experiments to detect SA in a crisis corpus.

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Marco Del Tredici, Raquel Fernández

Automatically Identifying Complaints in Social Media (P19-1)

Challenge:	Existing studies on fake news classification focus on textual content, but also social context in which news are consumed.
Approach:	They propose a model that creates representations of individuals on social media based only on the language they produce and uses them to detect fake news.
Outcome:	The proposed model exploits the relationship between language use and connections in the social graph to assess the presence of the Echo Chamber effect in the data.

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Daniel Preoţiuc-Pietro, Mihaela Gaman, Nikolaos Aletras

Speech & Audio in NLP

Challenge:	Complaining is a basic speech act used to express a negative mismatch between reality and expectations in a particular situation.
Approach:	They present a systematic analysis of complaints in computational linguistics . they collect annotated data set of written complaints expressed on Twitter .
Outcome:	The proposed model achieves predictive performance of up to 79 F1 using distant supervision.

A Spelling Correction Corpus for Multiple Arabic Dialects (2020.lrec-1)

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Fadhl Eryani, Nizar Habash, Houda Bouamor, Salam Khalifa

Syntactic Text Processing

Challenge:	Arabic dialects are non-standard varieties of Arabic commonly spoken across the Arab world, but lack standard orthographies.
Approach:	They present a corpus of 10,000 sentences from five Arabic city dialects represented in the Conventional Orthography for Dialectal Arabic (CODA) they use a bootstrapping technique to speed up annotation and compare similarity between dialects before and after CODA annotation.
Outcome:	The proposed method speeds up the annotation process and shows similarity between the dialects before and after CODA annotation.

Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models (2024.emnlp-main)

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Layla Bouzoubaa, Elham Aghakhani, Rezvaneh Rezapour

Ethical NLP

Challenge:	Only 7% of people living with an SUD receive any form of treatment, with stigma reported as a major barrier.
Approach:	They propose a computational framework for analyzing stigma and de-stigmatizing online content and delving into the linguistic features that propagate stigma towards PWUS.
Outcome:	The proposed model transforms stigmatizing language into more empathetic language and analyzes over 1.2 million posts on social media .

Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (2020.coling-main)

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Arun Ramachandran, Gerard de Melo

Representation Learning

Challenge:	Emotion lexicons provide information about associations between words and emotions.
Approach:	They use crowdsourcing to annotate words with Plutchik's 8 basic emotions, providing binary labels.
Outcome:	The proposed lexicons provide information about associations between words and emotions . the lexiconics are useful in emotional analyses of reviews, literary texts, and posts on social media .

Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)

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Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang

Content Fuzzing for Escaping Information Cocoons on Digital Social Media (2026.findings-acl)

Challenge:	Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates.
Approach:	They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals.
Outcome:	The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average.

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Yifeng He, Ziye Tang, Hao Chen

Multimodal Sarcasm Target Identification in Tweets (2022.acl-long)

Challenge:	Information cocoons restrict users’ exposure to posts with diverse viewpoints . social media platforms restrict the range of viewpoints that users encounter .
Approach:	They propose a confidence-guided fuzzing framework that rewrites posts while preserving their human-interpreted intent and induces different machine-inferred stance labels.
Outcome:	The proposed framework rewrites posts while preserving human-interpreted intent and induces different machine-inferred stance labels while maintaining semantic integrity with respect to the original content.

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Jiquan Wang, Lin Sun, Yi Liu, Meizhi Shao, Zengwei Zheng

HateCheckHIn: Evaluating Hindi Hate Speech Detection Models (2022.lrec-1)

Challenge:	Existing methods to detect sarcasm target with text lacking context are not sufficient and complete.
Approach:	They propose a multi-modal sarcasm target identification task that performs both textual and visual detection.
Outcome:	The proposed model can perform textual target labeling and visual target detection.

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Mithun Das, Punyajoy Saha, Binny Mathew, Animesh Mukherjee

BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)

Challenge:	Hate speech detection models are evaluated on a held-out test data, but they are incapable of identifying weaknesses.
Approach:	They propose to use multilingual hate speech detection models to evaluate their performance on social media conversation.
Outcome:	The proposed model can detect hate speech in multiple languages using a real-world conversation on social media.

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Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo

Using Human Attention to Extract Keyphrase from Microblog Post (P19-1)

Challenge:	Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution.
Approach:	They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process.
Outcome:	The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work.

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Yingyi Zhang, Chengzhi Zhang

Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline (2022.coling-1)

Challenge:	Existing studies on keyphrase extraction neglect human reading behavior during keyphrase annotating.
Approach:	They propose to integrate human attention into keyphrase extraction models by an attention mechanism and combine it with neural network models.
Outcome:	The proposed models improve on two Twitter datasets.

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Feng Ge, Weizhao Li, Haopeng Ren, Yi Cai

Examining Temporalities on Stance Detection towards COVID-19 Vaccination (2024.lrec-main)

Challenge:	Sentiment analysis in social media is challenging because of the lack of context.
Approach:	They propose to use stickers to perform a multimodal sentiment analysis task using Chinese stickers.
Outcome:	The proposed model performs best compared with other models.

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Yida Mu, Mali Jin, Kalina Bontcheva, Xingyi Song

Automatically Discovering How Misogyny is Framed on Social Media (2025.naacl-long)

Challenge:	Existing studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus.
Approach:	They evaluate a range of transformer-based models using chronological and random splits of social media data to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination.
Outcome:	The proposed models show that the models performed better with chronological and random splits than with random split models.

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Rakshitha Rao Ailneni, Sanda M. Harabagiu

Challenge:	a study on misogyny on social media reveals that there are many forms of misogamy, each addressing another MisogYny Problem (MP) the detection of the way misogony is framed is important for identifying misos .
Approach:	This paper considers the automatic discovery of misogyny problems and their frames through the Dis-MP&F method . it proposes a data-driven, rich Taxonomy of MisogYny (ToM) method that can be used to generate a misomy benchmark dataset.
Outcome:	The proposed method can generate a data-driven, rich Taxonomy of misogyny (ToM) and produces promising results on a misomyne benchmark dataset.

MMChat: Multi-Modal Chat Dataset on Social Media (2022.lrec-1)

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Yinhe Zheng, Guanyi Chen, Xin Liu, Jian Sun

Dialogue Systems & Conversational Agents

Challenge:	Incorporating multi-modal contexts in conversation is important for developing engaging dialogue systems.
Approach:	They propose a large scale Chinese multi-modal dialogue corpus that contains image-grounded dialogues from real conversations on social media.
Outcome:	The proposed model can handle sparsity issues in dialogue generation tasks by incorporating image features.

Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development (2024.findings-acl)

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Pranab Sahoo, Ayush Singh, Sriparna Saha, Aman Chadha, Samrat Mondal

CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts (2022.lrec-1)

Challenge:	ADEs are a serious public health concern and cost healthcare systems billions of dollars . despite advancements in healthcare, ADE detection remains a significant challenge .
Approach:	They propose a multimodal adverse drug event detection dataset that merges ADE-related textual information with visual aids to enhance patient safety.
Outcome:	The proposed dataset integrates ADE-related textual information with visual aids to improve patient safety and healthcare accessibility.

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Muskan Garg, Chandni Saxena, Sriparna Saha, Veena Krishnan, Ruchi Joshi, Vijay Mago

When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text (2025.findings-acl)

Challenge:	Social media platforms are important resources for investigating mental health of users.
Approach:	They propose a new dataset for Causal Analysis of Mental health in Social media posts (CAMS) they crawl and annotate 3155 Reddit data and reannotate a publicly available SDCNL dataset .
Outcome:	The proposed model outperforms existing models on 3155 Reddit posts and 1896 instances of the dataset.

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Hillary Dawkins, Kathleen C. Fraser, Svetlana Kiritchenko

Language Models

Challenge:	detecting AI-generated text on social media is difficult due to short text length and informal language of the internet . a recent study shows that detection of AI-generated posts is difficult under assumptions that an attacker has no knowledge of the generating model .
Approach:	They use open-source, closed-source and fine-tuned social media to detect AI-generated text . they use assumptions about knowledge of and access to the generating models to test detection .
Outcome:	a human study shows that detection of AI-generated social media posts is difficult . the study compared 505,159 posts from open-source, closed-source and fine-tuned models .

Afaan Oromo Hate Speech Detection and Classification on Social Media (2022.lrec-1)

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Teshome Mulugeta Ababu, Michael Melese Woldeyohannis

ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media (2025.coling-main)

Challenge:	Hate and offensive speech on social media is a global problem that suffers the community especially, for an under-resourced language like Afaan Oromo.
Approach:	They develop a model to detect and classify Afaan Oromo hate speech on social media using different machine learning algorithms.
Outcome:	The proposed model outperforms existing models in gender, religion, race, and offensive speech on social media.

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Kung-Hsiang Huang, Hou Pong Chan, Kathleen McKeown, Heng Ji

Challenge:	Existing studies have focused on the identification of social media posts that contain misrepresentations of information within associated news articles.
Approach:	They propose a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles.
Outcome:	The proposed model outperforms large language models on the ManiTweet dataset and reveals intriguing connections between manipulation and the domain and factuality of news articles.

Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show (2022.lrec-1)

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Anietie Andy, Reno Kriz, Sharath Chandra Guntuku, Derry Tanti Wijaya, Chris Callison-Burch

The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech (2025.emnlp-main)

Challenge:	Prior work identified and summarized scenes associated with a TV show by selecting a few representative social media posts (5 posts) that were published during the timeline of the scenes.
Approach:	They propose a method to predict social media posts associated with a TV show from those that are not-indicative.
Outcome:	The proposed method can predict posts indicative of what happened in a scene from those that are not-indicative based on high AUC's on social media posts associated with a popular TV show .

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Naama Rivlin-Angert, Guy Mor-Lan

DisorBERT: A Double Domain Adaptation Model for Detecting Signs of Mental Disorders in Social Media (2023.acl-long)

Challenge:	a new computational model for political delegitimization discourse is proposed for analysis of democratic discourse . we identify the importance of PDD as a powerful tool in political competition .
Approach:	They propose a computational classification pipeline for political delegitimization discourse . they annotate a Hebrew-language corpus of 10,410 sentences from parliamentary speeches, facebook posts and leading news outlets .
Outcome:	The proposed model achieves an F1 of 0.74 for binary detection and a macro-F1 of 0.6 for classification of delegitimization characteristics.

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Mario Aragon, Adrian Pastor Lopez Monroy, Luis Gonzalez, David E. Losada, Manuel Montes

Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection (2024.lrec-main)

Challenge:	Mental disorders affect millions of people worldwide and cause interference with their thinking and behavior.
Approach:	They propose to adapt a social media-based mental health model to automatically analyze social media content to detect signs of mental disorders.
Outcome:	The proposed model improves classification performance and competitiveness against state-of-the-art methods.

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Ya Gao, Shaoxiong Ji, Pekka Marttinen

PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events (2025.emnlp-main)

Challenge:	Recent studies have used word embedding and deep learning to automate ADE detection from text, but they did not incorporate explicit medical knowledge about drugs and adverse reactions or the corresponding feature learning.
Approach:	They propose to integrate medical knowledge into ADE detection from text . they use contextualized embeddings from pretrained language models and convolutional graph neural networks to learn features differently for different types of nodes in the graph.
Outcome:	The proposed model outperforms existing models on four public datasets and shows that it is based on medical knowledge and embeddings from pretrained language models and neural networks.

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Mengzhu Liu, Zhengqiu Zhu, Chuan Ai, Chen Gao, Xinghong Li, Lingnan He, Kaisheng Lai, Yingfeng Chen, Xin Lu, Yong Li, Quanjun Yin

Explainability & Interpretability in NLP

Challenge:	Social media's rich information content and spatiotemporal granularity provide unique opportunities for emotion prediction and management.
Approach:	They propose a Psychology-driven generative Agent framework for explainable panic prediction based on emotion arousal theory.
Outcome:	The proposed framework improves panic emotion prediction performance by 13% to 21% compared to baseline models.

KPatch: Knowledge Patch to Pre-trained Language Model for Zero-Shot Stance Detection on Social Media (2024.lrec-main)

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Shuohao Lin, Wei Chen, Yunpeng Gao, Zhishu Jiang, Mengqi Liao, Zhiyu Zhang, Shuyuan Zhao, Huaiyu Wan

MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models (2025.emnlp-main)

Challenge:	Existing knowledge injection methods fail to understand the semantics of tweets .
Approach:	They propose a method to flexibly inject knowledge into a pre-trained language model and adaptively expand tweets context.
Outcome:	The proposed method is based on two training stages to flexibly inject knowledge into the pre-trained language model and adaptively expand tweets context.

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Zixin Chen, Hongzhan Lin, Kaixin Li, Ziyang Luo, Yayue Deng, Jing Ma

More Than Sum of Its Parts: Deciphering Intent Shifts in Multimodal Hate Speech Detection (2026.findings-acl)

Challenge:	Existing evaluation approaches focus on mLLMs’ detection accuracy for binary classification tasks, which often fail to reflect the in-depth interpretive nuance of harmfulness across diverse contexts.
Approach:	They propose an agent-based arena-style evaluation framework that provides context-aware and unbiased assessment for mLLMs’ understanding of multimodal harmfulness.
Outcome:	The proposed framework reduces evaluation biases of judge agents and provides unbiased comparisons of mLLMs’ abilities to interpret multimodal harmfulness.

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Runze Sun, Yu Zheng, Zexuan Xiong, Zhongjin Qu, Lei Chen, Jie Zhou, Jiwen Lu

GenEx: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection (2023.emnlp-main)

Challenge:	Existing systems struggle with multimodal content where the emergent meaning transcends the aggregation of individual modalities.
Approach:	They propose a framework to characterize semantic intent shifts where modalities interact to construct implicit hate from benign cues or neutralize toxicity through semantic inversion.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on H-VLI and on established benchmarks.

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Krishanu Maity, Raghav Jain, Prince Jha, Sriparna Saha, Pushpak Bhattacharyya

Reasoning

Challenge:	a significant gap exists in understanding code-mixed languages and the need for explainability in this context.
Approach:	They propose to annotate posts with four labels to identify bullies in code-mixed languages . they propose to use a generative framework to reimagine the multitask problem as a text-to-text generation task.
Outcome:	The proposed model outperforms baseline models and state-of-the-art models on the BullyExplain dataset.

SOLAR: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offs (2025.emnlp-main)

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Younghun Lee, Dan Goldwasser

Challenge:	Existing studies suggest that Large Language Models can account for individual-level subjectivity, yet exploring whether LLMs can generate perspectives and reasoning that align well with a specific persona or demographic information has not been adequately studied.
Approach:	They propose a framework that observes value conflicts and trade-offs in user-generated texts to better represent subjective ground of individuals.
Outcome:	The proposed framework improves inference performance for users with limited data and in controversial situations.

Evaluating Short-Term Temporal Fluctuations of Social Biases in Social Media Data and Masked Language Models (2024.emnlp-main)

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Yi Zhou, Danushka Bollegala, Jose Camacho-Collados

Detecting Online Community Practices with Large Language Models: A Case Study of Pro-Ukrainian Publics on Twitter (2024.emnlp-main)

Challenge:	Social biases such as gender or racial biase are reported in language models . a recent study has shown that MLMs encode discriminatory social biase .
Approach:	They analyse temporal corpora of MLMs trained on chronologically ordered temporal snapshots . they find that gender and racial biases are encoded in MLM models .
Outcome:	The proposed model identifies gender biases in MLMs but most remain stable over time . gender bias is associated with higher likelihood scores in some demographic groups .

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Kateryna Kasianenko, Shima Khanehzar, Stephen Wan, Ehsan Dehghan, Axel Bruns

Multilingual Topic Classification in X: Dataset and Analysis (2024.emnlp-main)

Challenge:	Existing methods for identifying practices within social media are not yet available.
Approach:	They propose a methodological workflow for computational identification of such practices within social media texts by using open-source models and OpenAI’s large language models.
Outcome:	The proposed method improves accuracy and supports context-sensitive moderation and advancing the understanding of online community dynamics.

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Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Jose Camacho-Collados

FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis (2025.findings-acl)

Challenge:	Social media platforms such as X (Twitter), Snapchat and Instagram provide an environment for content creation and information sharing.
Approach:	They propose a multilingual dataset featuring tweet topic classification in four languages . they leverage X-Topic to perform cross-linguistic and multilingual analysis .
Outcome:	The proposed dataset includes topics in four languages and is useful for cross-linguistic analysis and the development of robust multilingual models.

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Yilun Zheng, Sha Li, Fangkun Wu, Yang Ziyi, Lin Hongchao, Zhichao Hu, Cai Xinjun, Ziming Wang, Jinxuan Chen, Sitao Luan, Jiahao Xu, Lihui Chen

Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention (2024.lrec-main)

Challenge:	Parody is an emerging phenomenon on social media, where individuals imitate a role or position opposite to their own . limited available data and deficient diversity in current datasets hinder study of parody .
Approach:	They build a dataset of parody users and annotated comments from both English and Chinese corpora to test parody detection and comment sentiment analysis.
Outcome:	The proposed datasets provide richer contextual information, which is lacking in existing datasets.

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Mingmin Wu, Guixin Su, Yongcheng Zhang, Zhongqiang Huang, Ying Sha

Insights into using temporal coordinated behaviour to explore connections between social media posts and influence (2025.findings-emnlp)

Challenge:	Existing methods based on deep learning struggle to grasp idiom semantics due to the figurative meanings of many idiomas deviating from their literal interpretations.
Approach:	They propose a Chinese idiom cloze test to capture comprehensive idiomatics and a semantic sense contrastive learning module to enhance the representation of idiomics.
Outcome:	The proposed model outperforms state-of-the-art models on the Chinese idiom cloze test and on other benchmark datasets.

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Elisa Sartori, Serena Tardelli, Maurizio Tesconi, Mauro Conti, Alessandro Galeazzi, Stefano Cresci, Giovanni Da San Martino

Challenge:	Political campaigns often use coordinated behaviour to identify communities of users who exhibit similar patterns.
Approach:	They analysed messages users were exposed to during the UK 2019 election and compared those received by users who shifted communities with others covering the same topics.
Outcome:	The results show that political campaigns often use coordinated behaviour to identify communities of users who exhibit similar patterns.

Just a Scratch: Enhancing LLM Capabilities for Self-harm Detection through Intent Differentiation and Emoji Interpretation (2025.acl-long)

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Soumitra Ghosh, Gopendra Vikram Singh, Shambhavi Shambhavi, Sabarna Choudhury, Asif Ekbal

HATECAT-TR: A Hate Speech Span Detection and Categorization Dataset for Turkish (2025.findings-emnlp)

Challenge:	Self-harm detection on social media is critical for early intervention and mental health support, yet remains challenging due to the subtle, context-dependent nature of such expressions.
Approach:	They propose a framework to distinguish intent through nuanced language–emoji interplay.
Outcome:	The proposed framework improves self-harm detection and explanation tasks on three state-of-the-art LLMs.

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Hasan Kerem Şeker, Gökçe Uludoğan, Pelin Önal, Arzucan Özgür

MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)

Challenge:	a new dataset of Turkish tweets contains 4465 hateful spans . each hateful post is directed at one of eight minority groups .
Approach:	They propose a span-annotated dataset of Turkish tweets containing 4465 hateful spans . each hateful spat is categorized into one of five discourse types .
Outcome:	The proposed dataset contains 4465 hateful spans across 2981 tweets . each span is categorized into one of five discourse types .

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Bingbing Wang, Zhengda Jin, Bin Liang, Wenjie Li, Jing Li, Ruifeng Xu, Min Zhang

MemeIntel: Explainable Detection of Propagandistic and Hateful Memes (2025.emnlp-main)

Challenge:	Existing methods operate by learning to fuse modalities, leading to frequent misjudgments.
Approach:	They propose a paradigm shift from learning to fuse to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop.
Outcome:	The proposed model significantly outperforms baseline models and exhibits strong generalization.

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Mohamed Bayan Kmainasi, Abul Hasnat, Md Arid Hasan, Ali Ezzat Shahroor, Firoj Alam

PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media (2026.acl-long)

Challenge:	Existing methods for label detection and explanation generation have been limited in understanding complex issues . identifying propaganda and hate in memes is essential for combating misinformation and minimizing harm .
Approach:	They propose an explanation-enhanced dataset for propaganda memes in Arabic and hateful memes on English to solve these tasks.
Outcome:	The proposed model outperforms the current state-of-the-art in label detection and explanation generation.

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Zoher Kachwala, Bao Tran Truong, Rasika Muralidharan, Haewoon Kwak, Jisun An, Filippo Menczer

Language Models

Challenge:	Social media are shifting towards community-governed platforms where groups define their own norms.
Approach:	They propose a multimodal, multilingual benchmark for detecting 13,371 rule violations across 1,989 Reddit communities . they show that bigger models and increased context provide marginal gains, and universal rules like civility and self-promotion are easier to detect.
Outcome:	The proposed model can detect 13,371 rule violations across 1,989 Reddit communities across 2,885 rules in 9 languages.

FigSIM: A Dataset for Fine-grained Suicide Severity and Figurative Language in Suicide Memes (2026.findings-acl)

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Liuliu Chen, Elise Carrotte, Brian E. Chapman, Jo Robinson, Mike Conway