Austria

In-context learning (ICL) has shown significant benefits, particularly in scenarios where large amounts of labeled data are unavailable. However, its effectiveness for highly subjective tasks, such as toxic language detection, remains an open question. A key challenge in this setting is to select shots to maximize performance. Although previous work has focused on enhancing variety and representativeness, the role of annotator disagreement in shot selection has received less attention. In this paper, we conduct an in-depth analysis of ICL using two families of open-source LLMs (Llama-3* and Qwen2.5) of varying sizes, evaluating their performance in five prominent English datasets covering multiple toxic language phenomena. We use disaggregated annotations and categorize different types of training examples to assess their impact on model predictions. We specifically investigate whether selecting shots based on annotators&#39; entropy -- focusing on ambiguous or difficult examples -- can improve generalization in LLMs. Additionally, we examine the extent to which the order of examples in prompts influences model behavior.
Our results show that selecting shots based on entropy from annotator disagreement can enhance ICL performance. Specifically, ambiguous shots with a median entropy value generally lead to the best results for our selected LLMs in the few-shot setting. However, ICL often underperforms when compared to fine-tuned encoders.

ACL 2025

Learning from Disagreement: Entropy-Guided Few-Shot Selection for Toxic Language Detection

## Welcome to EMNLP 2024! 
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offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

In-context learning (ICL) has shown significant benefits, particularly in scenarios where large amounts of labeled data are unavailable. However, its effectiveness for highly subjective tasks, such as toxic language detection, remains an open question. A key challenge in this setting is to select shots to maximize performance. Although previous work has focused on enhancing variety and representativeness, the role of annotator disagreement in shot selection has received less attention. In this paper, we conduct an in-depth analysis of ICL using two families of open-source LLMs (Llama-3* and Qwen2.5) of varying sizes, evaluating their performance in five prominent English datasets covering multiple toxic language phenomena. We use disaggregated annotations and categorize different types of training examples to assess their impact on model predictions. We specifically investigate whether selecting shots based on annotators' entropy -- focusing on ambiguous or difficult examples -- can improve generalization in LLMs. Additionally, we examine the extent to which the order of examples in prompts influences model behavior.
Our results show that selecting shots based on entropy from annotator disagreement can enhance ICL performance. Specifically, ambiguous shots with a median entropy value generally lead to the best results for our selected LLMs in the few-shot setting. However, ICL often underperforms when compared to fine-tuned encoders.

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

The curation of hate speech datasets involves complex design decisions that balance competing priorities. This paper critically examines these methodological choices in a diverse range of datasets, highlighting common themes and practices, and their implications for dataset reliability. Drawing on Max Weber's notion of ideal types, we argue for a reflexive approach in dataset creation, urging researchers to acknowledge their own value judgments during dataset construction, fostering transparency and methodological rigour.

Web(er) of Hate: A Survey on How Hate Speech Is Typed

Automated counter-narratives (CN) offer a promising strategy for mitigating online hate speech, yet concerns about their affective tone, accessibility, and ethical risks remain. We propose a framework for evaluating Large Language Model (LLM)-generated CNs across four dimensions: persona framing, verbosity and readability, affective tone, and ethical robustness. Using GPT-4o-Mini, Cohere's CommandR-7B, and Meta's LLaMA 3.1-70B, we assess three prompting strategies on the MT-Conan and HatEval datasets. Our findings reveal that LLM-generated CNs are often verbose and adapted for people with college-level literacy, limiting their accessibility. While emotionally guided prompts yield more empathetic and readable responses, there remain concerns surrounding safety and effectiveness.

Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate Speech.

The escalating spread of homophobic and transphobic rhetoric in both online and offline spaces has become a growing global concern, with Italy standing out as one of the countries where acts of violence against LGBTQIA+ individuals persist and increase year after year. This short paper study analyzes hateful language against LGBTQIA+ individuals in Italian using novel annotation labels for aggressiveness and target. We assess a range of multilingual and Italian language models on this new
annotation layers across zero-shot, few-shot, and fine-tuning settings. The results reveal significant performance gaps across models and settings, highlighting the limitations of zero- and few-shot approaches and the importance of fine-tuning on labelled data, when available, to achieve high prediction performance.

HODIAT: A New Dataset for Detecting Homotransphobic Hate Speech in Italian with Aggressiveness and Target Annotation

Online communities provide support and help to individuals transitioning gender. However, this point of transition also increases vulnerability, coupled with increased exposure to online harms. In this research, we analyse a popular hate and harassment site known for targeting minority groups, including transgender people. We analyse 17 million posts dating back to 2012 to gain insights into the types of information collected about targets. We find users commonly link to social media sites such as Twitter/X and meticulously archive links related to their targets. We scrape over 150,000 relevant links posted to Twitter/X and their archived versions and analyse the profiles and posts. We find targets often tweet about harassment, popculture, and queer and gender-related discussions. We develop and evaluate classifiers to detect calls for harassment, doxxing, mention of transgender individuals, and toxic/abusive speech within the forum posts. The results of our classifiers show that forum posts about transgender individuals are significantly more likely to contain other harmful content.

Beyond the Binary: Analysing Transphobic Hate and Harassment Online

We introduce a novel class of adversarial attacks on toxicity detection models that exploit language models' failure to interpret spatially structured text in the form of ASCII art. To evaluate the effectiveness of these attacks, we propose ToxASCII, a benchmark designed to assess the robustness of toxicity detection systems against visually obfuscated inputs. Our attacks achieve a perfect Attack Success Rate (ASR) across a diverse set of state-of-the-art large language models and dedicated moderation tools, revealing a significant vulnerability in current text-only moderation systems.

Evading Toxicity Detection with ASCII-art: A Benchmark of Spatial Attacks on Moderation Systems

Counterspeech is a key strategy against harmful online content, but scaling expert-driven efforts is challenging. Large Language Models (LLMs) present a potential solution, though their use in countering conspiracy theories is under-researched. Unlike for hate speech, no datasets exist that pair conspiracy theory comments with expert-crafted counterspeech. We address this gap by evaluating the ability of GPT-4o, Llama 3, and Mistral to effectively apply counterspeech strategies derived from psychological research provided through structured prompts. Our results show that the models often generate generic, repetitive, or superficial results. Additionally, they over-acknowledge fear and frequently hallucinate facts, sources, or figures, making their prompt-based use in practical applications problematic.

Debunking with Dialogue? Exploring AI-Generated Counterspeech to Challenge Conspiracy Theories

Connectivity and message propagation are central, yet often underutilised, sources of information in misinformation detection—especially on poorly moderated platforms such as Telegram, which has become a critical channel for misinformation dissemination, namely in the German electoral context. In this paper, we introduce Misinfo-TeleGraph, the first German-language Telegram-based graph dataset for misinformation detection. It includes over 5 million messages from public channels, enriched with metadata, channel relationships, and both weak and strong labels. These labels are derived via semantic similarity to fact-checks and news articles using M3-embeddings, as well as manual annotation. To establish reproducible baselines, we evaluate both text-only models and graph neural networks (GNNs) that incorporate message forwarding as a network structure. Our results show that GraphSAGE with LSTM aggregation significantly outperforms text-only baselines in terms of Matthews Correlation Coefficient (MCC) and F1-score. We further evaluate the impact of subscribers, view counts, and automatically versus human-created labels on performance, and highlight both the potential and challenges of weak supervision in this domain. This work provides a reproducible benchmark and open dataset for future research on misinformation detection in German-language Telegram networks and other low-moderation social platforms.

MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages

This paper examines how emotional responses to football matches influence online discourse across digital spaces on Reddit. By analysing millions of posts from dozens of subreddits, it demonstrates that real-world events trigger sentiment shifts that move across communities. It shows that negative sentiment correlates with problematic language; match outcomes directly influence sentiment and posting habits; sentiment can transfer to unrelated communities; and offers insights into the content of this shifting discourse. These findings reveal how digital spaces function not as isolated environments, but as interconnected emotional ecosystems vulnerable to cross-domain contagion triggered by real-world events, contributing to our understanding of the propagation of online toxicity. While football is used as a case-study to computationally measure affective causes and movements, these patterns have implications for understanding online communities broadly.

Catching Stray Balls: Football, fandom, and the impact on digital discourse

Online hate speech poses a significant challenge, as it can incite violence and contribute to social polarization. This study evaluates traditional machine learning, deep learning and large language models (LLMs) for Lithuanian hate speech detection, addressing class imbalance issue via data augmentation and resampling techniques. Our dataset included 27,358 user-generated comments, annotated into Neutral language (56\%), Offensive language (29\%) and Hate speech (15\%). We trained BiLSTM, LSTM, CNN, SVM, and Random Forest models and fine-tuned Multilingual BERT, LitLat BERT, Electra, RWKV, ChatGPT, LT-Llama-2, and Gemma-2 models. Additionally, we pre-trained Electra for Lithuanian. Models were evaluated using accuracy and weighted F1-score. On the imbalanced dataset, LitLat BERT (0.76 weighted F1-score) and Multilingual BERT (0.73 weighted F1-score) performed best. Over-sampling further boosted weighted F1-scores, with Multilingual BERT (0.85) and LitLat BERT (0.84) outperforming other models. Over-sampling combined with augmentation provided the best overall results. Under-sampling led to performance declines and was less effective. Finally, fine-tuning LLMs improved their accuracy which highlighted the importance of fine-tuning for more specialized NLP tasks.

Exploring Hate Speech Detection Models for Lithuanian Language

Multilingual hate speech detection presents a challenging task, particularly in limited-resource contexts when performance is affected by cultural nuances and data scarcity. Fine-tuned models are often unable to generalize beyond their training, which limits their efficiency, especially for low-resource languages. 
In this paper, we introduce HS-RAG, a retrieval-augmented generation (RAG) system that directly leverages knowledge, in English, French, and Arabic, from Hate Speech Superset (publicly available dataset) and Wikipedia to Large Language Models (LLMs). 
To further enhance robustness, we introduce HS-MemRAG, a memory-augmented extension that integrates a semantic cache. This model reduces redundant retrieval while improving contextual relevance and hate speech detection among the three languages.

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