Austria

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.

ACL 2025

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

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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you need to log in with the **email address you registered with**. 

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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.

RAG and Recall: Multilingual Hate Speech Detection with Semantic Memory

This work investigates the effectiveness of masked language models (MLMs) and autoregressive language models (LLMs) with fewer than one billion parameters in the detection of implicit hate speech through fine-grained span identification. The evaluation spans zero-shot, few-shot, and full supervision settings across two core benchmarks—SBIC and IHC—and an auxiliary testbed, OffensiveLang.

RoBERTa-Large-355M emerges as the strongest zero-shot model, achieving the highest F1 scores of 75.8 (SBIC) and 72.5 (IHC), outperforming larger models like LLaMA 3.2-1B. ModernBERT-125M closely matches this performance with scores of 75.1 and 72.2, demonstrating the advantage of architectural efficiency. Among instruction-tuned models, SmolLM2-135M Instruct and LLaMA 3.2 1B Instruct consistently outperform their non-instructed counterparts, with up to +2.3 F1 gain on SBIC and +1.7 on IHC. Interestingly, the larger SmolLM2-360M Instruct does not outperform the 135M variant, highlighting that model scale does not always correlate with performance in implicit hate detection tasks.

Few-shot fine-tuning with SmolLM2-135M Instruct achieves F1 scores of 68.2 (SBIC) and 64.0 (IHC), trailing full-data fine-tuning by only 1.6 and 2.0 points, respectively, with accuracy drops under 0.5 points. This illustrates the promise of compact, instruction-aligned models in data-scarce settings, particularly when optimized with Low-Rank Adaptation (LoRA).

Topic-guided error analysis using Latent Dirichlet Allocation (LDA) reveals recurring model failures in ideologically charged or euphemistic discourse. Misclassifications often involve neutral references to identity, politics, or advocacy language, underscoring current limitations in discourse-level inference and sociopragmatic understanding.

Implicit Hate Target Span Detection in Zero- and Few-Shot Settings with Selective Sub-Billion Parameter Models

Bridging NLP with political science, this paper examines both the potential and the limitations of a computational hate speech detection method in addressing real-world questions. Using Greece as a case study, we analyze over 4 million tweets from 2015 to 2022—a period marked by economic, refugee, foreign policy, and pandemic crises. The analysis of false positives highlights the challenges of accurately detecting different types of verbal attacks across various targets and timeframes. In addition, the analysis of true positives reveals distinct linguistic patterns that reinforce populist narratives, polarization and hostility. By situating these findings within their socio-political context, we provide insights into how hate speech manifests online in response to real-world crises.

Hate Speech in Times of Crises: a Cross-Disciplinary Analysis of Online Xenophobia in Greece

Toxic language online poses growing challenges for content moderation. Detoxification, which rewrites toxic content into neutral form, offers a promising alternative but remains underexplored beyond English. We present Detoxify-IT, the first Italian dataset for this task, featuring toxic comments and their human-written neutral rewrites. Our experiments show that even limited fine-tuning on Italian data leads to notable improvements in content preservation and fluency compared to both multilingual models and LLMs used in zero-shot settings, underlining the need for language-specific resources. This work enables detoxification research in Italian and supports broader efforts toward safer, more inclusive online communication.

DETOXIFY-IT: An Italian Parallel Dataset for Text Detoxification

Online communities play an integral part in communication for communication across the globe. Online communities that are known for extremist content. As a field of surveillance technologies, NLP and other ML fields hold particular promise for monitoring extremist communities that may turn violent. Such communities make use of a wide variety of modalities of communication, including textual posts on specialised fora, memes, videos, and podcasts. Furthermore, such communities undergo rapid linguistic evolution, thus presenting a challenge to machine learning technologies that quickly diverge from the data that are used. In this position, we argue that radicalisation is a nascent area for which machine learning is particularly apt. However, in addressing radicalisation research it is important that avoids falling into the temptation of focusing on prediction. We argue that such communities present a particular avenue for addressing key concerns with machine learning technologies: (1) temporal misalignment of models and (2) aligning and linking content across modalities.

Pathways to Radicalisation: On Research for Online Radicalisation in Natural Language Processing and Machine Learning

Understanding how words shift in meaning is crucial for analyzing societal attitudes. In this study, we investigate the contextual variations of the terms feminist, feminists along three axes: time, language, and domain. To this aim, we collect and release FEMME, a dataset comprising the occurrences of such terms from 2014 to 2023 in English, Italian and Swedish in Twitter, Reddit and Incel domains. Our methodology leverages frame analysis, as well as fine-tuning and LLMs. We find that the connotation of the plural form feminists is consistently more negative than feminist, indicating more hostility towards feminists as a collective, which often triggers greater societal pushback, reflecting broader patterns of group-based hostility and stigma. Across languages, we observe similar stereotypes towards feminists that often include body shaming, as well as accusations of hypocrisy and irrational behavior. In terms of time, we identify events that trigger a peak in terms of negative or positive connotation. As expected, the Incel spheres show predominantly negative connotations, while the general domains show mixed connotations.

Blue-haired, misandriche, rabiata: Tracing the Connotation of 'Feminist(s)' Across Time, Languages and Domains

Numerous studies have proposed computational methods to detect hate speech online, yet most focus on the English language and emphasize model development. In this study, we evaluate the counterfactual fairness of hate speech detection models in the Dutch language, specifically examining the performance and fairness of transformer-based models. We make the following key contributions. First, we curate a list of Dutch Social Group Terms that reflect social context. Second, we generate counterfactual data for Dutch hate speech using LLMs and established strategies like Manual Group Substitution (MGS) and Sentence Log-Likelihood (SLL). Through qualitative evaluation, we highlight the challenges of generating realistic counterfactuals, particularly with Dutch grammar and contextual coherence. Third, we fine-tune baseline transformer-based models with counterfactual data and evaluate their performance in detecting hate speech. Fourth, we assess the fairness of these models using Counterfactual Token Fairness (CTF) and group fairness metrics, including equality of odds and demographic parity. Our analysis shows that models perform better in terms of hate speech detection, average counterfactual fairness and group fairness. This work addresses a significant gap in the literature on counterfactual fairness for hate speech detection in Dutch and provides practical insights and recommendations for improving both model performance and fairness.

Towards Fairness Assessment of Dutch Hate Speech Detection

The 'femosphere' is a term coined to describe a group of online ideological spaces for women characterised by toxicity, reactionary feminism, and hetero-pessimism. It is often portrayed as a mirror of a similar group of communities for men, called the `manosphere'. Although there have been several studies investigating the ideologies and language of the manosphere, the femosphere has been largely overlooked - especially in NLP. This paper presents a study of two communities in the femosphere: Female Dating Strategy and Femcels. It presents an exploration of the language of these communities on topics related to relationships, sex, and men from the perspective of hetero-pessimism using topic modelling and semantic analysis. It reveals dissatisfaction with heterosexual courtship and frustration with the patriarchal society through which members attempt to navigate.

Between Hetero-Fatalism and Dark Femininity: Discussions of Relationships, Sex, and Men in the Femosphere

Large language models (LLMs) have become ubiquitous, thus it is important to understand their risks and limitations, such as their propensity to generate harmful output. This includes smaller LLMs, which are important for settings with constrained compute resources, such as edge devices. Detection of LLM harm typically requires human annotation, which is expensive to collect. This work studies two questions: How do smaller LLMs rank regarding generation of harmful content? How well can larger LLMs annotate harmfulness? We prompt three small LLMs to elicit harmful content of various types, such as discriminatory language, offensive content, privacy invasion, or negative influence, and collect human rankings of their outputs. Then, we compare harm annotation from three state-of-the-art large LLMs with each other and with humans. We find that the smaller models differ with respect to harmfulness. We also find that large LLMs show low to moderate agreement with humans.

Downloads

Next from ACL 2025

Exploring Hate Speech Detection Models for Lithuanian Language

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2025

Exploring Hate Speech Detection Models for Lithuanian Language

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads