Austria

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.

ACL 2025

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

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

To access the event page you need to register [**here.**](http://acl.swoogo.com/naacl2024) 
Your access to the event page is limited based on your registration type. If you registered for workshops only, you will gain access to full workshops content on the day of the workshops program.

Please register!

NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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)

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.

Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet

The way we relay factual information and the way we present deceptive information as truth differs from the perspective of argumentation. In this paper, we explore whether these differences can be exploited to detect deceptive political news in English. We do this by training a model to detect different kinds of argumentation in online news text. We use sentence embeddings extracted from an argumentation type classification model as features for a deceptive news classifier. This deceptive news classification model leverages the sequence of argumentation types within an article to determine whether it is credible or deceptive. Our approach outperforms other state-of-the-art models while having lower variance. Finally, we use the output of our argumentation model to analyze the differences between credible and deceptive news based on the distribution of argumentation types across the articles. Results of this analysis indicate that credible political news presents statements supported by a variety of argumentation types, while deceptive news relies on anecdotes and testimonial.

Are You Trying to Convince Me or Are You Trying to Deceive Me? Using Argumentation Types to Identify Deceptive News

The recent advancements in Large Language Models(LLMs) have had a significant impact on a wide range of fields, from general domains to specialized areas. However, these advancements have also significantly increased the potential for malicious users to exploit harmful and jailbreak prompts for malicious attacks. Although there have been many efforts to prevent harmful prompts and jailbreak prompts, protecting LLMs from such malicious attacks remains an important and challenging task. In this paper, we propose QGuard, a simple yet effective safety guard method, that utilizes question prompting to block harmful prompts in a zero-shot manner. Our method can defend LLMs not only from text-based harmful prompts but also from multi-modal harmful prompt attacks. Moreover, by diversifying and modifying guard questions, our approach remains robust against the latest harmful prompts without fine-tuning. Experimental results show that our model performs competitively on both text-only and multi-modal harmful datasets. Additionally, by providing an analysis of question prompting, we enable a white-box analysis of user inputs. We believe our method provides valuable insights for real-world LLM services in mitigating security risks associated with harmful prompts.

QGuard:Question-based Zero-shot Guard for Multi-modal LLM Safety

A dogwhistle is a communicative act intended to broadcast a message only understood by a select in-group while going unnoticed by others (out-group). We illustrate that political dogwhistle behavior in a more radical community precedes the occurrence of the dogwhistles in a less radical community, but the reverse does not hold. We study two Swedish online communities -- Flashback and Familjeliv -- which both contain discussions of life and society, with the former having a stronger anti-immigrant subtext. Expressions associated with dogwhistles are substantially more frequent in Flashback than in Familjeliv. We analyze the time series of changes in intensity of three dogwhistle expressions (DWEs), i.e., the strength of association of a DWE and its in-group meaning modeled by Swedish Sentence-BERT, and model the dynamic temporal relationship of intensity in the two communities for the three DWEs using Vector Autoregression (VAR). We show that changes in intensity in Familjeliv are explained by the changes of intensity observed at previous lags in Flashback but not the other way around. This suggests a direction of travel for dogwhistles associated with radical ideologies to less radical contexts.

Who leads? Who follows? Temporal dynamics of political dogwhistles in Swedish online communities

Despite growing interest in automated hate speech detection, most existing approaches overlook the linguistic diversity of online content. Multilingual instruction-tuned large language models such as LLaMA, Aya, Qwen, and BloomZ offer promising capabilities across languages, but their effectiveness in identifying hate speech through zero-shot and few-shot prompting remains underexplored. This work evaluates LLM prompting-based detection across eight non-English languages, utilizing several prompting techniques and comparing them to fine-tuned encoder models. We show that while zero-shot and few-shot prompting lag behind fine-tuned encoder models on most of the real-world evaluation sets, they achieve better generalization on functional tests for hate speech detection. Our study also reveals that prompt design plays a critical role, with each language often requiring customized prompting techniques to maximize performance.

Can Prompting LLMs Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Study

Conspiracist narratives posit an omnipotent, evil group causing harm throughout domains. However, modern-day online conspiracism is often more erratic, consisting of loosely connected posts displaying a general anti-establishment attitude pervaded by negative emotions. We gather a dataset of 300 conspiracist and mainstream, Telegram channels in Italian and English and use the automatic extraction of entities and emotion detection to compare structural characteristics of both types of channels. We create a co-occurrence network of entities to analyze how the different types of channels introduce and use them across posts and topics. We find that conspiracist channels are characterized by anger. Moreover, co-occurrence networks of entities appearing in conspiracist channels are more dense. We theorize that this reflects a narrative structure where all actants are pushed into a single domain. Conspiracist channels disproportionately associate the most central group of entities with anger and fear. We do not find evidence that entities in conspiracist narratives occur across more topics. This could indicate an erratic type of online conspiracism where everything can be connected to everything and that is characterized by a high number of entities and high levels of anger.

Multilingual Analysis of narrative characteristics in conspiracist vs mainstream Telegram channels

Hate speech detection is vital for creating safe online environments, as harmful content can drive social polarization. This study explores the impact of enriching text with intent and group tags on machine performance and human moderation workflows. For machine performance, we enriched text with intent and group tags to train hate speech classifiers. Intent tags were the most effective, achieving state-of-the-art F1-score improvements on the IHC, SBIC, and DH datasets, respectively. Cross-dataset evaluations further demonstrated the superior generalization of intent-tagged models compared to other pre-trained approaches. Then, through a user study (N=100), we evaluated seven moderation settings, including intent tags, group tags, model probabilities, and randomized counterparts. Intent annotations significantly improved the accuracy of the moderators, allowing them to outperform machine classifiers by 12.9%. Moderators also rated intent tags as the most useful explanation tool, with a 41% increase in perceived helpfulness over the control group. Our findings demonstrate that intent-based annotations enhance both machine classification performance and human moderation workflows.

Downloads

Next from ACL 2025

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

Similar lecture

Investigating radicalisation indicators in online extremist communities

.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

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

Similar lecture

Investigating radicalisation indicators in online extremist communities

Downloads