Austria

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.

ACL 2025

Hate Explained: Evaluating NER-Enriched Text in Human and Machine Moderation of Hate Speech

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

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)

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

Technology-facilitated gender-based violence has become a global threat to women's political representation and democracy. Understanding how online hate affects its targets is thus paramount. This study analyzes 10 million tweets directed at female candidates in the Brazilian election in 2022 and examines their reactions to online misogyny. Using a self-trained machine learning classifier to detect Portuguese misogynistic tweets and a quantitative analysis of the candidates' tweeting behavior, the study investigates how the number of misogynistic attacks received alters the online activity of the female candidates. The analysis finds that young and left-wing candidates and candidates with higher visibility online received significantly more attacks. Furthermore, the analysis finds that an increase in misogynistic attacks in the previous week is associated with a decrease in female candidates' tweets in the following week. This potentially threatens their equal participation in public opinion formation and silences women's voices in political discourse.

Online Misogyny Against Female Candidates in the 2022 Brazilian Elections: A Threat to Women's Political Representation?

Technology-facilitated violence constitutes a growing threat to inclusive democratic participation, particularly affecting women and marginalized groups. Drawing on a mixed-methods study from Brazil, this research examines how online harassment impacts politically active individuals, with a focus on gendered experiences and differential outcomes. A quantitative survey of 597 politically active individuals and seven in-depth interviews with female politicians were analyzed to assess the psychological and political consequences of technology-facilitated violence. More than 70% of survey respondents reported harassment within the past four weeks, with women disproportionately affected by gendered types of abuse. Regression results demonstrate that years of activism, education, and occupation significantly shape both exposure to harassment and coping responses. While some interviewees reported emotional exhaustion and disengagement, others described increased motivation and political resolve. These findings offer critical insights into how technology-facilitated violence shapes political behavior and underscore the need for platform accountability and institutional support structures.

Technology-Facilitated Violence and Political Participation: Evidence from a Mixed-Methods Study in Brazil

This work in progress presents an early-stage, conceptual exploration of how civil society actors can be meaningfully involved in the development of an AI-assisted tool for monitoring anti-democratic movements on Telegram. Using conspiracy theory classification as a use case, we outline and compare two concrete approaches to enabling such collaboration.

Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool

**(Civil Society Reports)**


Online hate speech propagation is a complex issue, deeply influenced by both the perpetrator and the target's cultural, historical, and societal contexts. Consequently, developing a universally robust hate speech classifier for diverse social media texts remains a challenging and unsolved task. The lack of mechanisms to track the spread and severity of hate speech further complicates the formulation of effective solutions. In response to this, to monitor hate speech in Indonesia during the recent 2024 presidential election, we have employed advanced
Natural Language Processing (NLP) technologies to create an improved hate speech classifier
tailored for a narrower subset of texts; specifically, texts that target vulnerable groups that
have historically been the targets of hate speech in Indonesia.

(Civil Society Reports) - Monitoring Hate Speech in Indonesia: An NLP-based Classification of Social Media Texts

**(Non-Archival Submission)**


Toxicity and polarization are often treated as distinct problems in NLP, despite their increasing overlap in online discourse. Indonesia, as the third-largest democracy in the world, has recently seen a sharp increase in both toxicity and polarizing messages online. We present a novel multi-labeled Indonesian dataset annotated for both toxicity and polarization, and with information on annotator demographics. Our benchmark results show that polarization cues help toxicity detection and vice versa. Including demographic features further improves toxicity and polarization classification, suggesting that identity and opinion dynamics are deeply intertwined in toxic and polarizing speech.

(Non-Archival Submission) A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information

In recent years, researchers have investigated the socio-demographic and personal factors influencing perceptions of toxic speech, with a primary focus on English-speaking contexts. We introduce a work-in-progress annotation study conducted in a German-language setting. Preliminary findings from our pre-study suggest that annotators' perceptions of the toxicity of statements correlate more strongly with their perceptions of the toxicity of other statements than with socio-demographic factors, attitudes toward freedom of speech, or perceived (dis-)similarity to the author and target of the statement. Subsequently, we aim to utilize this novel dataset to explore perspectivist fine-tuning approaches for LLMs.

Alignment by Disagreement? Toward Investigating LLMs' Adaptation to Personal and Sociodemographic Variability in the Perception of Toxicity

We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investigate the impact of injecting diverse persona descriptions into LLM prompts across two studies, exploring whether personas increase annotation diversity and whether the impacts of individual personas on the resulting annotations are consistent and controllable. Our results indicate that persona-prompted LLMs generate more diverse annotations than LLMs prompted without personas, and that the effects of personas on LLM annotations align with subjective differences in human annotations. These effects are both controllable and repeatable, making our approach a valuable tool for enhancing data annotation in subjective NLP tasks such as toxicity detection.

Personas with Attitudes: Controlling LLMs for Diverse Data Annotation

As large language models (LLMs) become increasingly prevalent, ensuring their robustness against adversarial misuse is crucial. This paper introduces the GAP (Graph of Attacks with Pruning) framework, an advanced approach for generating stealthy jailbreak prompts to evaluate and enhance LLM safeguards. GAP addresses limitations in existing tree-based methods by implementing an interconnected graph structure that enables knowledge sharing across attack paths. Our experimental evaluation demonstrates GAP's superiority over existing techniques, achieving a 20.8% increase in attack success rates while reducing query costs by 62.7%. GAP consistently outperforms state-of-the-art methods across various open and closed LLMs, with attack success rates of 
96%. Additionally, we present specialized variants like GAP-Auto for automated seed generation and GAP-VLM for multimodal attacks. GAP-generated prompts prove highly effective in improving content moderation systems, increasing true positive detection rates by 108.5% and accuracy by 183.6% when used for fine-tuning.

Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt. Generation for Enhanced LLM Content Moderation

Detecting harmful content is a crucial task in the landscape of NLP applications for Social Good, with hate speech being one of its most dangerous forms. But what do we mean by hate speech, how can we define it and how does prompting different definitions of hate speech affect model performance? The contribution of this work is twofold. At the theoretical level, we address the ambiguity surrounding hate speech by collecting and analyzing existing definitions from the literature. We organize these definitions into a taxonomy of 14 conceptual elements—building blocks that capture different aspects of hate speech definitions, such as references to the target of hate. At the experimental level, we employ the collection of definitions in a systematic zero-shot evaluation of three LLMs, on three hate speech datasets representing different types of data (synthetic, human-in-the-loop, and real-world). We find that choosing different definitions, i.e., definitions with a different degree of specificity in terms of encoded elements, impacts model performance, but this effect is not consistent across all architectures.

A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance

Ensuring the safe deployment of AI systems is critical in industry settings where biased outputs can lead to significant operational, reputational, and regulatory risks. Thorough evaluation before deployment is essential to prevent these hazards. Red-teaming addresses this need by employing adversarial attacks to develop guardrails that detect and reject biased or harmful queries, enabling models to be retrained or steered away from harmful outputs. However, red-teaming techniques are often limited, and malicious actors may discover new vulnerabilities that bypass safety fine-tuning, underscoring the need for ongoing research and innovative approaches. Notably, most red-teaming efforts focus on harmful or unethical instructions rather than addressing social bias, leaving this critical area under-explored despite its significant real-world impact, especially in customer-facing AI systems. We propose two bias-specific red-teaming methods, Emotional Bias Probe (EBP) and BiasKG, to evaluate how standard safety measures for harmful content mitigate bias. For BiasKG, we refactor natural language stereotypes into a knowledge graph. and use adversarial attacking strategies to induce biased responses from several open- and closed-source language models. We find our method increases bias in all models, even those trained with safety guardrails. Our work emphasizes uncovering societal bias in LLMs through rigorous evaluation, addressing adversarial challenges to ensure AI safety in high-stakes industry deployments.

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Online Misogyny Against Female Candidates in the 2022 Brazilian Elections: A Threat to Women's Political Representation?

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Online Misogyny Against Female Candidates in the 2022 Brazilian Elections: A Threat to Women's Political Representation?

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Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction | VIDEO

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