Austria

Wikipedia serves millions of readers daily with multimedia articles, but its content is not equally accessible to all. Visually impaired readers who rely on screen readers can access only a small fraction of images due to missing alt text.
We explore the potential of multimodal generative AI (GPT-4o) to generate high-quality alt text for Wikipedia images. Guided by accessibility best practices, we design four prompting strategies that incorporate writing guidelines and the textual context surrounding each image.
We evaluate these strategies through crowdsourcing, expert review, and a study involving blind and low-vision (BLV) readers. Crowd worker evaluations show that combining context and guidelines significantly improves alt-text quality, earning six times more majority votes than the baseline. Expert editors confirm these results and, through qualitative analysis, identify recurring issues related to accuracy, relevance, formatting, screen reader compatibility, and inclusive representation.
Interviews with BLV users reveal a consistent preference for context- and criteria-grounded alt text, along with concrete recommendations for improving description quality.
This work offers practical guidance for developing AI-assisted tools to help editors make Wikipedia more accessible.

ACL 2025

Can LLMs Improve Image Accessibility on Wikipedia?

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workshop paper

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

Malicious sockpuppet detection on Wikipedia is critical to preserving access to reliable information on the internet and preventing the spread of disinformation. Prior machine learning approaches rely on stylistic and meta-data features, but do not prioritise adaptability to author-specific behaviours. As a result, they struggle to effectively model the behaviour of specific sockpuppet-groups, especially when text data is limited. To address this, we propose the application of meta-learning, a machine learning technique designed to improve performance in data-scarce settings by training models across multiple tasks. Meta-learning optimises a model for rapid adaptation to the writing style of a new sockpuppet-group. Our results show that meta-learning significantly enhances the precision of predictions compared to pre-trained models, marking an advancement in combating sockpuppetry on open editing platforms. We release an updated dataset of sockpuppet investigations to foster future research in both sockpuppetry and meta-learning fields.

Detecting Sockpuppetry on Wikipedia Using Meta-Learning

In this paper, we present a thorough analysis of the impact of Large Language Models (LLMs) on Wikipedia, examining the evolution of Wikipedia through existing data and using simulations to explore potential risks. We begin by analyzing page views and article content to study the recent changes in Wikipedia and assess the impact of LLMs. Subsequently, we evaluate how LLMs affect various Natural Language Processing (NLP) tasks related to Wikipedia, including machine translation and retrieval-augmented generation (RAG). Our findings and simulation results reveal that Wikipedia articles have been affected by LLMs, with an impact of approximately 1\%-2\% in certain categories. If the machine translation benchmark based on Wikipedia is influenced by LLMs, the scores of the models may become inflated, and the comparative results among models could shift. Moreover, the effectiveness of RAG might decrease if the knowledge contaminated by LLMs. While LLMs have not yet fully changed Wikipedia's language and knowledge structures, we believe that our empirical findings signal the need for careful consideration of potential future risks.

Wikipedia in the Era of LLMs: Evolution and Risks

Algorithmic hate speech detection is widely used today. However, biases within these systems can lead to discrimination. This research presents an overview of bias mitigation strategies in the field of hate speech detection. The identified principles are grouped into four categories, based on their operation principles. A novel taxonomy of bias mitigation methods is proposed. The mitigation strategies are characterized based on their key concepts and analyzed in terms of their application stage and their need for knowledge of protected attributes. Additionally, the paper discusses potential combinations of these strategies. This research shifts the focus from identifying present biases to examining the similarities and differences between mitigation strategies, thereby facilitating the exchange, stacking, and ensembling of these strategies in future research.

A Comprehensive Taxonomy of Bias Mitigation Methods for Hate Speech Detection

The detection of sensitive content in large datasets is crucial for ensuring that shared and analysed data is free from harmful material. However, current moderation tools, such as external APIs, suffer from limitations in customisation, accuracy across diverse sensitive categories, and privacy concerns. Additionally, existing datasets and open-source models focus predominantly on toxic language, leaving gaps in detecting other sensitive categories such as substance abuse or self-harm. In this paper, we put forward a unified dataset tailored for social media content moderation across six sensitive categories: conflictual language, profanity,sexually explicit material, drug-related content, self-harm, and spam. By collecting and annotating data with consistent retrieval strategies and guidelines, we address the shortcomings of previous focalised research. Our analysis demonstrates that fine-tuning large language models (LLMs) on this novel dataset yields significant improvements in detection performance compared to open off-the-shelf models such as LLaMA, and even proprietary OpenAI models, which underperform by 10-15% overall. This limitation is even more pronounced on popular moderation APIs, which cannot be easily tailored to specific sensitive content categories, among others.

Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation

Algorithmic content moderation governs online speech on large-scale commercial platforms, often under the guise of neutrality. Yet, it routinely reproduces white, middle-class norms of civility and penalizes marginalized voices for unruly and resistant speech. This paper critiques the prevailing ‘pathological' approach to moderation that prioritizes sanitization over justice. Drawing on Marxist-feminist ethics, this paper advances three theses for the future of context-aware algorithmic moderation: (1) prioritizing participatory parity over civility, (2) incorporating identity- and context-aware analysis of speech; and (3) replacing purely numerical evaluations with justice-oriented, community-sensitive metrics. While acknowledging the structural limitations posed by platform capitalism, this paper positions the proposed framework as both critique and provocation, guiding regulatory reform, civil advocacy, and visions for mission-driven online content moderation serving digital commons.

From civility to parity: Marxist-feminist ethics for context-aware algorithmic

The consistently high prevalence of hate speech on the Internet continues to pose significant social and individual challenges. Given the centrality of social networks in public discourse, automating the identification of criminally relevant content is a pressing challenge. This study addresses the challenge of developing an automated system that is capable of classifying online comments in a criminal justice context and categorising them into relevant sections of the criminal code. Not only technical, but also ethical and legal requirements must be considered. To this end, 351 comments were annotated by public prosecutors from the Central Office for Combating Internet and Computer Crime (ZIT) according to previously formed paragraph classes. These groupings consist of several German criminal law statutes that most hate comments violate. In the subsequent phase of the research, a further 839 records were assigned to the classes by student annotators who had been trained previously.

A Novel Dataset for Classifying German Hate Spe

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.

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

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.

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