S afe S peech: A Comprehensive and Interactive Tool for Analysing Sexist and Abusive Language in Conversations (2025)

Abstract: Detecting toxic language, including sexism, harassment, and abusive behaviour, remains a critical challenge, particularly in its subtle and context-dependent forms. Existing approaches largely focus on isolated message-level classification, overlooking toxicity that emerges across conversational contexts. To promote and enable future research in this direction, we introduce *SafeSpeech*, a comprehensive platform for toxic content detection and analysis that bridges message-level and conversation-level insights. The platform integrates fine-tuned classifiers and large language models (LLMs) to enable multi-granularity detection, toxic-aware conversation summarization, and persona profiling. *SafeSpeech* also incorporates explainability mechanisms, such as perplexity gain analysis, to highlight the linguistic elements driving predictions. Evaluations on benchmark datasets, including EDOS, OffensEval, and HatEval, demonstrate the reproduction of state-of-the-art performance across multiple tasks, including fine-grained sexism detection.

## introduction the dissemination of toxic content has become a pervasive issue across digital communication platforms. toxic language, including sexism, harassment, and abusive behaviour, is not only widespread but also often amplified by the ease of dissemination in online environments. while explicit forms of harmful language, such as direct insults or threats, have been the focus of much research @xcite , there is increasing awareness that toxicity frequently manifests in more subtle or contextdependent forms. subtle toxic behaviours include 1 safespeech demo website coercive language, controlling speech, and microaggressions @xcite . these may seem benign in isolation but reveal their toxicity through recurring patterns in conversations, as observed in contexts such as violence against women and girls @xcite . recent advances, such as the edos dataset for fine-grained sexism detection @xcite and large language models (llms) capable of analyzing multi-turn dialogues @xcite , highlight the potential for bridging this gap. yet, existing approaches to toxic content detection mainly operate at the message level @xcite @xcite , using classifiers trained to identify the harmful language in isolated text @xcite . and to date, there is no comprehensive platform that integrates message-level classification with conversation-level analysis to systematically evaluate and benchmark model performance across these dimensions. therefore, we introduce safespeech, a unified platform designed for toxic content detection and analysis. safespeech bridges the gap between message-level classification and conversation-level analysis, providing users with the tools to evaluate both explicit and context-dependent forms of toxicity. the platform supports a broad range of tasks, from granular single-message classification to the contextual understanding of multi-turn conversations. it leverages recent advances in dataset availability @xcite @xcite and large language models (llms) capable of processing extended sequences and capturing conversational context. safespeech incorporates prompt-based customization, enabling users to apply tailored prompt templates for classification tasks. to better understand the models' prediction, safespeech integrates a soft-explainability mechanism. this module, based on perplexity gain analysis @xcite , identifies and visually highlights the phrases that most contributed to a classification output. the platform extends its capabilities with a toxicaware summarization module designed for long, multi-turn conversations. by combining semantic chunking with instruction-based summarization @xcite , this module identifies recurring or overlapping topics, ensuring that semantically related content is grouped together, even when interrupted by unrelated dialogue, and produces concise summaries. these summaries are conditioned on toxic classification outputs, to explicitly highlight harmful content. in addition to summarization, safespeech includes a persona analysis module that goes beyond detecting toxic content to characterize individuals' behavioural tendencies. existing methodologies offering automated persona analysis for conversations typically provide only general predictions @xcite @xcite . in contrast, our platform represents the first approach to leverage the big five personality traits framework @xcite and toxic-aware summaries to provide trait-based insights conditioned on toxic content classifications. safespeech has been thoroughly evaluated across diverse datasets and practical use cases. it provides state-of-the-art model performance within benchmark tasks for sexism, abusive, and hate speech detection, along with advanced tools for conversation summarization and persona analysis. the contributions of this paper can be summarized as follows: ## related work existing approaches to toxic content detection mainly operate at the message level @xcite @xcite @xcite @xcite , which use classifiers to identify the harmful language in the isolated text without considering the complicated interactions in conversation settings. these systems commonly framed toxic content detection as sentence classification, then followed by extensive feature engineering and supervised training of statistical machine learning models or deep neural networks @xcite . perspective api @xcite is based on a bert model pre-trained on "subword" sequences (utf-8 bytes) and thus is capable of multilingual hate speech detection. camacho-collados et al. ## conclusion and future work safespeech is the first platform to provide tools for conveniently analysing hate speech or violence against women and girls in a conversational format. it connects with state-of-the-art supervised fine-tuned models and large language models, offering a comprehensive and interactive interface for researchers to test their new ideas without the need for coding. in future work, we will try to expand the platform and make it easy to use for the general public, making it an accessible tool for social media moderation.