H ate PRISM : Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation (2025)

Abstract: Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hateful content persists as a significant challenge. Existing approaches primarily rely on reactive measures such as blocking or suspending offensive messages, with emerging strategies focusing on proactive measurements like detoxification and counterspeech. In our work, which we call HATEPRISM, we conduct a comprehensive examination of hate speech regulations and strategies from three perspectives: country regulations, social platform policies, and NLP research datasets. Our findings reveal significant inconsistencies in hate speech definitions and moderation practices across jurisdictions and platforms, alongside a lack of alignment with research efforts. Based on these insights, we suggest ideas and research direction for further exploration of a unified framework for automated hate speech moderation incorporating diverse strategies.

## introduction ai continues to advance rapidly across various domains, offering diverse applications. among these, leveraging ai for societal positive impact @xcite is becoming an important direction to explore. specifically, in the field of nlp @xcite , one of the important societal applications lies in mitigating digital violence @xcite . digital violence persists as a pressing issue in online social environments, posing tangible risks to users @xcite . it involves using information and communication technologies to hurt, humiliate, disturb, frighten, exclude, and victimize individuals. this often results in increased anxiety, sadness, tension, and a loss of motivation at work @xcite . it includes harmful online activities such as abusive behavior, hate speech, toxic speech and offensive language, significantly affecting an individual's professional and social effectiveness and efficiency @xcite . traditional automated moderation methods typically involve measures such as blocking or suspending accounts that disseminate hateful messages @xcite . major technology companies, including meta and x, have implemented these strategies to manage hate speech. however, such measures have proved insufficient in curbing hateful sentiments over the long term @xcite . alternatives such as counterspeech have gained traction as promising strategies to mitigate hate speech by engaging in dialogue aimed at challenging harmful narratives @xcite . furthermore, text detoxification represents an approach intended to reduce the toxicity of communications while maintaining the original message (nogueira dos @xcite . despite their potential, these approaches have yet to be widely adopted as part of social media platforms' moderation strategies. ## methodology in hateprism, our approach incorporates a strategic analysis of hate speech regulation & mitigation through three primary perspectives: countryspecific regulations, social media platforms' policies and nlp research approaches. for each of the three perspectives, we developed specific selection criteria to obtain representative samples and crafted a series of questions to analyze and gain deeper insights into each area. this dual strategy ensures a comprehensive examination of the regulatory landscape and the effectiveness of various moderation techniques. furthermore, our analysis also aims to examine three common approaches of content moderation: blocking/suspending hateful content, detoxification of toxic language, and counter of hate speech to engage users constructively. hence, these moderation techniques were also considered during curation. questions for each of the three dimensions, we first tunneled down categories and then brewed relevant questions for each category (refer figures 3, 4, 5). these two steps were specifically performed to audit different perspectives in a relevant and robust manner. note that the surveys used in this study were carefully designed and answered by a group of qualified researchers, including phd students and postdoctoral fellows, who have expertise in social media policies and online hate speech regulation. information regarding social media platform policies were gathered through a thorough examination of policy documents and guidelines available on the platforms' official websites. ## results and analysis in this section, we will discuss the outcomes of our investigation across three key areas aimed at mitigating hate speech: country regulations, platform policies and research datasets. we have summarized our analysis quantitatively in figures 6 , 7 and 8 and have also uploaded the full list of questionnaire; as mentioned previously. ## conclusion and future directions the challenges identified by hateprism in addressing hate speech from governmental, platform, and research angles are as following. firstly, the lack of a universally accepted definition of hate speech complicated the development of consistent regulations across countries. while most nations have a definition of hate speech, only a third defined it specifically for the online environment. this underscored the need to raise awareness about online hate and its mitigation. however, some of the countries are proactive in hate speech moderation methods development. secondly, social media platforms showed policy inconsistencies, which hindered effective content moderation. a fifth of the platforms failed to adapt hate definitions to local languages and cultures, and moderation typically focused on banning rather than proactive strategies. thirdly, most nlp research did not align with platform or regulatory guidelines, often reusing outdated definitions from previous computer science studies. moreover, many studies did not explore proactive measures such as counterspeech or detoxification in operational settings. such data labeling could improve online hate mitigation. ultimately, collaboration between platforms, governments, and researchers is essential to create dynamic moderation frameworks. aligning definitions and promoting proactive strategies will lead to more effective solutions for combating online hate. the further exploration of proactive moderation pipeline which consists of thoughtful combination of text detoxification, counter speech generation, other preventing measures, and preparing such datasets for automatic methods development should be a frontier for future research.