Which Argumentative Aspects of Hate Speech in Social Media can be reliably identified? (2023)

Abstract: The expansion of Large Language Models (LLMs) into more serious areas of application, involving decision-making and the forming of public opinion, calls for a more thoughtful treatment of texts. Augmenting them with explicit and understandable argumentative analysis could foster a more reasoned usage of chatbots, text completion mechanisms or other applications. However, it is unclear which aspects of argumentation can be reliably identified and integrated by them. In this paper we propose an adaptation of Wagemans (2016)’s Periodic Table of Arguments to identify different argumentative aspects of texts, with a special focus on hate speech in social media. We have empirically assessed the reliability with which each of these aspects can be automatically identified. We analyze the implications of these results, and how to adapt the proposal to obtain reliable representations of those that cannot be successfully identified.

## introduction with the impressive advances obtained in large language models (llms), applications of automated language generation are quickly expanding to affect more and more areas of human activity, specially with the generalization of conversational chatbots. it is known that these models tend to amplify stereotypes, resulting in the naturalization of prejudices and finally the dehumanization of social groups in the form of hate speech. hate speech is a grave danger. the international convention on the elimination of all forms of racial discrimination states that hate speech "rejects basic human rights principles of human dignity and equality and seeks to degrade the position of individuals and groups in society's esteem" 1 . 1 united nations strategy and plan of action on hate through the amplification provided by social media and llms, its effects are also amplified, as it can deepen prejudice and stereotypes @xcite . that is why great efforts have been made to detect and neutralize it. the most common form of neutralization to date has been banning hate speech from public forums. however, this strategy collisions with the right to freedom of expression. in addition, it is usually implemented by resorting to human moderators who are exposed to toxic content for long workdays. automatic argumentation analysis enables alternatives to censorship like argument retrieval and organization or automatic generation of counterarguments. the recent developments of llms make these tasks more feasible. but, although they behave in a competent way from a purely conversational point of view, they do have not been designed to reason or argue. moreover, they do not seem to be able to prevent harmful effects beyond very shallow guardrails, which is a critical concern when dealing with hate speech. that is why it seems necessary to enhance them beyond pure unannotated text, to obtain a more nuanced treatment of the argumentative dimension of texts. the question remains, how can we know which argumentative aspects will be useful for llms to improve their performance in nuanced, risky tasks like automatic generation of counter-arguments against hate speech in social media? in this work we present the argumentation structure of hate messages online (asohmo), a protocol to annotate argumentative information in hate tweets, and an annotated dataset of tweets to train automatic classifiers. these annotations are an adaptation of @xcite 's proposal for hate speech in twitter, where much of the argumentation refers to implicit elements, and one finds typos, speech: detailed guidance on implementation for united nations field presences, 2020. the rest of the paper is organized as follows. in the next section we discuss relevant work, including the foundational wagemans (2016)'s proposal. section 3 describes the categories that we distinguish in our annotation framework, and in section 4 we present how they apply to hate speech in social media, more concretely, to the manual annotation of the hateval corpus @xcite . finally, in section 5 we show how llms can identify some argumentative components, but not others, with varying degrees of success. we analyze the causes of low success and propose how to adapt the definition of the target argumentative aspects to improve their reliability of annotation, both manual and automatic. ## relevant work there are many different proposals on how to model the argumentative aspects of texts, even if we only consider those aimed or used for computational application. we are not providing an exhaustive overview of approaches here, but just some examples to motivate and frame the model of argument that we present in this work. one of the main distinctions between proposals is whether they are general purpose or domain specific. domain-specific approaches propose tailored categories, like @xcite 's "background", "aim" or "comparison" for scientific papers, or @xcite 's "anecdote" or "statistics" for the argumentative analysis of editorials. they tend to achieve good inter-annotator agreement and good accuracy in automatic identification, but are not portable to different domains. general-purpose argumentation models have very different approaches. many computationoriented proposals are based on @xcite 's theory of practical argument. they distinguish between two main components of arguments, "conclusion" (also called "claim") and "fact" (also called "justification" or "premise"). they usually try to identify relations between components and between arguments, aiming to create a full argument tree that accounts for the argumentative structure of a text. this kind of model has been applied to essays @xcite or user-generated discourse @xcite . it is very general, thus easily portable to different domains. at the same time, it is not very stable, since inter-annotator agreement is not high, and the information it provides about the argument is not as rich as in the case of domain-specific approaches. another approach to modelling argument in texts are schemes. argument schemes are "patterns of informal reasoning" @xcite that "represent forms of argument that are widely used in everyday conversational argumentation" @xcite . argument schemes specify a pattern of reasoning and a set of critical questions oriented to test the defeasibility conditions on the pattern. this pattern and critical questions provide very insightful, actionable information about the argument, which can be later used for applications like building a counter-argument. several authors have adapted walton's schemes to specific purposes, even proposing alternatives to critical questions @xcite . the main drawback of these proposals is that the inventory of scheme is very profligate, and it has become clear that, identifying a scheme within a given text becomes quite difficult, both manually and automatically. ## a framework to identify argument components on twitter hate speech the goal of our argumentation model is to provide an argumentative analysis that can help expose the core of the reasoning supporting a hate message. we believe that this can help both humans and automatic models to better address hate speech. we are labeling two kinds of information: domain-specific and argumentative-general. domain-specific information allows to exploit particular characteristics of hate messages on twitter: they always mention a collective that is implicitly or explicitly associated with a negative property, action or consequence. argumentative-general structure is based on a simplification of wageman's proposal that is aimed to increase inter-annotator agreement. reaching acceptable levels of interannotation agreement is very important to our purpose, as it indicates that the annotation process can be systematized and possibly automatized. we created an annotation protocol foot_0 where both kinds of argumentative information are defined in a procedural manner. this protocol was applied by human analysts to annotate hate speech tweets, with five steps that are described as follows. the annotation team and environment are described in appendix a. ## the asohmo corpus we applied our argumentation model via the annotation protocol described in the previous section to the hateval 2019 corpus @xcite . focusing on argumentative tweets, we did not annotate tweets labeled as "aggressive", consisting mostly of abusive language (name callings, insults, exhortations to action and other types of attacks), nor tweets targeted against specific individuals or women, as they were almost exclusively abusive and non-argumentative. after these filters, a corpus of 970 tweets in english and 196 tweets in spanish remained. the dataset is released foot_2 for the free use of the scientific community, together with the scripts for reproducing experiments. ## automatic identification of arguments we conducted several experiments to assess the feasibility that llms can automatically identify different argumentative aspects. for each set of hyperparameters used, we finetuned the same language models using different random tweets for each partition, always respecting this proportion. we report the average of these three fine-tuned models' f1, precision and recall to detect or classify argument components. for multi-label classification, the macro average is calculated, otherwise, we report the score of the target class. we also report per-class f1 scores for the three possible types of premises: fact, value and policy. models. we fine-tuned the following llms: ## discussion of results we have seen that some argumentative aspects of hate speech in tweets can be successfully identified by large language models (llms), namely, whether a tweet is argumentative or not and justifi-cations, conclusions and the type of conclusions. this kind of information may be useful to provide an argumentative analysis of tweets, possibly for argument retrieval. it is probably also useful to guide the (semi-)automatic generation of some counter-narratives, like those that are aimed to question the justification or those aimed to some kinds of conclusions, like values or policies. domain-specific argument information, like collective and property, are not very successfully identified. different strategies, like named entity recognition approaches, may yield better results. pivots, aiming to identify the relation between justification and conclusions, and a key component to reconstruct wagemans's typology, cannot be successfully identified, either by humans or automatically. it seems that a different approach must be taken to identify them manually, possibly identifying all possible sequences of words that elicit a relation between justification and conclusion. these results will be instrumental for the annotation of a bigger annotated corpus, specially for spanish, and to integrate these concepts into llms. ## summary and future work we have presented an approach to determine which aspects of argumentative information from hate speech in social media is liable to be integrated into llms. we have adapted the analytic approach of an informal logic based on @xcite and have developed annotation guidelines which have then used to enrich a reference dataset for hate speech with argumentative information. we developed a robust annotation process and guidelines to obtain high agreement between annotators. indeed, an initial assessment of interannotator agreement, shows agreement above κ = .6 for most categories, except the most interpretative ones. considering we are dealing with usergenerated text, we find this a very hopeful scenario. we are also working on adapting the categories with more disagreement, like pivot, based on the patterns of the disagreemeent between annotators, so that in further annotation efforts they can be identified in a more reproducible ways, both by humans and automatic methods. we show to which extent it is possible for large language models to automatically identify the argumentative components, so that this kind of information can be integrated with purely data-driven approaches to enrich the analysis of text and produce more insightful, reasoned outputs. finally, the published dataset is also a contribution to the existing corpora of argument mining on social networks. it is publicly available at https: //github.com/asohmo/asohmo-dataset. for future work, we plan to annotate bigger corpora, focusing on improving reliability on difficult, yet potentially useful, components, like pivot. we also plan to add counter-narratives associated to each tweet and train models to automatically generate them. we want to assess to which extent the argumentative information helps in better generating automatic responses.