Factoring Hate Speech: A New Annotation Framework to Study Hate Speech in Social Media (2023)

Abstract: In this work we propose a novel annotation scheme which factors hate speech into five separate discursive categories. To evaluate our scheme, we construct a corpus of over 2.9M Twitter posts containing hateful expressions directed at Jews, and annotate a sample dataset of 1,050 tweets. We present a statistical analysis of the annotated dataset as well as discuss annotation examples, and conclude by discussing promising directions for future work.

## introduction social media has come to constitute a space for the propagation of hostility (see @xcite and provides fertile grounds for the radicalization of individuals in support of violent extremist groups @xcite . much research has been devoted to automatically identifying hate speech in online forums @xcite @xcite along with factors that facilitate its propagation @xcite , and classifying different forms of hateful and abusive content @xcite . however, the very concept of "hate" and its presence in written text is somewhat illusive and amorphous @xcite ; therefore, some efforts have been made to define different typologies of hate speech @xcite @xcite . in this work, we address this important subject through the specific case of hate speech directed towards jews in twitter posts. rather than attempting to classify hate speech into different types, we present a novel annotation scheme which factors hate speech into a comprehensive set of separate † both authors contributed equally to this work. this factorization aims to achieve several goals: first, make the annotation process more focused and accurate, by decomposing the amorphous and ambiguous concept of "hate" into more specific and narrowly defined discourse aspects, rendering the annotation process more objective. second, it allows exploring and analyzing hate speech across these different aspects, hopefully leading to a deeper understanding of its complexities, variety, and nuance. furthermore, this set of aspects defines various possible distinct combinations, each of which encodes a different and unique configuration of hate speech. although this annotation scheme was designed to capture and characterize hate speech directed towards jews, with the exception of one group-specific aspect, it is general enough to be applied to any other group-directed hate speech. we constructed a corpus of twitter conversations in english containing over 2.9m tweets, collected through twitter api v2. in order to evaluate our annotation scheme on real twitter posts, we used it to annotate a sample of 1,050 tweets taken from the corpus. we present a quantitative analysis of the annotated dataset, as well as a qualitative one (through the use of some examples). we conclude by discussing several directions to extend and develop our work. content warning: this document contains some examples of hateful content. this is strictly for the purpose of enabling this research. please be aware that this content could be offensive and cause you distress. ## tweets corpus the tweets were extracted through the twitter api v2 using the tweepy python module foot_0 . we applied for, and were granted, an academic research access to the api foot_1 , which offers a full-archive access to public data posted on the platform, going back to april 2006. to increase the likelihood of retrieving tweets that contain expressions of hate towards jews, we used two types of keyword-based filters in our queries: neutral keywords and racial keywords. the neutral stop list -containing 14 words -was compiled from keywords referenced in previous studies @xcite . the racial stop list -containing 28 words and expressions -was compiled using the hatebase database, a multilingual lexicon for racial terms foot_2 , by extracting from the database all the english terms pertaining to jews and judaism that had at least one sighting. following preliminary experimentation with the api directed at increasing the chances of retrieving a conversation (thread) containing jew-related hate expressions, we decided to focus on collecting conversations which stemmed from a "source" tweet (a new post rather than a reply to another tweet) adhering to our keyword filters. we devised the following 2-step process. given a specific date (24-hour interval) and a specific keyword filter: for each date between july 1 st 2018 and june 30 th 2022 (defining a period of exactly 4 years), we applied the procedure with the racial keywords filter and collected as many tweets as possible. then, we applied the same procedure with the neutral keyword filter to collect a similar number of tweets from the same date foot_3 . this was done in order to keep the corpus as balanced as possible between the two types of keyword filters. the result is a large corpus of twitter conversations started between july 1 st 2018 and june 30 th 2022, segmented by the type of the filter applied to the conversation's source tweet (neutral or racial jews-related keywords). aside from the text itself, the corpus includes additional meta-data for each tweet: tweet id, conversation id, posting date, reply-to id (if the tweet was written as a reply to another tweet), tweet statistics (retweets, replies, likes, quotes and views), place & country (if available), author id (a unique identifier for the author of the tweet) and author statistics (followers, verified status). note that the "conversation id" and "reply-to id" fields allow a complete hierarchical reconstruction of a conversation given any tweet from that conversation. statistics for the corpus are given in the five categories are: these discursive categories not only encompass substantive elements of hate, such as contempt and prejudice, but also address the manner in which negative discourse is conveyed, including abusive language and incitement of violence. all the categories, except the fifth (holocaust denial), are general and may naturally be applied to other groups besides our target group (jews). in addition, while the categories encode separate aspects of hateful discourse, they may conjointly characterize the same expression; for example, a post can be abusive while also expressing prejudice. consequently, the annotation scheme defines a multi-label classification task. ## conclusion in this paper, we address the task of capturing and characterizing hate speech directed towards jews in twitter posts. for that purpose, we devised a novel annotation scheme that encodes five different aspects of hate speech, four of which are not specific to our target group (jews), allowing us to factorize the generally amorphous concept of "hate" into more concretely defined aspects. we utilized the twitter api v2 to collect and assemble a corpus of twitter conversations in english containing over 2.9m tweets, using two types of keyword filters (neutral and racial) to maximize the likelihood of retrieving tweets that contain expressions of hate towards jews. we then used our annotation scheme to annotate a sample of 1,050 tweets, and demonstrated its potential contribution through select examples. we intend to make all of these resources (tweet corpus, annotation guidelines and sample dataset) available to the research community. we are currently engaged in an ongoing effort to train additional annotators and use our assembled tweet corpus to produce a large and comprehensive annotated dataset. we are also working -in parallel -on assembling and annotating a similar corpus for muslim-related hateful expressions. another direction we are currently pursuing is taking advantage of the fact that the corpus is comprised of complete twitter conversations, to annotate expressions of hate in the context of the conversation which the tweet is a part of (rather than just based on the content of the tweet itself). for example, replying to a hateful post with strong agreement may be considered as hate speech only if the context (preceding posts) is taken into account. using the hierarchical structure of the conversation will allow not only encoding such cases, but also modelling the dynamics of hate speech as it progresses through the conversation and over time. in addition, we plan to utilize the tweet corpus to explore counter-hate speech @xcite @xcite . as these types of expressions are reactive by nature, complete twitter conversations are instrumental in addressing and analyzing them. augmenting hate-speech annotated twitter conversations with counter-hate annotation will allow us to explore the inter-changing dynamics of hate and counter-hate speech, as well as which kinds of counter messages are tailored to the different hate categories. we might find for example that the effective counter messages for abusive speech are those that attack the user, while most effective counter messages for prejudice deliver data and facts to contradict the prejudiced beliefs.