Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler (2022)

Abstract: Social platforms such as Gab and Parler, branded as ‘free-speech’ networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit.In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make 16.1% of Parler active users, and that they have distinct characteristics comparing to other user groups. We further complement our analysis by comparing the trends observed in Parler to those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level.

## introduction social platforms like twitter, facebook, and reddit have become a central communication channel for billions of users. 1 the immense popularity of social platforms resulted in a significant rise in the toxicity of the discourse, ranging from cyberbullying to explicit hate speech and calls for violence against individuals and groups @xcite @xcite . women, people of color, the lgbt community, muslims, immigrants, and jews are among the most targeted groups. recent studies report on a surge in islamophobia @xcite @xcite , antisemitism @xcite , xenophobia @xcite , hate of asians @xcite and hate crimes @xcite @xcite . facing an increased public and legislature scrutiny, mainstream social platforms (e.g., facebook, twitter, reddit) committed to a stricter enforcement of community standards, curbing levels of hate on the platform. 2 the stricter moderation of content drove many users into joining alternative social platforms such as parler and gab. touting their commitment to 'free speech' and 'no moderation' policy, these platforms attract users that were suspended from mainstream platforms, conspiracy theorists, extremists, unhinged users, free-speech advocates, political activists as well as others. user migration to parler and gab was not only grass-root. the platforms were promoted by prominent news anchors and political figures. for example, u.s. senator ted cruz (r-tx) tweeted "i'm proud to join @parler_app -a platform gets what free speech is all about -and i'm excited to be a part of it. let's speak. let's speak freely. and let's end the silicon valley censorship" (6/25/2020). sean hannity, a popular host and commentator on fox news, informed the viewers of his daily show that "i saw that the president had joined it. at least there is a place, it's like twitter, it's called parler, i have an account there... good for you because the president joined, because they are censoring him and dan scavino and everybody else" (1/8/2021). hate, brewing online, often spills to the streets @xcite @xcite . thus, defending 'hate speech' under the right for 'free speech' may result in very concrete actions in real life. the perpetrator of the pittsburgh synagogue shooting foot_3 was active on gab, referring to "kike infestation" and "the children of satan". his final post, minutes before opening fire in the synagogue, was "i can't sit by and watch my people get slaughtered. screw your optics, i'm going in.". similarly, the storming of the u.s. capitol on january 6, 2021 was found by the u.s. senate investigation committee to be encouraged and coordinated on parler @xcite . in this work we focus on parler, investigating the proliferation of hate speech on the platform, both on the post level and on the user level. we identify three distinct groups of users, denoted as hate mongers, standard users and hate flirts. we show significant differences between the groups in terms of language, emotion, activity level and role in the network. we further compare our results to the hateful dynamics reported for gab. ## related work a growing body of work studies the magnitude and the different manifestations of hate speech in social media @xcite @xcite , among others. here, we present an overview of the current literature in three different perspectives: (i) the detection of hate speech on the post level, (ii) the detection of hate-promoting users, and (iii) the characterization of hate speech on the platform level. post-level classification most previous works address the detection of hate in textual form. keywords and sentence structure in twitter and whisper were used in @xcite , demonstrating the limitations of a lexical approach. the use of code words, ambiguity and dog-whistling, and the challenges they introduce to text-based models were studied by @xcite @xcite . the detection of implicit forms of hate speech is addressed by @xcite which detects the use of hate code words (e.g., google, skype, bing and skittle to refer to black people, jews, chinese, and muslims, respectively) using an svm classifier based on bag-of-words. @xcite introduced a benchmark corpus of 22.5k tweets to study implicit hate speech. the authors presented baseline results over this dataset using jigsaw perspective, foot_5 svm, and different variants of bert @xcite . the use of demographic features such as gender and location in the detection of hate speech is explored by @xcite . user meta features, e.g., account age, posts per day, number of followers/friends, are used by @xcite . computational methods for the detection of hate speech and abusive language range from svm and logistic regression @xcite @xcite , to neural architectures. recently, transformer-based architectures @xcite @xcite @xcite @xcite achieved significant improvements over rnn and cnn models @xcite @xcite . in an effort to mitigate the need for extensive annotation some works use transformers to generate more samples, e.g., @xcite @xcite . @xcite integrate features from external resources to support the model performance. in order to account for the often elusive and coded language and for the unfortunate variety of targeted groups @xcite , a set of functional test was suggested by @xcite , allowing an quick evaluation of hate-detection models. classification of hate users characterizing accounts that are instrumental in the propagation of hate is gaining interest from the research community and industry alike, whether in order to better understand the social phenomena or in order to suspend major perpetrators instead of removing sporadic content. detection and characterization of hateful twitter and gab users was tackled by @xcite ; @xcite and @xcite , among others. an annotated dataset of a few hundreds twitter users was released as part of a shared task in clef 2021, see @xcite for an overview of the data and the submissions. @xcite intro-duced a user-level annotated dataset of 798 gab users which we use for evaluation and comparison. hate speech on parler and gab while most prior work focus on the manifestations of hate in mainstream platforms, a number of works do address alternative platforms such as gab and parler. two annotated gab datasets were introduced by @xcite and by @xcite . we use these datasets in this work as we compare parler to gab. focusing on users, rather than posts, @xcite experiment with an array of models for hate users classification. @xcite aims to understand what users join gab and what kind of content they share, while @xcite conduct a qualitative analysis studying gab's platform norms, given the lack of moderation. @xcite explore whether users seek out gab in order to express hate, or that the toxic attitude is adopted after joining the platform. the diffusion dynamics of the content posted by hateful and non-hateful gab users is modeled by @xcite and by @xcite . parler, launched in august 2018 and experiencing its impressive expansion of user base from late 2020, is only beginning to draw the attention of the research community. early works analysed the language in parler in several aspects such as qanon content @xcite , covid-19 vaccines @xcite , and the 2021 capitol riots @xcite . the first dataset of parler messages was introduced by @xcite , along with a basic statistical analysis of the data, e.g., the number of posts and the number of registered users per month, along with the most popular tokens, bigrams, and hashtags. we use this dataset in the current work to analyze hate speech on parler. @xcite used a list of predefined keywords (hate terms), assessing the level of hate-speech on the platform. our work differs from these works in a number of fundamental aspects. first, we combine textual and social (network) signals in order to detect both hateful posts and hate-promoting accounts. second, we suggest models that rely on state-of-the-art neural architectures and computational methods, while previous work detects hate speech by matching a fixed set of keywords from a predefined list of hate terms. furthermore, we provide a thorough analysis of the applicability of different algorithms, trained and fine-tuned on various datasets and tasks. third, we provide a broader context to our analysis of the proliferation of hate in parler, as we compare and contrast it to trends observed on gab. ## data in this section we describe the datasets used for this work -starting with a general overview of the platforms, then providing a detailed description of the datasets and the annotation procedure. ## methods in this work we are interested in the detection of hate, both on the post level and the account level. our interest in the post level classification is twofold. given an accurate classifier, we can: (a) approximate the hate degree in different aggregation levels -e.g., over the full network or and per user, and (b) use the post-level predictions to support training a user level classifier. a review of the various post level classifiers is provided in section 4.1 and our modifications to a diffusionbased model for user classification are presented in section 4.2. ethical considerations related to user classification are discussed at the end of section 7. ## analysis: the propensity of hate 6.1 hate on the post level taking our conservative approach, we find that the frequency of hate posting is higher in parler (3.29%), compared in gab (2.13%). however, we find that 13.95% of parler users share at least one hateful post -a significantly lower number compared to gab (18.58%). we find that 65.5% of the hate content in parler is posted as a reply to other parlays. this reflects a significant overrepresentation of replies compared with full corpus distribution (46.2% of posts are replies, see ## discussion ethical considerations analysing and modeling hate speech in a new social platform such as parler is of great importance. however, classifying users as hate mongers, based on the output of an algorithm, may result in marking users falsely (which may result in suspension or other measures taken against them). while we always opted for a conservative approach, as well as focusing on aggregated measures characterizing the trends of a platform, we note that user labeling should be carefully used, ideally involving a 'man-in-the-loop'. considering the annotation task -the annotation process did not include any information about the identity of the users. in addition, we warned our human annotators about the possible inappropriate and triggering content of the posts. we also make sure to remove users' information from the annotated dataset that we publish. ## conclusion and future work to the best of our knowledge, we present the first large-scale computational analysis of hate speech on parler, and provide a comparison to trends observed in the gab platform. we tag and share the first annotated parler dataset, containing 10k posts labeled by the level of hate they convey. we used this dataset to finetune a transformer model to be used to mark a seed set of users in a diffusion model, resulting in user-level classification. we find significant differences between hate mongers (hm) and other user groups: hm represent only 16.1% and 10% of the active users in parler and gab respectively. however, they generate 41.23% of the content in parler and 71.38% of the content in gab. we find that hm show higher engagement levels and they have significantly more followers and followees. other differences are manifested through the sentiment level expressed and the emotions conveyed. future work takes two trajectories: (i) comparison of the current results with a more traditional social platform (e.g., twitter); and (ii) an early detection of hate mongers -building a classifier to detect hate mongers based on their very first steps in the social platform.