Multilingual Cross-domain Perspectives on Online Hate Speech

In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content. Analysis of the multilingual corpora shows that the different contexts share certain characteristics in their hateful rhetoric. To expose the main features, we have focused on text classification, text profiling, keyword and collocation extraction, along with manual annotation and qualitative study.Comment: 24 page

HATE SPEECH ON SOCIAL MEDIA: A CASE STUDY OF BLASPHEMY IN INDONESIAN CONTEXT

Many scholars have conducted research on hate speech, spanning from hate speech delivery tactics to the negative consequences they create, as well as the role of technology in the dissemination of hate speech on social media. However, research on hate speech categories and degrees is still relatively unexplored. As a result, the purpose of this research is to uncover the strategies and levels of hate speech on social media, primarily YouTube channels, in response to the Minister of Religion's comments about the sound of mosque loudspeakers that need to be adjusted in volume. This comment has generated both positive and negative reactions in Indonesian society. This research looks into netizen comments in the comments column on the YouTube channel that carries the statement. Purposive sampling was used to select 300 comments from among the 840 comments in the comments column. For the purposes of this study, the sample was obtained in the form of comments containing hate speech. The data was then analyzed using content analysis, in which the data was categorized and categorized according to hate speech categories. According to the study's findings, there are three types of hate speech in netizen comments: early warning, dehumanization and demonization, and violence and incitement. Early warning is the most common type of hate speech, followed by violence and hostility, as well as dehumanization and demonization. Due to cultural influences and contrasts in rank and power between the commentator and the person who is the subject of the hate speech, hate speech delivered by Indonesian netizens tends to be dominated by disagreement, negative character, and action

The Neurocognitive Process of Digital Radicalization: A Theoretical Model and Analytical Framework

Recent studies suggest that empathy induced by narrative messages can effectively facilitate persuasion and reduce psychological reactance. Although limited, emerging research on the etiology of radical political behavior has begun to explore the role of narratives in shaping an individual’s beliefs, attitudes, and intentions that culminate in radicalization. The existing studies focus exclusively on the influence of narrative persuasion on an individual, but they overlook the necessity of empathy and that in the absence of empathy, persuasion is not salient. We argue that terrorist organizations are strategic in cultivating empathetic-persuasive messages using audiovisual materials, and disseminating their message within the digital medium. Therefore, in this paper we propose a theoretical model and analytical framework capable of helping us better understand the neurocognitive process of digital radicalization

You are a Bot! -- Studying the Development of Bot Accusations on Twitter

The characterization and detection of bots with their presumed ability to manipulate society on social media platforms have been subject to many research endeavors over the last decade. In the absence of ground truth data (i.e., accounts that are labeled as bots by experts or self-declare their automated nature), researchers interested in the characterization and detection of bots may want to tap into the wisdom of the crowd. But how many people need to accuse another user as a bot before we can assume that the account is most likely automated? And more importantly, are bot accusations on social media at all a valid signal for the detection of bots? Our research presents the first large-scale study of bot accusations on Twitter and shows how the term bot became an instrument of dehumanization in social media conversations since it is predominantly used to deny the humanness of conversation partners. Consequently, bot accusations on social media should not be naively used as a signal to train or test bot detection models.Comment: 11 pages, 7 figure

Examining Dehumanization Through the "Political Brain Perspective": Towards a Minimal Neuropolitical Theory for Hyperdiverse Societies

What cognitive conditions need to be in place in order for cooperation, and potentially, solidarity, to exist in hyperdiverse societies? What aspects of our social human brain are indispensible when it comes to achieving shared goals in divided liberal democracies? This dissertation singles out the dehumanized perception and categorization of out-groups as one of the most decisive disruptors of political cooperation. I develop an interdisciplinary model – the “Political Brain Perspective” (PBP) – that combines political theory, political science and social neuroscience insights to advance my argument about dehumanization in both domestic and International political contexts. I argue that dehumanized perception at the brain level is politically troublesome because it disables an important social brain function called mentalizing, which is foundational for both basic political transactions and more complex feelings such as empathy. I show how this is relevant in regard to the neuropolitical duties public representatives owe to their constituents in a diverse liberal democracy, and further, how various liberal traditions such as social contract, multiculturalism and human rights theories have hitherto ignored dehumanization as a fundamental disruptor to any political cooperative process. At the international level, I examine the potential for dehumanization within civilizational discourses in history, with a particular focus on the post-Cold War distinction between “civilized” and “barbarians”. I show that in the international context of genocide, intergroup conflict and identity politics, dehumanizing categories not only diminish the cognitive reasoning and mentalizing abilities of the dehumanizer, but also have an intense impact on the dehumanized, in the form of reciprocal dehumanization and retributive violence. Based on the epistemological premises of the PBP, I contend that a minimal neuropolitical theory of cooperation ought not to prioritize an ontological concept of human dignity but instead treat the ascription of humanness as an interpersonal brain mechanism. The brain data, in other words, can only tell us what our brains do when engaging in politics, not who we are as political beings in an essentialist way. In sum, this dissertation highlights the need for political scientists to pay attention to the neuronal mechanisms underlying dehumanization, and to distinguish it from other forms of exclusion and prejudice as a fundamental brain ability in its own right

Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning

Detecting problematic content, such as hate speech, is a multifaceted and ever-changing task, influenced by social dynamics, user populations, diversity of sources, and evolving language. There has been significant efforts, both in academia and in industry, to develop annotated resources that capture various aspects of problematic content. Due to researchers' diverse objectives, the annotations are inconsistent and hence, reports of progress on detection of problematic content are fragmented. This pattern is expected to persist unless we consolidate resources considering the dynamic nature of the problem. We propose integrating the available resources, and leveraging their dynamic nature to break this pattern. In this paper, we introduce a continual learning benchmark and framework for problematic content detection comprising over 84 related tasks encompassing 15 annotation schemas from 8 sources. Our benchmark creates a novel measure of progress: prioritizing the adaptability of classifiers to evolving tasks over excelling in specific tasks. To ensure the continuous relevance of our framework, we designed it so that new tasks can easily be integrated into the benchmark. Our baseline results demonstrate the potential of continual learning in capturing the evolving content and adapting to novel manifestations of problematic content

Explaining the distribution of implicit means of misrepresentation:A case study on Italian immigration discourse

This study analyzes Fillmore's frames in a large corpus of Italian news headlines concerning migrations, dating from 2013 to 2021 and taken from newspapers of diverse ideological stances. Our goal is to assess whether, how, and why migrants' representation varies over time and across ideological stances. Our approach combines corpus-assisted critical discourse analysis with cognitive linguistics. We present a new methodology that exploits SOCIOFILLMORE, a tool integrating a novel Natural Language Processing model for automatic frame annotation into a web-based user interface for exploring frame-annotated corpora. In our corpus, the frequency distribution of frames varies over time according to detectable contextual factors. Across political stances, instead, the most frequent frames remain more constant: both right-winged and left-winged news providers contribute to reifying migrants into non-agentive entities. Further, in religious (Christian) press migrants are given a more humanizing depiction, but they still often appear in non-agentive roles. The distributions of frames can be explained by the fact that the latter act as indirect, routinized, and implicit means of (mis)representation. We suggest that framing entails inferential operations that take place unconsciously and can therefore escape the cognitive screening not only of those who receive discourse, but also of those who (re)produce it.</p