Abstract
Harmful language is frequent in social media, in particular in spaces which are considered anonymous and/or allow free participation. In this paper, we analyze the language in a Telegram channel populated by followers of former US President Donald Trump. We seek to identify the ways in which harmful language is used to create a specific narrative in a group of mostly like-minded discussants. Our research has several aims. First, we create an extended taxonomy of potentially harmful language that includes not only hate speech and direct insults (which have been the focus of existing computational methods), but also other forms of harmful speech discussed in the literature. We manually apply this taxonomy to a large portion of the corpus, including the time period leading up to and the aftermath of the January 2021 US Capitol riot. Our data gives empirical evidence for harmful speech, such as in/out-group divisive language and the use of codes within certain communities, that have not often been investigated before. Second, we compare our manual annotations of harmful speech to several automatic methods for classifying hate speech and offensive language, namely list-based and machine-learning-based approaches. We find that the Telegram data sets still pose particular challenges for these automatic methods. Finally, we argue for the value of studying such naturally-occurring, coherent data sets for research on online harm and how to address it in linguistics and philosophy.
Highlights
Journal of OpenDigital media can cause harm in different ways
We presented a new corpus of a channel of the instant messaging platform Telegram, which we chose for its large potential for harmful language
We argued for a broad notion of online harmful speech, which includes direct attacks and pejorative expressions, and divisive rhetoric and code words which seek to establish and reinforce an in-group identity and sense of community in order to justify attacks against target groups
Summary
Our goal is to establish an empirical basis for the varieties of harmful speech in online communication rather than arguing about their specific effects To this end, we seek to provide a comprehensive taxonomy of various forms of expressions used to cause harm as outlined under (1)–(5). Category III includes expressions that are being used derogatively, but are not inherently pejorative in their conventional meaning This includes jokes and inventive forms of speech, which serve to put down specific individuals or groups (e.g., “DemoRATS”, “Commiefornia”), insulting metaphors (e.g., “they are a sickness”), as well as non-pejorative words when used pejoratively in context (e.g., “Jews”, “gay”). Our goal is to provide a qualitative and quantitative analysis of the corpus we gathered and provide a basis towards improving tools for automatic detection of online harmful speech
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