Abstract
The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the users’ data can be weaponized in political campaigns, which many times trigger hate speech and anti-immigration views. Hate speech detection is a challenging task due to the different sources of hate that can have an impact on the language used, as well as the lack of relevant annotated data. To tackle this, we collected and manually annotated an immigration-related dataset of publicly available Tweets in UK, US, and Canadian English. In an empirical study, we explored anti-immigration speech detection utilizing various language features (word n-grams, character n-grams) and measured their impact on a number of trained classifiers. Our work demonstrates that using word n-grams results in higher precision, recall, and f-score as compared to character n-grams. Finally, we discuss the implications of these results for future work on hate-speech detection and social media data analysis in general.
Highlights
Social Networking Sites (SNS) have been established as an important aspect of people’s lives in modern society, seeping into many aspects of our everyday life, both private and public
The results show that the second class had a more uniform vocabulary, where the words are more evenly distributed in the set, which is true for the top 25 occurrences
While being disturbing, provide an opportunity to study and better understand mechanisms behind such discourse, which can be utilized to improve classification methods. They can raise awareness regarding the absence of privacy on social media, where disclosing antagonistic opinions can have far-reaching consequences, even in form of imprisonment [39]
Summary
Social Networking Sites (SNS) have been established as an important aspect of people’s lives in modern society, seeping into many aspects of our everyday life, both private and public. The presence of anti-immigration sentiments both in private and public can be motivated by several reasons with terrorist attacks being one of the extreme examples, often having profound influence on rising anti-immigration sentiments [4]. Politics is another influencing factor, which can spark debates about immigration, and openly display anti-immigration attitudes. Khan [18] analyzed what motivates user participation and consumption on YouTube, with regards to uses and gratification framework, and distinguished five main factors that influence user engagement on this platform, namely: (i) information seeking, (ii) giving information, (iii) self-status seeking, (iv) social interaction, and (v) relaxing entertainment
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