Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted Religious Hatred in Arabic Social Media: Analysis, Detection, and Personalization(2023-05) Albadi, Nuha; Mishra, ShivakantMiddle Eastern societies have long suffered from civil wars and domestic tensions that are partly caused by conflicting religious beliefs. This thesis examines the extent of religious hate in Arabic social media, evaluates the impact of automated accounts (i.e., bots) and personalized recommendation algorithms on its spread, and investigates social computing methods for automatically recognizing Arabic-language content and bots promoting religious hatred. First, the thesis addresses the scarcity of Arabic resources in the field by creating two publicly available, annotated Arabic datasets for Twitter and YouTube through crowdsourcing. It then presents a comprehensive analysis highlighting the prevalence of religious hatred on Arabic social networks, the most targeted religious groups, the unique characteristics of perpetrators, and the distinctions between Twitter and YouTube in terms of hate speech volume and targeted groups. Based on gathered insights, it then develops and evaluates several supervised machine learning models to automatically and efficiently detect hateful content. This thesis also contributes new insights into the role of Arabic-language bots in spreading religious hatred on Twitter and introduces a novel regression model tailored to detect Arabic-tweeting bots. Finally, the thesis audits YouTube’s recommendation algorithm to assess the effect of personalization based on demographics and watch history on the extent of hateful content recommended to users. The research presented in this thesis offers practical implications for platform designers to facilitate enforcing their policy against hate and malicious automation and contributes to the broader effort to combat online radicalization.32 0Item Restricted Deep Learning Methods to Investigate Online Hate Speech and Counterhate Replies to Mitigate Hateful Content(2025-05-15) Albanyan, Abdullah; Blanco, Eduardo; Albert, MarkHateful content and offensive language are commonplace on social media platforms. Many surveys prove that high percentages of social media users experience online harassment. Previous efforts have been made to detect and remove online hate content automatically. However, removing users’ content restricts free speech. A complementary strategy to address hateful content that does not interfere with free speech is to counter the hate with new content to divert the discourse away from the hate. In this dissertation, we complement the lack of previous work on counterhate arguments by analyzing and detecting them. Firstly, we study the relationships between hateful tweets and replies. Specifically, we analyze their fine-grained relationships by indicating whether the reply counters the hate, provides a justification, attacks the author of the tweet, or adds additional hate. The most obvious finding is that most replies generally agree with the hateful tweets; only 20% of them counter the hate. Secondly, we focus on the hate directed toward individuals and detect authentic counterhate arguments from online articles. We propose a methodology that assures the authenticity of the argument and its specificity to the individual of interest. We show that finding arguments in online articles is an efficient alternative compared to counterhate generation approaches that may hallucinate unsupported arguments. Thirdly, we investigate the replies to counterhate tweets beyond whether the reply agrees or disagrees with the counterhate tweet. We analyze the language of the counterhate tweet that leads to certain types of replies and predict which counterhate tweets may elicit more hate instead of stopping it. We find that counterhate tweets with profanity content elicit replies that agree with the counterhate tweet. This dissertation presents several corpora, detailed corpus analyses, and deep learning-based approaches for the three tasks mentioned above.54 0