Arjun Arunasalam (Purdue University), Habiba Farrukh (University of California, Irvine), Eliz Tekcan (Purdue University), Z. Berkay Celik (Purdue University)

Refugees form a vulnerable population due to their forced displacement, facing many challenges in the process, such as language barriers and financial hardship. Recent world events such as the Ukrainian and Afgan refugee crises have centered this population in online discourse, especially in social media, e.g., TikTok and Twitter. Although discourse can be benign, hateful and malicious discourse also emerges. Thus, refugees often become targets of toxic content, where malicious attackers post online hate targeting this population. Such online toxicity can vary in nature; e.g., toxicity can differ in scale (individual vs. group), and intent (embarrassment vs. harm), and the varying types of toxicity targeting refugees largely remain unexplored. We seek to understand the types of toxic content targeting refugees in online spaces. To do so, we carefully curate seed queries to collect a corpus of ∼3 M Twitter posts targeting refugees. We semantically sample this corpus to produce an annotated dataset of 1,400 posts against refugees from seven different languages. We additionally use a deductive approach to qualitatively analyze the motivating sentiments (reasons) behind toxic posts. We discover that trolling and hate speech are the predominant toxic content that targets refugees. Furthermore, we uncover four main motivating sentiments (e.g., perceived ungratefulness, perceived fear of safety). Our findings synthesize important lessons for moderating toxic content, especially for vulnerable communities.

View More Papers

DorPatch: Distributed and Occlusion-Robust Adversarial Patch to Evade Certifiable...

Chaoxiang He (Huazhong University of Science and Technology), Xiaojing Ma (Huazhong University of Science and Technology), Bin B. Zhu (Microsoft Research), Yimiao Zeng (Huazhong University of Science and Technology), Hanqing Hu (Huazhong University of Science and Technology), Xiaofan Bai (Huazhong University of Science and Technology), Hai Jin (Huazhong University of Science and Technology), Dongmei Zhang…

Read More

WIP: A Trust Assessment Method for In-Vehicular Networks using...

Artur Hermann, Natasa Trkulja (Ulm University - Institute of Distributed Systems), Anderson Ramon Ferraz de Lucena, Alexander Kiening (DENSO AUTOMOTIVE Deutschland GmbH), Ana Petrovska (Huawei Technologies), Frank Kargl (Ulm University - Institute of Distributed Systems)

Read More

DEMASQ: Unmasking the ChatGPT Wordsmith

Kavita Kumari (Technical University of Darmstadt, Germany), Alessandro Pegoraro (Technical University of Darmstadt), Hossein Fereidooni (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Read More