By Layla Hashemi, Edward Huang & Louise Shelley 

 Over 58 million counterfeit respirators of substandard quality unable to protect individuals from infection have been seized globally since the start of the COVID-19 pandemic. These seizures have primarily occurred in urban warehouses and ports around the world according to analysis of public and corporate data shared with the authors. The presence of tens of millions of respirators in storage facilities prior to distribution demonstrates that urban areas are key elements of illicit supply chains. Data suggests that the concept of urban insecurity needs to be reconsidered in light of illicit supply chains for counterfeit respirators and their role in facilitating disease transmission in urban areas. The analysis presented in this article suggests that threats to human life should not be confined narrowly to violent acts or the consumption of drugs. Human life can also be threatened through the massive distribution of counterfeit N95 masks during a pandemic, a problem that has become more acute with more contagious mutations of COVID-19. 

Urban Crime - An International Journal Vol. 3-No 2-September 2022 

By Gaurav Verma, Rynaa Grover, Jiawei Zhou, Binny Mathew, Jordan Kraemer, Munmun De Choudhury, Srijan Kumar

Violence-provoking speech -- speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech (F1=0.89), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task (F1=0.69). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises.

2024