From Yellow Peril to Model Minority: Asian stereotypes in social media during the COVID-19 pandemic

Heightened racial tensions during the COVID-19 pandemic contributed to the increase and rapid propagation of online hate speech towards Asians. In this work, we study the relationship between the racist narratives and conspiracy theories that emerged related to COVID-19 and historical stereotypes underpinning Asian hate and counter-hate speech on Twitter, in particular the Yellow Peril and model minority tropes. We find that the pandemic catalyzed a broad increase in discourse engaging with racist stereotypes extending beyond COVID-19 specifically. We also find that racist narratives and conspiracy theories which emerged during the pandemic and gained widespread attention were rooted in deeply-embedded Asian stereotypes. In alignment with theories of idea habitat and processing fluency, our work suggests that historical stereotypes provided an environment vulnerable to the racist narratives and conspiracy theories which emerged during the pandemic. Our work offers insight for ongoing and future anti-racist efforts. © 2023 ACM.

Self-disclosure on Twitter During the COVID-19 Pandemic: A Network Perspective

Amidst social distancing, quarantines, and everyday disruptions caused by the COVID-19 pandemic, users’ heightened activity on online social media has provided enhanced opportunities for self-disclosure. We study the incidence and the evolution of self-disclosure temporally as important events unfold throughout the pandemic’s timeline. Using a BERT-based supervised learning approach, we label a dataset of over 31 million COVID-19 related tweets for self-disclosure. We map users’ self-disclosure patterns, characterize personal revelations, and examine users’ disclosures within evolving reply networks. We employ natural language processing models and social network analyses to investigate self-disclosure patterns in users’ interaction networks as they seek social connectedness and focused conversations during COVID-19 pandemic. Our analyses show heightened self-disclosure levels in tweets following the World Health Organization’s declaration of pandemic worldwide on March 11, 2020. We disentangle network-level patterns of self-disclosure and show how self-disclosure characterizes temporally persistent social connections. We argue that in pursuit of social rewards users intentionally self-disclose and associate with similarly disclosing users. Finally, our work illustrates that in this pursuit users may disclose intimate personal health information such as personal ailments and underlying conditions which pose privacy risks. © 2021, Springer Nature Switzerland AG.