publications

Publications in reversed chronological order, generated by jekyll-scholar. Once upon a time, I considered attending graduate school in social media or human-computer interaction. The thought will still cross my mind from time to time.

2024

Chillbot: Content Moderation in the Backchannel

Joseph Seering, Manas Khadka, Nava Haghighi, and 3 more authors

Proc. ACM Hum.-Comput. Interact., Nov 2024

Abs DOI

Moderating online spaces effectively is not a matter of simply taking down content: moderators also provide private feedback and defuse situations before they cross the line into harm. However, moderators have little tool support for these activities, which often occur in the backchannel rather than in front of the entire community. In this paper, we introduce Chillbot, a moderation tool for Discord designed to facilitate backchanneling from moderators to users. With Chillbot, moderators gain the ability to send rapid anonymous feedback responses to situations where removal or formal punishment is too heavy-handed to be appropriate, helping educate users about how to improve their behavior while avoiding direct confrontations that can put moderators at risk. We evaluated Chillbot through a two week field deployment on eleven Discord servers ranging in size from 25 to over 240,000 members. Moderators in these communities used Chillbot more than four hundred times during the study, and moderators from six of the eleven servers continued using the tool past the end of the formal study period. Based on this deployment, we describe implications for the design of a broader variety of means by which moderation tools can help shape communities’ norms and behavior.

2021

Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online

Crystal Lee, Tanya Yang, Gabrielle D Inchoco, and 2 more authors

In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, Nov 2021

Abs DOI

Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government’s pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.