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Online harassment is pervasive. While substantial research has examined the nature of online harassment and how to moderate it, little work has explored how social media users evaluate the profiles of online harassers. This is important for helping people who may be experiencing or observing harassment to quickly and efficiently evaluate the user doing the harassing. We conducted a lab experiment (N=45) that eye-tracked participants while they viewed profiles of users who engaged in online harassment on mock Facebook, Twitter, and Instagram profiles. We evaluated what profile elements they looked at and for how long relative to a control group, and their qualitative attitudes about harasser profiles. Results showed that participants look at harassing users' post history more quickly than non-harassing users. They are also somewhat more likely to recall harassing profiles than non-harassing profiles. However, they do not spend more time on harassing profiles. Understanding what users pay attention to and recall may offer new design opportunities for supporting people who experience or observe harassment online. 

Abstract Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.