Large-scale measurement of aggregate human colocation patterns for epidemiological modeling.

To understand and model public health emergencies, epidemiologists need data that describes how humans are moving and interacting across physical space. Such data has traditionally been difficult for researchers to obtain with the temporal resolution and geographic breadth that is needed to study, for example, a global pandemic. This paper describes Colocation Maps, which are spatial network datasets that have been developed within Meta's Data For Good program. These Maps estimate how often people from different regions are colocated: in particular, for a pair of geographic regions x and y, these Maps estimate the rate at which a randomly chosen person from x and a randomly chosen person from y are simultaneously located in the same place during a randomly chosen minute in a given week. These datasets are well suited to parametrize metapopulation models of disease spread or to measure temporal changes in interactions between people from different regions; indeed, they have already been used for both of these purposes during the COVID-19 pandemic. In this paper, we show how Colocation Maps differ from existing data sources, describe how the datasets are built, provide examples of their use in compartmental modeling, and summarize ideas for further development of these and related datasets. Among the findings of this study, we observe that a pair of regions can exhibit high colocation despite few people moving between those regions. Additionally, for the purposes of clarifying how to interpret and utilize Colocation Maps, we scrutinize the Maps' built-in assumptions about representativeness and contact heterogeneity.

Adherence to suicide reporting guidelines by news shared on a social networking platform.

Rates of suicide in the United States are at a more than 20-y high. Suicide contagion, or spread of suicide-related thoughts and behaviors through exposure to sensationalized and harmful content is a well-recognized phenomenon. Health authorities have published guidelines for news media reporting on suicide to help prevent contagion; however, uptake of recommendations remains limited. A key barrier to widespread voluntary uptake of suicide-reporting guidelines is that more sensational content is perceived to be more engaging to readers and thus enhances publisher visibility and engagement; however, no empirical information exists on the actual influence of adherence to safe-reporting practices on reader engagement. Hence, we conducted a study to analyze adherence to suicide-reporting guidelines on news shared on social media and to assess how adherence affects reader engagement. Our analysis of Facebook data revealed that harmful elements were prevalent in news articles about suicide shared on social media while the presence of protective elements was generally rare. Contrary to popular perception, closer adherence to safe-reporting practices was associated with a greater likelihood of an article being reshared (adjusted odds ratio [AOR] = 1.19, 95% confidence interval [CI] = 1.10 to 1.27) and receiving positive engagement ("love" reactions) (AOR = 1.20, 95% CI = 1.13 to 1.26). Mean safe-reporting scores were lower in the US than other English-speaking nations and variation existed by publisher characteristics. Our results provide empirical evidence that improved adherence to suicide-reporting guidelines may benefit not only the health of individuals, but also support publisher goals of reach and engagement.

Evidence of Online Performance Deterioration in User Sessions on Reddit.

This article presents evidence of performance deterioration in online user sessions quantified by studying a massive dataset containing over 55 million comments posted on Reddit in April 2015. After segmenting the sessions (i.e., periods of activity without a prolonged break) depending on their intensity (i.e., how many posts users produced during sessions), we observe a general decrease in the quality of comments produced by users over the course of sessions. We propose mixed-effects models that capture the impact of session intensity on comments, including their length, quality, and the responses they generate from the community. Our findings suggest performance deterioration: Sessions of increasing intensity are associated with the production of shorter, progressively less complex comments, which receive declining quality scores (as rated by other users), and are less and less engaging (i.e., they attract fewer responses). Our contribution evokes a connection between cognitive and attention dynamics and the usage of online social peer production platforms, specifically the effects of deterioration of user performance.