Attribution of responsibility and blame are important topics in political science especially as individuals tend to think of political issues in terms of questions of responsibility, and as blame carries far more weight in voting behavior than that of credit. However, surprisingly, there is a paucity of studies on the attribution of responsibility and blame in the field of disaster research. The Flint water crisis is a story of government failure at all levels. By studying microblog posts about it, we understand how citizens assign responsibility and blame regarding such a man-made disaster online. We form hypotheses based on social scientific theories in disaster research and then operationalize them on unobtrusive, observational social media data. In particular, we investigate the following phenomena: the source for blame; the partisan predisposition; the concerned geographies; and the contagion of complaining. This paper adds to the sociology of disasters research by exploiting a new, rarely used data source (the social web), and by employing new computational methods (such as sentiment analysis and retrospective cohort study design) on this new form of data. In this regard, this work should be seen as the first step toward drawing more challenging inferences on the sociology of disasters from "big social data".

Abstract-Understanding the topological characteristics of the Internet is an important research issue as the Internet grows with no central authority. Internet Topology mapping studies help better understand the dynamics of the Internet backbone network. Knowing underlying topology, researchers can develop new protocols and services or fine-tune existing ones. In this paper, we first discuss issues in subnet-level Internet topology mapping and present approaches to handle them. Then, we introduce Cheleby, an integrated Internet topology mapping system. Cheleby, first, dynamically probes every observed subnetwork in the Internet using a team of PlanetLab nodes around the world. Then, it utilizes efficient algorithms for resolving subnets, IP aliases, and unresponsive routers in collected data to provide subnet-level topologies. Different from current topology mapping systems, Cheleby not only samples the Internet topology but also processes the collected data to build more complete maps. Sample topologies are provided at http://cheleby.cse.unr.edu.

Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

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During the early months of the COVID-19 pandemic, millions of people had to work from home. We examine the ways in which COVID-19 affect organizational communication by analyzing five months of calendar and messaging metadata from a technology company. We found that: (i) cross-level communication increased more than that of same-level, (ii) while within-team messaging increased considerably, meetings stayed the same, (iii) off-hours messaging became much more frequent, and that this effect was stronger for women; (iv) employees respond to non-managers faster than managers; finally, (v) the number of short meetings increased while long meetings decreased. These findings contribute to theories on organizational communication, remote work, management, and flexibility stigma. Besides, this study exemplifies a strategy to measure organizational health using an objective (not self-report based) method. To the best of our knowledge, this is the first study using workplace communication metadata to examine the heterogeneous effects of mandatory remote work.