71,177 research outputs found
Shocking the Crowd: The Effect of Censorship Shocks on Chinese Wikipedia
Collaborative crowdsourcing has become a popular approach to organizing work
across the globe. Being global also means being vulnerable to shocks --
unforeseen events that disrupt crowds -- that originate from any country. In
this study, we examine changes in collaborative behavior of editors of Chinese
Wikipedia that arise due to the 2005 government censor- ship in mainland China.
Using the exogenous variation in the fraction of editors blocked across
different articles due to the censorship, we examine the impact of reduction in
group size, which we denote as the shock level, on three collaborative behavior
measures: volume of activity, centralization, and conflict. We find that
activity and conflict drop on articles that face a shock, whereas
centralization increases. The impact of a shock on activity increases with
shock level, whereas the impact on centralization and conflict is higher for
moderate shock levels than for very small or very high shock levels. These
findings provide support for threat rigidity theory -- originally introduced in
the organizational theory literature -- in the context of large-scale
collaborative crowds
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
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