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The prospect of using the Internet and other Big Data methods to construct event data promises to transform the field but is stymied by the lack of a coherent strategy for addressing the problem of selection. Past studies have shown that event data have significant selection problems. In terms of conventional standards of representativeness, all event data have some unknown level of selection no matter how many sources are included. We summarize recent studies of news selection and outline a strategy for reducing the risks of possible selection bias, including techniques for generating multisource event inventories, estimating larger populations, and controlling for nonrandomness. These build on a relativistic strategy for addressing event selection and the recognition that no event data set can ever be declared completely free of selection bias.

Previous analyses of the relationship between dissent and repression have turned up mixed, and often conflicting, results. Although research on the effect of repression on dissent has been inconsistent, it becomes obvious that time and country variation does matter: the effects of dissent and repression do not occur in a social vacuum. Our analyses seek to determine what country-level contextual variables influence levels of nonviolent and violent dissent, as well as nonviolent and violent repression. We include a battery of variables describing domestic economic and political conditions, sociodemographics, and global linkages. We test specific hypotheses about these potential determinants of various forms of dissent and repression by using data on 530 event-weeks of the period 1994-2004 across 97 countries. We find that proximity to the center of the world polity network and capacity for state terror have an effect on both dissent and repression, and international, political, and economic factors have an...