No abstract

Google search is ubiquitous, and Google Trends (GT) is a potentially useful access point for big data on many topics the world over. We propose a new ‘variance-in-time’ method for forecasting events using GT. By collecting multiple and overlapping samples of GT data over time, our algorithm leverages variation both in the mean and the variance of a search term in order to accommodate some idiosyncracies in the GT platform. To elucidate our approach, we use it to forecast protests in the United States. We use data from the Crowd Counting Consortium between 2017 and 2019 to build a sample of true protest events as well as a synthetic control group where no protests occurred. The model’s out-of-sample forecasts predict protests with higher accuracy than extant work using structural predictors, high frequency event data, or other sources of big data such as Twitter. Our results provide new insights into work specifically on political protests, while providing a general approach to GT that should be useful to researchers of many important, if rare, phenomena.

Twitter data are becoming an important part of modern political science research, but key aspects of the inner workings of Twitter streams as well as self-censorship on the platform require further research. A particularly important research agenda is to understand removal rates of politically charged tweets. In this article, I provide a strategy to understand removal rates on Twitter, particularly on politically charged topics. First, the technical properties of Twitter’s API that may distort the analyses of removal rates are tested. Results show that the forward stream does not capture every possible tweet –between 2 and 5 percent of tweets are lost on average, even when the volume of tweets is low and the firehose not needed. Second, data from Twitter’s streams are collected on contentious topics such as terrorism or political leaders and non-contentious topics such as types of food. The statistical technique used to detect uncommon removal rate patterns is multilevel analysis. Results show significant differences in the removal of tweets between different topic groups. This article provides the first systematic comparison of information loss and removal on Twitter as well as a strategy to collect valid removal samples of tweets.

Is Google Trends (GT) useful to survey populations? Extant work has shown that certain search queries reflect the attitudes of hard-to-survey populations, but we do not know if this extends to the general population. In this article, we leverage abundant data from the Covid-19 pandemic to assess whether people’s worries about the pandemic match epidemiological trends as well as political preferences. We use the string ‘will I die from coronavirus’ on GT as the measure for people’s level of distress regarding Covid-19. We also test whether concern for coronavirus is a partisan issue by contrasting GT data and 2016 election results. We find strong evidence that (1) GT search volume close matches epidemiological data and (2) significant differences exist between states that supported Clinton or Trump in 2016.

Formal institutions in dictatorship are known to improve authoritarian governance and promote power-sharing. Yet institutions also act as tools of information propagation and can be used by autocrats for signaling purposes. In this article, I argue that in times of weakness, dictators follow an expand-and-signal strategy, expanding the ruling coalition to decrease the relative power of coup plotters and then create visible formal institutions to signal strong support. Doing so decreases (1) the probability that a coup is launched and (2) that one succeeds if staged. I propose a formal model to unpack the mechanisms of my argument and use the case of the Dominican Republic during Rafael Trujillo's rule to illustrate my theory.