international’s Corruption Perception Index (CPI). The data cover 189 countries. The _Common Corruption_ dummy takes the value of 1 if both countries under consideration are relatively corrupt, i.e., the corruption indices of both countries for the last two years are less than the median value. Appendix Table A2 presents summary statistics of actual protests, news-based protests, polity IV, corruption perception index, and social media penetration in 2018.

We next present the test as to whether protests in period, _t-1,_ in the rest of world (i.e., for all other countries _j_ in the rest of the world) lead to protests in the current period, _t_ , in country _i_ . The number of protests per capita is skewed to the left, with a majority of countries having no protest (see Table 1).

The framework is an autoregressive spatial model following Lesage and Pace (2009). The ZeroInflated Poisson (ZIP) model mixes two zero generating processes. The first process generates zeros. The second process is governed by a Poisson distribution with an estimation as follows: ) (1) where is an N-by-1 vector that contains the number of protests tt tt−1 tt−ττ ii tt tt _per capita_ in N countries at yy = NNeepp (ρWWyy + δδIIyy + FFEE + FFEE + cc current month . is an N-by-1 vector that contains the number of protests _per capita_ in N tt yy countries in the past month. In subsequent robustness checks, we use quarterly frequency instead tt−1 of monthly. In addition to the Zero-Inflated Poisson estimator, we use the standard Poisson as an PP yy alternative estimator. We also use an alternative measure of protests based on the intensity of media chatter around protests. is an N-by-N spatial weight matrix that captures geographic proximity between countries. In the baseline specification, we measure the spatial spillovers using geographical distance. The WWdiagonal elements are zero. The off-diagonal elements are the inverse of the distance between 1 country capitals (i.e., ~~)~~ . Next, the weight matrix is standardized so that the sum of row is unity. ρ captures the importance of spatial dependence in protest spillover from foreign countries. ddiiddttd ddeeii,jj is an N-by-N identity matrix. captures the autoregressive effects of domestic protests _per capita_ in previous months. and are vectors of country and month fixed effects. Data on II δδ<sup>ττ</sup> distance and language are obtained from CEPII. ii tt FFEE FFEE To account for the role that social media could play as a catalyst for protests, we use information about social media penetration in both source and destination of protests. Specifically, we test whether source countries with higher social media penetration are more likely to send stronger protest spillover to other countries. The econometric specification is as follows: ) (2) The only difference between (2) and (1) is the spatial weight matrix. tt tt−1 tt−1 ii tt tt now is also an N-by-N yy = NNeepp (ρZZyy + δδIIyy + FFEE + FFEE + cc spatial weight matrix but is different from . The diagonal elements are zero. The off-diagonal ZZ elements are the inverse of the distance multiplied by common social media penetration (i.e., 1 WW a ddiiddttdsum of row is unity. The idea is to account for protests in countries with a common level of social ddeeii,jj × aaPPc PPcc NNPPa ccNNmma ii,jj<sup>). The weight matrix is then also standardized so that the</sup> 7