Figure 3 reveals a robust statistical relationship between yesterday’s level of coordination and today’s number of protests. To reach this conclusion, we use a negative binomial regression model where the dependent variable is the count of protests at time t and the primary independent variables is the coordination at time \(t-1\). The coordination measure has a p-value less than 0.01 (coefficient of 2.239 and standard error of 0.569), and a one standard deviation increase in the coordination measure is associated with a 25.4% increase in the number of protests on the following day. Figure 3 also shows that the result holds when a number of potential other methods of coordination are modeled. The percent of tweets that contain a hashtag indicates the extent to which people are contributing to a tagged conversation, in some cases about protests. When a high percent of tweets are retweeted, a smaller number of original tweets drive the conversation. The percent of tweets that contain links indicates that more people are referencing an important blog or news item. And the percent of tweets that mention other users indicates that more direct communication is happening between people on Twitter. Retweets and hashtags are especially conducive to spreading information that may have a coordinating effect. None of these measures correlate as strongly with protests on the following day as coordination, and none of them are significantly associated with protests after controlling for coordination. A more detailed presentation of the model and controls is reported in the Supplementary Materials.
We also show in the Supplementary Materials that these results are robust to model type (linear vs. negative binomial regression), protest data source (a handcoded dataset and a different machine-coded one deliver the same results), and serial correlation in errors (multiple tests suggest that including a lagged dependent variable is sufficient to deal with the problem, and including several extra lags does not change the results). All models account for unobserved between-country differences with country fixed effects (accounting for stable characteristics like country size and political history), and they control for unobserved sources of variation over time with day fixed effects (helping to control for day-of-week variation in Twitter activity or special events like Ramadan that affect all countries in the study). Moreover, a model that drops high protest days also shows a significant relationship, suggesting that the association is not driven by a few large events. Finally, to ensure that the results are not driven by individuals trying to draw international attention to the protests [23], we drop all English tweets; the results remain the same.
An important question about the potential effect of social media coordination is its source - is it decentralized or does it come from traditional sources like news media and political activists? In the Supplementary Materials, we add to our models a number of measures of Twitter activity from these traditional sources [27], including the percent of tweets on a given day that were sent by media organizations, media personalities, digital activists, and the top 5% most active Twitter users. However, these models with controls continue to show a similar association with hashtag coordination, with an effect for media organizations and no effect for digital activists. Coordination therefore appears to come through decentralized activity of many individuals, not the frequent tweeting of a few specialized actors.
Figure 4 shows the prevalence of the hashtags in Bahrain, Egypt, Morocco, and Qatar most often used for coordination during the study period. They were chosen by observing the most common hashtag in a country on a given day, counting how many times that hashtag was the most common during the study period, and keeping the 4 most common. In Egypt, three features stand out. First, there is little coordination on any one hashtag before January 25th. The hashtag ‘#egypt’ is barely used, the first appearance of ‘#jan25’ is not until January 19th (and then accounts for only 0.1% of tweets), and ‘#tahrir’ does not appear until January 25th (and it does not appear in large numbers until just before the resignation of Mubarak). Second, which hashtag is most prevalent depends on the type of upcoming event. During the 18 days of initial protest, ‘#jan25’ dominates, as this was the focal date for the protests. Though the largest protests while Mubarak was in power took place in Tahrir Square, they were contentious, which is why more general hashtags such as ‘#jan25’ and ‘#egypt’ dominate. Moreover, ‘#jan25’ consistently declines in usage after the 18 days of protest. By the middle of March, it will never be the most common of the three hashtags again, and it ceases to correlate with the other two. Third, overall levels of coordination decrease after the first 18 days. They first decrease sharply after President Mubarak’s resignation, and their average prevalence gradually continues to decline. ‘#tahrir’ might be an exception, as it is used much more narrowly to coordinate specific, Tahrir Square-centric events, but the frequency of its usage declines as well.
In Bahrain, there is a dramatic spike in coordination almost immediately after the start of protests on February 14th. This coordination is then sustained throughout the year, with increases and spikes around important events. We include in Figure 4 the prevalence of the hashtag #lulu - though it is never the most common hashtag, it is the one used to discuss events concerning the Pearl Roundabout, the main focus of protestor activity in Bahrain. #lulu is not in our dataset before February 14th, and its use varies over time in more specific patterns than #Bahrain. Finally, note the focus on events in Egypt at the end of the Egyptian protests and starting again in mid-October.
Morocco experienced much less protest, as shown in Figure 1, and Figure 4 shows they also experienced less coordination. The initial protests in Morocco occurred on February 20th, when coordination peaked, but otherwise the level of coordination is not on the same scale seen in more contentious countries. Note also that Moroccans did not appear to take as much interest in events occurring elsewhere in the Middle East and North Africa, suggesting that they may have felt less affinity towards protestors in other countries. Similarly, Qatar exhibits low levels of coordination and no attempts at organizing protests. The day with the highest level of hashtag coordination is December 2nd, 2010, when Qatar learned it was chosen to host the 2022 World Cup. However, people in Qatar paid close attention to the events in #Libya, the third most common hashtag (and, not shown, hashtags about the Egypt protests ranked 6th and 7th).