Overall characteristics of the pro- and anti-protest groups
Table 1 summarizes the number of tweets and users present for each group. There are approximately double the number of Anti-Protest users as Pro-Protest users and very few users that fit our definition of “Mixed”. The Anti-Protest users tweeted approximately 1.7 times more tweets than the Pro-Protest users.
Table 2 summarizes the tweets by tweet type and group label. It shows how similar the Pro- and Anti-Protest groups are in terms of the percentage of their tweets that are original tweets, retweets, and comments. It also shows that the Pro- and Anti-Protest groups are similar in the number of other accounts they mention and hashtags they use though the Anti-Protest users use hashtags less often. Together the results from Tables 1, 2 are suggesting that although the anti-protest group is using Twitter more to push their message, this message is less coordinated and more varied. In contrast the Pro-Protest group are all pushing a more cohesive similar message as evidenced by the higher use of hashtags.
We next considered three different aspects of community: (1) what types of actors are present, (2) the structure of the communications network, and (3) the structural position of the most central actors in those networks. Table 3 summarizes information about the accounts present in the Pro- and Anti-Protest groups. There are low levels of verified users and news organizations with the lowest numbers in the Pro-Protest group. The Pro-Protest group also had the highest levels of accounts with default profiles and suspended accounts (as of May 20, 2020). Default profiles and suspended accounts can signal actors new to Twitter, or actors that violated the terms of service, including bots. We also find that accounts that cannot be labeled as either Pro- or Anti- the protest, have a surfeit of news agencies and verified actors. This supports our observation that this group is largely made up of those reporting on the protest. These results suggest that by and large news agencies are acting in a more objective fashion in their lack of retweeting activity. In addition, they suggest that the Pro-Protest group is more supported by bots and less credible actors.
We next compared the full Pro- and Anti-Protest communities on general network metrics for the combined User × User communication network (retweets, comments, and mentions) as summarized in Table 4. The Pro-Protest network was found to be slightly denser than the Anti-Protest network. This suggests there is slightly more cohesion and coordination within the Pro-Protest group.
We also compared the degree and eigenvector centrality of the top 50 most central users in each group, as summarized in Fig. 3. The top Pro-Protest users measured higher on total degree centrality metrics than the top Anti-Protest users. As users outside of the top 50 are looked at, the users in both groups have similar measures. This higher degree centrality among the top actors in the Pro-Protest group suggests that these actors are more tied to other Pro-Protestors and/or are spending more of their messages mentioning and attacking actors on the Anti-Protest side.
One possible indicator of organized activity related to protests is the creation of new accounts to push narratives around such protests. Figure 4 shows the creation dates of the users involved in the conversation by group for those accounts that started since 2019. The creation of what are labeled Pro- and Anti-Protest accounts appear to be similar until March 2020, when there is a spike in Pro-Protest account creation. This result provides further evidence of a possible type of orchestration on the Pro-Protest side of the debate.
Table 5 summarizes characteristics of these newer accounts. Approximately 60% of the newer accounts and tweets from newer accounts are from the Pro-Protest group, otherwise, the newer accounts looks relatively similar between the two groups.
Figure 5 details the retweet network between these younger user accounts. The majority of newer accounts are not interacting with each other, but for those that do, it appears that the Pro-Protest accounts are interacting with each other more as can be seen by the largest component. It is also of interest that the two most active accounts on the Pro-Protest side overall are located in this main component (one of which is now suspended by Twitter, and both of which have bot scores above 0.75). These two accounts as well as many others shown in Fig. 5 appear to be troll-like accounts upon manual inspection: they are consistently retweeting attacks on the opposite group or praise for their own. These results indicate that both groups have troll-like new accounts active in the conversation, but the Pro-Protester group appears to have more coordination of such accounts.
Narrative differences (shared hashtags and URLs)
To broadly investigate the differences in content shared by the Pro-Protest and Anti-Protest groups, we obtained the top five most used hashtags and top five most shared website domains (both including retweets) for each group as shown in Tables 6, 7. In terms of hashtags, we can see an emphasis on certain states and the Trump MAGA slogan on the Pro-Protest side and an emphasis on COVID and attacking President Trump (e.g., #25thAmendmentNow) on the Anti-Protest side. There does not seem to be much difference in the use of hashtags between verified and high-bot-score accounts compared with the overall group.
In terms of shared domains, the top five for the Pro-Protest side are dominated by either center to far right news sites or social media, while the Anti-Protest side are dominated by center/center left news media, demonstrating the apparent partisan divide. For both sides it appears that verified accounts share link-shortened addresses to a greater degree.
Tables 8, 9 show the changes to the top five most shared hashtags and websites by group over the three time periods of interest. The top hashtags shared by Pro-Protest follow the states in which protests were occurring in each time period. The first period also contains pro-Trump hashtags (MAGA and TheGreatAwakening, a reference to pro-Trump conspiracy, QAnon). COVID19 is prominently used throughout by the Anti-Protest users as well as anti-protest/anti-Trump hashtags such as “Covidiots” and “25th AmendmentNow”. It should be noted that in the last period the DropOutBiden hashtag appears to have been an attack on the Democratic nominee from progressives. Similarly, the use of MAGA in the last period is in tweets attacking President Trump, his supporters, or the protests.
The top five web domains shared by the two groups reiterate their apparent partisan leanings and both groups prominently shared other social media links in the first period. Pro-Protest users continued to do so while the Anti-Protest group shared additional news/opinion sites.
Dynamic change in groups, activity and targets
Figure 6 again shows the tweets over time throughout the three periods of interest, but now grouped by what cohort is the source of the tweet. Period 1, which coincides with the original OperationGridlock protest in Michigan, is dominated by Pro-Protest activity. The second period instead shows an original spike in activity from the Pro-Protest group (focused mostly on retweets of realDonaldTrump’s “Liberate” tweets) and then the response from the Anti-Protest group against both President Trump and the protests, which overtake the Pro-Protest side in volume. This can be seen in the change in number of users with the Pro-Protest side only increasing from 46,056 unique users in Period 1 to 106,575 users in Period 2, while the Anti-Protest side increases from 12,404 to 244,193 users. Additionally, 26.0% of the Pro-Protest users in Period 2 were present in Period 1, whereas for the Anti-Protest side, only 3.5% of the users in Period 2 were from Period 1. These results suggest a higher level of consistent and continued coordination and participation among the Pro-Protest group, while the Anti-Protest group is able to attract a larger number of newer users between periods.
We compared the users within each group that are doing the targeting as shown in Figs. 7, 8. In contrast to their targets, there is a much lower level of verified users involved in retweeting, commenting, and mentioning in both groups. The Anti-Protest group has a higher presence of verified accounts in all time periods than the Pro-Protest group. The bot score distributions summarized in Fig. 8 present an interesting comparison between the Pro- and Anti-Protest users. While the Pro-Protest user distributions are very similar across all time periods, the Anti-Protest distribution flattens out in period 2 (the period with the most activity and attention). This indicates a higher relative presence of users with low bot scores. This could indicate that the Anti-Protest group is more successful at attracting activity from the general public during the most active period than the Pro-Protest group. It also suggests that the Pro-Protest group’s composition is less organic than that of the Anti-Protest group.
To obtain an understanding of any differences in how these Pro- and Anti-Protest users operated over time, we also explored the most targeted accounts for each group for each time period (Appendix for Table 11). “Most targeted” in this context refers to the accounts that were most retweeted or had the most comments (replies and quotes) directed at them, or which were most mentioned by Pro- or Anti-Protest users. The top five most targeted accounts within each period are dominated by government, political group, pundit, journalist, and news organization accounts. For the retweet and comment networks, the focus is on a mix of verified and unverified accounts for both Pro- and Anti-Protest groups. The top five mentions in both groups are all verified accounts. In looking at the top five targets across the comment and mention networks and all time periods, the most apparent difference between the Pro- and Anti-Protest activity is that the none of the top five targets for the Pro-Protest side are labeled as Anti-Protest, whereas many of the top five targets of Anti-Protest users are Pro-Protest (Pro-Protest users do target NotLabeled user accounts of the Democratic governors of states where protests took place, and such accounts are most likely Anti-Protest though they are not labeled as such due to not being in the retweet networks).
To get a wider perspective on which types of accounts were being targeted by each group, we compared the top 100 targets for each group at each time period through the percentage of verified accounts, news organization accounts and high-bot-scoring (> 0.75) accounts. As Fig. 9 shows, there are higher percentages of verified accounts in the top 100 most retweeted accounts for the Anti-Protest group in contrast to the Pro-Protest group, especially in the second and third periods. The most commented on and mentioned accounts for both groups have similar percentages of verified accounts, though there are more new organizations being targeted by the Anti-Protest group in some periods. The percentage of targets for the Pro-Protest group that have high-bot scores hovers around 10% in all time periods, whereas for the Anti-Protest group the percentage is between 2 and 5%. This further supports the argument that the Pro-Protest was a less organic coordinated activity; rather, it appears more as a bot amplified and highly coordinated activity.