To develop a list of Twitter accounts for monitoring, we analyzed followers of @de_sputnik, the German-language Twitter account for Sputnik News.
While Sputnik is an overt vehicle for Russian influence, and thus obviously relevant to influence campaigns, not all of its followers on Twitter are meaningfully involved in such campaigns. Therefore we applied metrics to the set to find the most relevant users. The resulting dashboard monitors the output of:
- users who tweet content that followers of @de_sputnik engage with,
- users who actively engage with that content, and
- users who artificially amplify content within the network.
The analysis was performed in July 2017 and reflects activity from that period. The same analysis carried out at a different time would produce similar but distinct results.
We analyzed the followers of @de_sputnik in three ways, identifying:
- The 500 most influential accounts: These are accounts that receive the highest number of interactions (retweets, replies) from people who follow @de_sputnik
- The 500 accounts most responsive to influence: These are accounts that most actively engage with the content promoted by the influential accounts (by sending retweets and replies).
- The 500 top accounts scored by in-groupness: These are accounts that most-often direct interactions to other followers of @de_sputnik, relative to interactions sent to non-followers.
The metrics we used are fully detailed below. The three sets overlapped somewhat, for a total of 1,307 accounts, of which 104 (8 percent) had been suspended since the network was collected in July 2017. We weeded this set for maximum relevance, including removing accounts that indicated German was not the user’s first language. We sorted the remainder by follower count and included the 500 accounts with the lowest number of followers, in order to focus the analysis on less obvious participants.
We then returned to the analysis of the follower set to identify likely bot and troll accounts that exist primarily to artificially amplify other accounts. To do this, we identified accounts with:
- Fewer than 5,000 followers
- More than 100 tweets per day
- With at least 25 percent of their last 200 tweets being retweets
We then sorted this set by in-groupness and included the accounts that were not already included in one of the previous sets for a final total of 600 accounts.
The resulting dataset is a representative sample of accounts that generally share the same orientation as the seed account, @de_sputnik, including accounts that are measurably influenced by the seed account, other accounts that are effective at influencing the followers of the seed account, and accounts that amplify members of the first two sets.
Because we chose to highlight some bot activity, some commercial spam may creep into the dataset. We have attempted to remove these accounts when possible, primarily by identifying accounts that send repetitive and duplicative content, but also through a limited manual review.