As expected, many of the previously useful automated heuristics for bot detection have started to become less relevant as bot creators have updated their operational security (OPSEC) and tactics, techniques, and procedures (TTP) to account for common detection mechanisms. Bots that display obvious methods, like posting at odd hours, creating extremely high amounts of interaction, retweeting without ever posting original content, etc., are routinely suspended by Twitter’s internal team. This has produced a Darwinian effect on inauthentic accounts making them harder to discern using automated statistics.
What remains, especially for researchers without access to internal Twitter telemetry (such as log-in IP addresses or associated metadata such as email addresses or phone numbers), is analysis and classification of accounts based purely on behavior and content. Bots are useless to their creators unless influencing a conversation – aside from building some historical record. As a result, visually exposing accounts that attempt to communicate in a largely broadcast manner has traditionally been the most useful classification used by Brainspace and other data analytics tools (aka, the “star-pattern” analysis).
However, content-based sentiment analysis can also prove useful. Many sentiments are unusual for humans to have in conjunction with one another, simply because humans have a limited set of interests in which they Tweet about. An early example of this was discussed by Cyxtera analysts in research pertaining to seemingly Pro-Trump bots posting heavily about the U.S. leaving the Syrian war.