The Twitter Bot Analysis Module is designed to detect potential use of automation based on a machine learning API called Botometer, a project by the Observatory on Social Media (OSoMe) at the University of Indiana. It analyzes accounts in your dataset and generates the probability scores for a variety of different types of automated and/or fraudulent activities.
Botometer API Scores
After running the Bot Analysis as shown in the video tutorial above, download a CSV file of your dataset to access the Botometer scores for the various types of automated and/or fraudulent activities. As described by the Botometer team, the API calculates the following scores (each score ranges from 0=not likely to 1=highly likely):
Echo-chamber: accounts that engage in follow back groups and share and delete political content in high volume
Fake follower: bots purchased to increase follower counts
Financial: bots that post using cashtags
Self declared: bots from botwiki.org
Spammer: accounts labeled as spambots from several datasets
Other: miscellaneous other bots obtained from manual annotation, user feedback, etc.
Overall: this is a summary score based on several models trained by Botometer
In addition to the scores listed above, the CSV file will also include CAP values (or Complete Automation Probability). CAP is a probability (between 0 and 1) that a Twitter account (with a given overall score or greater) is automated (aka bot). Read more about CAP and how to interpret CAP values on the Botometer FAQ page.