  Bots have been in the spotlight for many social media studies, for they have
been observed to be participating in the manipulation of information and
opinions on social media. These studies analyzed the activity and influence of
bots in a variety of contexts: elections, protests, health communication and so
forth. Prior to this analyses is the identification of bot accounts to
segregate the class of social media users. In this work, we propose an ensemble
method for bot detection, designing a multi-platform bot detection architecture
to handle several problems along the bot detection pipeline: incomplete data
input, minimal feature engineering, optimized classifiers for each data field,
and also eliminate the need for a threshold value for classification
determination. With these design decisions, we generalize our bot detection
framework across Twitter, Reddit and Instagram. We also perform feature
importance analysis, observing that the entropy of names and number of
interactions (retweets/shares) are important factors in bot determination.
Finally, we apply our multi-platform bot detector to the US 2020 presidential
elections to identify and analyze bot activity across multiple social media
platforms, showcasing the difference in online discourse of bots from different
platforms.
