  Thanks to platforms such as Twitter and Facebook, people can know facts and
events that otherwise would have been silenced. However, social media
significantly contribute also to fast spreading biased and false news while
targeting specific segments of the population. We have seen how false
information can be spread using automated accounts, known as bots. Using
Twitter as a benchmark, we investigate behavioural attitudes of so called
`credulous' users, i.e., genuine accounts following many bots. Leveraging our
previous work, where supervised learning is successfully applied to single out
credulous users, we improve the classification task with a detailed features'
analysis and provide evidence that simple and lightweight features are crucial
to detect such users. Furthermore, we study the differences in the way
credulous and not credulous users interact with bots and discover that
credulous users tend to amplify more the content posted by bots and argue that
their detection can be instrumental to get useful information on possible
dissemination of spam content, propaganda, and, in general, little or no
reliable information.
