Socialbots: Implications on the Safety and Reliability of Twitter-Based Services

More and more, data extracted from social networks is used to build new applications and services, such as traffic monitoring platforms, identification of epidemic outbreaks, as well as several other applications related to the creation of smart cities, for example. However, such services are vulnerable to attacks from bots - automatized accounts - seeking to tamper statistics of public perception posting an excessive number of messages generated automatically. Bots can invalidate many existing services, which makes it crucial to understand the main forms of attacks and to seek defense mechanisms. This work presents a wide characterization of the behavior of bots on Twitter. From a real data set containing 19,115 bots, several characteristics of bots were identified, extracted from behavior and writing patterns, that have discriminative power. From these features, we present an automatic detection method capable to detect 92% of the bots while only less than 1% of real users are misclassified.