Profiling Astroturfing Facebook Users During Three Contiguous Israeli Election Periods

Astroturfing is the practice of using a fake online social media (OSM) profile in order to influence public opinion, while giving the impression that the profile belongs to an authentic human user. The proliferation of such profiles in the political OSM scene is becoming more and more common, and as such, poses a threat to democratic processes, especially around election time. We present here a unique technique for exposing astroturfing profiles for what they are. This technique was developed and tested using a large dataset which we collected from Facebook, containing over four million comments, posted within a time span of 15 months, during which three rounds of elections took place in Israel. This political situation provided a rare opportunity to create and research what is-to the best of our knowledge-one of the largest datasets of authentic and fake profiles curated from Facebook to this day. These unique circumstances of three elections held contiguously within a short time span brought forth a set of previously unrecognized features, creating a temporal layer in the feature set, with regard to the date of elections, such as pre-election and post-election, which greatly improved classification results. By creating and selecting the right features, and using various machine learning algorithms, we are able to identify such profiles, with good precision and recall. This setup can serve as a platform for large scale astroturfing profiling in regular situations. Our unique dataset is planned to be made publicly available to the research community.