Troll detection : A comparative study in detecting troll farms on Twitter using cluster analysis
Martin Engelin, Felix De Silva · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2016
The purpose of this research is to test whether clustering algorithmscan be used to detect troll farms in social networks. Troll farms are profes-sional organizations that spread disinformation online via fake personas.The research involves a comparative study of two different clustering algo-rithms and a dataset of Twitter users and posts that includes a fabricatedtroll farm. By comparing the results and the implementations of the K-means as well as the DBSCAN algorithm we have concluded that clusteranalysis can be used to detect troll farms and that DBSCAN is bettersuited for this particular problem compared to K-means.