On Rumor Source Detection and Its Experimental Verification on Twitter

This paper analysis the rumor source detection on three Twitter networks of different sizes: 1K, 10K and 100K tweets. At first step, an algorithm was designed, that selects from all users a set of potential rumormongers, who initiated the fake content tweet. The next step was based on tracking of propagation trails by (1) randomly distributed, (2) maximum, (3) minimum, and (4) median weight of node in the retweet trees. Given these postulates, the study describes an empirical investigation of finding the position of the rumor-teller, calculating the length of propagation path and using statistical methods to interpret and then report basic results. The results showed that we are not able to separate the initial rumor users from the most influential spreaders in the small networks. However, in the big network - 100K - those classifications are expected to bring a satisfactory result.