Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
ND-Sync: Detecting Synchronized Fraud Activities
verfasst von : Maria Giatsoglou, Despoina Chatzakou, Neil Shah, Alex Beutel, Christos Faloutsos, Athena Vakali
Erschienen in: Advances in Knowledge Discovery and Data Mining
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Given the retweeting activity for the posts of several Twitter users, how can we distinguish organic activity from spammy retweets by paid followers to boost a post’s appearance of popularity? More generally, given groups of observations, can we spot strange groups? Our main intuition is that organic behavior has more variability, while fraudulent behavior, like retweets by botnet members, is more synchronized. We refer to the detection of such
synchronized
observations as the
Synchonization Fraud
problem, and we study a specific instance of it,
Retweet Fraud Detection
, manifested in Twitter. Here, we propose: (A)
ND-Sync
, an efficient method for detecting
group fraud
, and (B) a set of carefully designed features for characterizing retweet threads.
ND-Sync
is
effective
in spotting retweet fraudsters,
robust
to different types of abnormal activity, and
adaptable
as it can easily incorporate additional features. Our method achieves a 97% accuracy on a real dataset of 12 million retweets crawled from Twitter.