
Graph Neural Networks Learn Twitter Bot Behaviour
Author(s) -
Albert Orozco,
Sacha Lévy,
Reihaneh Rabbany
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.52591/lxai2020121213
Subject(s) - computer science , social media , graph , set (abstract data type) , task (project management) , artificial neural network , contrast (vision) , artificial intelligence , social network (sociolinguistics) , machine learning , information retrieval , world wide web , data science , theoretical computer science , management , economics , programming language
Social media trends are increasingly taking a significant role for the understanding of modern social dynamics. In this work, we take a look at how the Twitter landscape gets constantly shaped by automatically generated content. Twitter bot activity can be traced via network abstractions which, we hypothesize, can be learned through state-of-the-art graph neural network techniques. We employ a large bot database, continuously updated by Twitter, to learn how likely is that a user is mentioned by a bot, as well as, for a hashtag. Thus, we model this likelihood as a link prediction task between the set of users and hashtags. Moreover, we contrast our results by performing similar experiments on a crawled data set of real users.