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Graph Neural Networks Learn Twitter Bot Behaviour
Albert Orozco Camacho

Social media trends are increasingly taking a significant role for the understanding of modern social dynamics. Platforms like Twitter enable democratic interaction within users, thus, providing an opportunity to inject influential ideas within formed communities. In this work, we take a look at how the Twitter landscape gets constantly shaped by automatically generated content.Twitter bot activity can bet raced 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. Furthermore, 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.

Author Information

Albert Orozco Camacho (McGill University / Mila - Québec AI Insititute)

My name’s Albert, I am originally from Guadalajara, México and I enjoy all things artificial intelligence. 😎 Currently, I am a Research Master’s student at Mila - Québec AI Institute and the Reasoning and Learning Lab of McGill University under Prof Reihaneh Rabbany’s supervision. My current research interests rely in the intersection of Network Science and Natural Language Processing. I have a broad spectrum of interests in AI: from the theoretical foundations of deep learning to applications regarding NLP, chatbots, and social networks. My current goals are directed towards enhancing how humans and machines communicate and understand themselves, as well as providing elegant models for such tasks.

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