Network Manipulation

Graph thinking changes the focus from individual data points to how they interact. We talk with Manita Pote, a PhD student at Indiana University Bloomington, specializing in online trust and safety, with a focus on detecting coordinated manipulation campaigns on social media. Key insights include how coordinated reply attacks target influential figures like journalists and politicians, how machine learning models can detect these inauthentic campaigns using structural and behavioral features, and how deletion patterns reveal efforts to evade moderation or manipulate engagement metrics.

Guest

Manita Pote: Hi, I’m Manita Pote. I am a PhD student in Informatics, Complex Networks and System at Indiana University, Bloomington supervised by Prof. Fil Menczer, Prof. Alessandro Flammini and Prof. Zoran Tiganj. My research interests lie in social media data mining, computational social science, network science to understand abusive user behavior for platform integrity and in developing machine learning/deep learning tools to curb those behaviors. I primarily focus on the detection of inauthentic coordination, identifying coordinated efforts and strategies used in influence operations campaigns on social networks.