Algorithmic Approaches for
Unraveling User Behavior on Social Media

Summary

In the contemporary, rapidly changing society, social media platforms have seamlessly integrated into the very essence of our everyday existence. With countless users actively participating in interactions and content consumption on a daily basis, these platforms form a complex and diverse network of connections. This significant increase in online engagement has triggered a pressing necessity to gain a deeper understanding of user behavior, thus igniting our drive to pursue the proposed research. At its core, our primary goal revolves around analyzing user trails and interactions, potentially incorporating additional data on the sentiments expressed by users. The research comprises three interlinked tracks, creating a comprehensive approach to our investigation.

• Track 1 focuses on comprehending timestamped trails resulting from sequential user actions within a defined space. Our primary aim is to reveal valuable understandings regarding patterns and behaviors exhibited by users through these trails.
• In Track 2, our dedicated effort is directed towards mining user interactions, specifically addressing unresolved challenges in correlation clustering, dense subgraph discovery and community detection.

Code

References to software tools will be provided here.

Datasets

References to both established datasets and those we've developed will be found here.

Publications

Publications will be found here here.