This study uncovers how coordinated campaigns shaped the information environment on Twitter during the 2023 Israel-Hamas War, analyzing 4.5 million tweets across 11 coordinated groups using multimodal methods.

This paper presents a machine learning framework to detect tweets that get coordinated replies as well as repliers involved in such attacks.

Presents labeled datasets combining platform-verified IO posts with over 13M posts by 303k control accounts across 26 information operation campaigns.

Surveys translation-based and neural network-based knowledge graph embedding models, discussing applications that leverage deep learning and social media data.

Reviews computational propaganda — the use of algorithms, automation, and human curation to distribute misleading information — and surveys bot detection systems developed to counter these threats.

Introduces MEIU22, a multi-platform dataset covering 1,011 candidates from October to December 2022 across Twitter, Facebook, Instagram, Reddit, and 4chan.

Presents a method to estimate user deletion behavior on social media and investigates online manipulation tactics observed through deletion patterns, revealing coordinated inauthentic activity on Twitter.