Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems

TL;DR

This paper introduces the Interval-censored Transformer Hawkes model to detect information operations on social media by analyzing reaction patterns, even with missing data, achieving high accuracy in identifying state-backed agents and predicting content categories.

Contribution

The work develops a novel model and data encoding scheme for detecting information operations using social media reaction data, handling missing data and uncovering coordinated behaviors.

Findings

Successfully detects state-backed agents on Twitter.

Predicts content categories using reaction timing patterns.

Reveals coordinated behavior among Russian-backed agents.

Abstract

Social media is being increasingly weaponized by state-backed actors to elicit reactions, push narratives and sway public opinion. These are known as Information Operations (IO). The covert nature of IO makes their detection difficult. This is further amplified by missing data due to the user and content removal and privacy requirements. This work advances the hypothesis that the very reactions that Information Operations seek to elicit within the target social systems can be used to detect them. We propose an Interval-censored Transformer Hawkes (IC-TH) architecture and a novel data encoding scheme to account for both observed and missing data. We derive a novel log-likelihood function that we deploy together with a contrastive learning procedure. We showcase the performance of IC-TH on three real-world Twitter datasets and two learning tasks: future popularity prediction and item category prediction. The latter is particularly significant. Using the retweeting timing and patterns solely, we can predict the category of YouTube videos, guess whether news publishers are reputable or controversial and, most importantly, identify state-backed IO agent accounts. Additional qualitative investigations uncover that the automatically discovered clusters of Russian-backed agents appear to coordinate their behavior, activating simultaneously to push specific narratives.