Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems
Quyu Kong, Pio Calderon, Rohit Ram, Olga Boichak, Marian-Andrei Rizoiu

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…
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Taxonomy
TopicsDemographic Trends and Gender Preferences
