Posts of Peril: Detecting Information About Hazards in Text

TL;DR

This paper introduces a new model for detecting hazard-related information in social media posts, demonstrating its effectiveness and potential use in analyzing geopolitical information campaigns and influence operations.

Contribution

The paper develops a novel hazard detection model trained on annotated data, outperforming dictionary approaches and revealing insights into hazard discourse in geopolitical conflicts.

Findings

Hazard information is prevalent in posts discussing conflicts.

Inorganic accounts often highlight hazards to elicit aid.

Differences in hazard mention rates suggest strategic framing by information operators.

Abstract

Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media. However, an indicator that is often overlooked is the presence or absence of information concerning harms or hazards. Here, we develop a new model to detect information concerning hazards, trained on a new collection of annotated X posts. We show that not only does this model perform well (outperforming, e.g., dictionary approaches), but that the hazard information it extracts is not strongly correlated with common indicators. To demonstrate the utility of our tool, we apply it to two datasets of X posts that discuss important geopolitical events, namely the Israel-Hamas war and the 2022 French national election. In both cases, we find that hazard information, especially information concerning conflict, is common. We extract accounts associated with information campaigns from each data set to explore how information about hazards could be used to attempt to influence geopolitical events. We find that inorganic accounts representing the viewpoints of weaker sides in a conflict often discuss hazards to civilians, potentially as a way to elicit aid for the weaker side. Moreover, the rate at which these hazards are mentioned differs markedly from organic accounts, likely reflecting information operators' efforts to frame the given geopolitical event for strategic purposes. These results are first steps towards exploring hazards within an information warfare environment. The model is shared as a Python package to help researchers and journalists analyze hazard content. The model, along with data and annotations, is available in the following repository: https://github.com/KeithBurghardt/DetectHazards.