Content-based features predict social media influence operations

Document Type

Article

Publication Date

7-1-2020

Abstract

We study how easy it is to distinguish influence operations from organic social media activity by assessing the performance of a platform-agnostic machine learning approach. Our method uses public activity to detect content that is part of coordinated influence operations based on human-interpretable features derived solely from content. We test this method on publicly available Twitter data on Chinese, Russian, and Venezuelan troll activity targeting the United States, as well as the Reddit dataset of Russian influence efforts. To assess how well content-based features distinguish these influence operations from random samples of general and political American users, we train and test classifiers on a monthly basis for each campaign across five prediction tasks. Content-based features perform well across period, country, platform, and prediction task. Industrialized production of influence campaign content leaves a distinctive signal in user-generated content that allows tracking of campaigns from month to month and across different accounts.

Identifier

85090076325 (Scopus)

Publication Title

Science Advances

External Full Text Location

https://doi.org/10.1126/sciadv.abb5824

e-ISSN

23752548

PubMed ID

32832674

Issue

30

Volume

6

This document is currently not available here.

Share

COinS