Work

Content-based features predict social media influence operations

Year: 2020

Type: article

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.

Source: Science Advances

DOI: 10.1126/sciadv.abb5824

Authors: Meysam Alizadeh, Jacob N. Shapiro, Cody Buntain, Joshua A. Tucker

Institutions: Princeton University, New Jersey Institute of Technology, New York University

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $4,500

Funders: Bertelsmann Stiftung, Microsoft