Detecting Coordinated Inauthentic Behavior in Likes on Social Media: Proof of Concept

TL;DR

This paper proposes two machine learning-based jury selection procedures to identify and exclude inauthentic agents in social media likes, improving the accuracy of collective judgments and combating misinformation.

Contribution

It introduces two novel jury selection procedures using clustering and logistic regression to detect inauthentic behavior in social media reactions.

Findings

GMM JSP detects more inauthentic agents

Both JSPs significantly improve vote correctness

Proof of concept supports releasing reactions data for misinformation fight

Abstract

Coordinated inauthentic behavior is used as a tool on social media to shape public opinion by elevating or suppressing topics using systematic engagements -- e.g. through *likes* or similar reactions. In an honest world, reactions may be informative to users when selecting on what to spend their attention: through the wisdom of crowds, summed reactions may help identifying relevant and high-quality content. This is nullified by coordinated inauthentic liking. To restore wisdom-of-crowds effects, it is therefore desirable to separate the inauthentic agents from the wise crowd, and use only the latter as a voting *jury* on the relevance of a post. To this end, we design two *jury selection procedures* (JSPs) that discard agents classified as inauthentic. Using machine learning techniques, both cluster on binary vote data -- one using a Gaussian Mixture Model (GMM JSP), one the k-means algorithm (KM JSP) -- and label agents by logistic regression. We evaluate the jury selection procedures with an agent-based model, and show that the GMM JSP detects more inauthentic agents, but both JSPs select juries with vastly increased correctness of vote by majority. This proof of concept provides an argument for the release of reactions data from social media platforms through a direct use-case in the fight against online misinformation.