Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

TL;DR

This paper presents a real-time framework for analyzing meme diffusion in social media, focusing on detecting astroturfing and misinformation during U.S. political elections using Twitter data.

Contribution

It introduces an extensible framework and web service for mining, visualizing, and classifying meme spread, with novel detection methods for abusive behaviors and misinformation.

Findings

Uncovered cases of abusive behaviors in social media streams

Preliminary results show effectiveness of supervised learning for suspicious meme detection

Framework enables real-time analysis of political meme diffusion

Abstract

Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We introduce an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. We describe a Web service that leverages this framework to track political memes in Twitter and help detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We present some cases of abusive behaviors uncovered by our service. Finally, we discuss promising preliminary results on the detection of suspicious memes via supervised learning based on features extracted from the topology of the diffusion networks, sentiment analysis, and crowdsourced annotations.