Misleading Repurposing on Twitter

TL;DR

This study investigates misleading account repurposing on Twitter, identifying over 100,000 potentially repurposed accounts and analyzing their characteristics, with implications for platform security, data integrity, and user deception.

Contribution

It introduces a new definition and methodology for detecting repurposed accounts using supervised learning and large-scale data analysis.

Findings

Over 100,000 accounts potentially repurposed.

Repurposed accounts often inactive before rebranding.

High follower count accounts are targeted or manipulated.

Abstract

We present the first in-depth and large-scale study of misleading repurposing, in which a malicious user changes the identity of their social media account via, among other things, changes to the profile attributes in order to use the account for a new purpose while retaining their followers. We propose a definition for the behavior and a methodology that uses supervised learning on data mined from the Internet Archive's Twitter Stream Grab to flag repurposed accounts. We found over 100,000 accounts that may have been repurposed. We also characterize repurposed accounts and found that they are more likely to be repurposed after a period of inactivity and deleting old tweets. We also provide evidence that adversaries target accounts with high follower counts to repurpose, and some make them have high follower counts by participating in follow-back schemes. The results we present have implications for the security and integrity of social media platforms, for data science studies in how historical data is considered, and for society at large in how users can be deceived about the popularity of an opinion.