University of Iowa: Measurement and Early Detection of Third-Party Application Abuse on Twitter. This is a PDF. “Third-party applications present a convenient way for attackers to orchestrate a large number of fake and compromised accounts on popular online social networks. Despite recent high-profile reports of third-party application abuse on Twitter, Facebook, and Google, prior work lacks automated approaches for accurate and early detection of abusive applications. In this paper, we perform a longitudinal study of abusive third-party applications on Twitter that perform a variety of malicious and spam activities in violation of Twitter’s terms of service. Our measurements over a period of 16 months demonstrate an ongoing arms race between attackers continuously registering and abusing new applications and Twitter trying to detect them. We find that hundreds of thousands of abusive applications remain undetected by Twitter for several months while posting tens of millions of tweets. To this end, we propose a machine learning approach for accurate and early detection of abusive Twitter applications by analyzing their first few tweets.”