Queue - Distributed Development
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Spam and the ongoing battle for the inbox
Communications of the ACM - Spam and the ongoing battle for the inbox
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
Relaxed online SVMs for spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An evaluation of Naive Bayes variants in content-based learning for spam filtering
Intelligent Data Analysis
Email Spam Filtering: A Systematic Review
Foundations and Trends in Information Retrieval
Spamalytics: an empirical analysis of spam marketing conversion
Proceedings of the 15th ACM conference on Computer and communications security
Controlled experiments on the web: survey and practical guide
Data Mining and Knowledge Discovery
A profitless endeavor: phishing as tragedy of the commons
Proceedings of the 2008 workshop on New security paradigms
New filtering approaches for phishing email
Journal of Computer Security - EU-Funded ICT Research on Trust and Security
Evaluating online ad campaigns in a pipeline: causal models at scale
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Re: CAPTCHAs: understanding CAPTCHA-solving services in an economic context
USENIX Security'10 Proceedings of the 19th USENIX conference on Security
Enhanced email spam filtering through combining similarity graphs
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 20th international conference on World wide web
LEET'11 Proceedings of the 4th USENIX conference on Large-scale exploits and emergent threats
Measuring pay-per-install: the commoditization of malware distribution
SEC'11 Proceedings of the 20th USENIX conference on Security
Dirty jobs: the role of freelance labor in web service abuse
SEC'11 Proceedings of the 20th USENIX conference on Security
Show me the money: characterizing spam-advertised revenue
SEC'11 Proceedings of the 20th USENIX conference on Security
An efficient framework for online advertising effectiveness measurement and comparison
Proceedings of the 7th ACM international conference on Web search and data mining
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In this paper we quantify the effect of unsolicited emails (spam) on behavior and engagement of email users. Since performing randomized experiments in this setting is rife with practical and moral issues, we seek to determine causal relationships using observational data, something that is difficult in many cases. Using a novel modification of a user matching method combined with a time series regression on matched user pairs, we develop a framework for such causal inference that is particularly suited for the spam exposure use case. Using our matching technique, we objectively quantify the effect that continued exposure to spam has on user engagement in Yahoo! Mail. We find that indeed spam exposure leads to significantly, both statistically and economically, lower user engagement. The impact is non-linear; large changes impact users in a progressively more negative fashion. The impact is the strongest on "voluntary" categories of engagement such as composed emails and lowest on "responsive" engagement metrics. Our estimation technique and results not only quantify the negative impact of abuse, but also allow decision makers to estimate potential engagement gains from proposed investments in abuse mitigation.