Arunesh Mathur will present his Pre FPO "Identifying and Measuring Manipulative Practices at Scale on the Web" on May 1, 2020 at 10am via Zoom.
The members of his committee are as follows: Marshini Chetty (adviser, reader), Arvind Narayanan (reader); Brandon Stewart, Jonathan Mayer, and Janet Vertesi (non-readers).
All are welcome to attend. Please see abstract below.
Abstract: The emerging threat of digital manipulation has raised numerous societal concerns, such as a loss of trust in online content, invasion of privacy, financial loss, and loss of autonomy. While recent work has begun to document the various kinds of manipulative threats that users encounter on a daily basis, we still lack a systematic and rigorous understanding of their prevalence on digital platforms. Building up this knowledge is necessary to gain a deeper understanding of the solutions we can develop to inform and protect users in light of these new threats.
In this dissertation, I present automated methods that combine web crawling and machine learning to uncover the prevalence of these manipulative practices at scale. Using these methods, I examine three distinct practices on the web. First, I describe how nearly 90% of online advertising content (endorsements) on platforms like YouTube is not disclosed—misleading users into believing they are viewing unbiased, non-advertising content. Second, I describe how 1,818 dark patterns on 1,254 shopping websites mislead and deceive users into making decisions they would otherwise not make. Third, in ongoing research, I describe how emails sent by political campaigns and organizations in the US employ clickbait and other deceptive tactics to influence and solicit donations from the public. I conclude with how the lessons learned from these measurements can be used to build technical defenses and lay out policy recommendations to mitigate their effects.