Social networks

Adolescents are at higher risk of engaging in risky behaviors in online social networks. This project develops digital intervention solutions to motivate, educate, support and engender safe social networking behaviors among adolescents. It significantly extends the current understanding of adolescent motivations for engaging in risky online behaviors and the state-of-the-art solutions for reducing adolescent exposure to such behaviors.

As social media permeates our daily life, there has been a sharp rise in the use of social media to humiliate, bully, and threaten others, which has come with harmful consequences such as emotional distress, depression, and suicide. The October 2014 Pew Research survey shows that 73% of adult Internet users have observed online harassment and 40% have experienced it. The prevalence and serious consequences of online harassment present both social and technological challenges.

Used by hundreds of millions of people every day, online services are central to everyday life. Their popularity and impact make them targets of public opinion skewing attacks, in which those with malicious intent manipulate the image of businesses, mobile applications and products. Website owners often turn to crowdsourcing sites to hire an army of professional fraudsters to paint a fake flattering image for mediocre subjects or trick people into downloading malicious software.

Natural language privacy policies have become a de facto standard to address expectations of notice and choice on the Web. Yet, there is ample evidence that users generally do not read these policies and that those who occasionally do struggle to understand what they read. Initiatives aimed at addressing this problem through the development of machine implementable standards or other solutions that require website operators to adhere to more stringent requirements have run into obstacles, with many website operators showing reluctance to commit to anything more than what they currently do.

This research explores a new approach to securing systems that are based on crowd computations, where the operator polls the opinions of crowds--arbitrary users of the system--to provide a variety of recommendation services. Examples include services like Yelp, YouTube, Twitter, and TripAdvisor. However, today's services are known to suffer from multiple identity (Sybil) attacks, where an attacker creates many identities to subvert the system (e.g., make their business appear to be more popular on Yelp).