Microtask crowdsourcing systems such as FoldIt and ESP partition work into short, self-contained microtasks, reducing barriers to contribute, increasing parallelism, and reducing the time to complete work. Could this model be applied to software development? To explore this question, we are designing a development process and cloud-based IDE for crowd development.
Anti-social behavior such as flaming and griefing is pervasive and problematic in many online venues. This behavior breaks established norms and unsettles the well-being and development of online communities. In a popular online game, Riot Games's League of Legends, the game company received tens of thousands of complaints about others every day. To regulate what they call "toxic" behavior, Riot devised the "Tribunal" system as a way of letting the community to police itself. The Tribunal is a crowdsoucing system that empowers players to identify and judge misbehavior.
In the era of big data and personalization, websites and (mobile) applications collect an increasingly large amount of personal information about their users. The large majority of users decide to disclose some but not all information that is requested from them. They trade off the anticipated benefits with the privacy risks of disclosure, a decision process that has been dubbed privacy calculus. Such decisions are inherently difficult though, because they may have uncertain repercussions later on that are difficult to weigh against the (possibly immediate) gratification of disclosure. How can we help users to balance the benefits and risks of information disclosure in a user-friendly manner, so that they can make good privacy decisions?
Research shows that sharing one’s location can help people stay connected, coordinate daily activities, and provide a sense of comfort and safety . Recently, smartphones and location-based services (LBS) have become widely available in developed countries , but only a small percentage of smartphone users have ever tried sharing location with other people . Our work aims to understand real-world factors shaping behaviors and attitudes towards social location-sharing, especially in regards to why people avoid or abandon the technology, or limit their usage.