Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users’ personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications.
predictive modeling; transparency; privacy; comprehensibility; inference; control
Real-world institutions dealing with social dilemma situations are based on mechanisms that are rarely implemented without flaw. Usually real-world mechanisms are noisy and imprecise, that is, which we call ‘fuzzy’. We therefore conducted a novel type of voluntary contributions experiment where we test a mechanism by varying its fuzziness. We focus on a range of fuzzy mechanisms we call ‘meritocratic matching’.
Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results.
The present work proposes the use of social media as a tool for better understanding the relationship between a journalists' social network and the content they produce. Specifically, we ask: what is the relationship between the idealogical leaning of a journalist's social network on Twitter and the news content he or she produces?
We know a lot about fake news. It's an old problem. Academics have been studying it - and how to combat it - for decades. In 1925, Harper's Magazine published "Fake News and the Public," calling it's spread via new communication technologies "a source of unprecedented danger."
Over the past 12 years, nearly 20 U.S. States have adopted voter photo identification laws, which require voters to show a picture ID to vote. These laws have been challenged in numerous lawsuits, resulting in a variety of court decisions and, in several instances, revised legislation.
Understanding the factors of network formation is a fundamental aspect in the study of social dynamics. Online activity provides us with abundance of data that allows us to reconstruct and study social networks. Statistical inference methods are often used to study network formation. Ideally, statistical inference allows the researcher to study the significance of specific factors to the network formation.