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.
Most individuals have few close friends, leading to potential isolation after a friend's death. Do social networks heal to fill the space left by the loss? We conduct such a study of self-healing and resilience in social networks. We compared de-identified, aggregate counts of monthly interactions in approximately 15,000 Facebook networks in which someone had died with similar friendship networks of living Facebook users.
Despite the shift from multilateral negotiations on legally binding mitigation commitments to the decentralized nonbinding Intended Nationally Determined Contributions (INDCs) approach in global climate policy, governments and other stakeholders continue to insist that fairness principles guide the overall effort. Key recurring principles in this debate are capacity and historical responsibility.
Social life continues to increasingly occur in digital environments and to be mediated by digital systems. Big data represents the data being generated by the digitization of social life which we break down into three domains: digital life, digitalized life, and digitized traces. We argue that there is enormous potential in using big data to study a variety of phenomena that remain difficult to observe.