The principle of homophily says that people associate with other groups of people who are mostly like themselves. Many online communities are structured around groups of socially similar individuals. On Twitter, however, people are exposed to multiple, diverse points of view through the public timeline. The authors captured 30,000 tweets about the shooting of George Tiller, a late-term abortion doctor, and the subsequent conversations among pro-life and pro-choice advocates. They found that replies between like-minded individuals strengthen group identity, whereas replies (...) between different-minded individuals reinforce in-group and out-group affiliation. Their results show that people are exposed to broader viewpoints than they were before but are limited in their ability to engage in meaningful discussion. They conclude with implications for different kinds of social participation on Twitter more generally. (shrink)

Published in 2014, the Facebook “emotional contagion” study prompted widespread discussions about the ethics of manipulating social media content. By and large, researchers focused on the lack of corporate institutional review boards and informed consent procedures, missing the crux of what upset people about both the study and Facebook’s underlying practices. This essay examines the reactions that unfolded, arguing the public’s growing discomfort with “big data” fueled the anger. To address these concerns, we need to start imagining a socio-technical approach (...) to ethics that does not differentiate between corporate and research practices. (shrink)

Algorithms and data-driven technologies are increasingly being embraced by a variety of different sectors and institutions. This paper examines how algorithms and data-driven technologies, enacted by an organization like Facebook, can induce similarity across an industry. Using theories from organizational sociology and neoinstitutionalism, this paper traces the bureaucratic roots of Big Data and algorithms to examine the institutional dependencies that emerge and are mediated through data-driven and algorithmic logics. This type of analysis sheds light on how organizational contexts are embedded (...) into algorithms, which can then become embedded within other organizational and individual practices. By investigating technical practices as organizational and bureaucratic, discussions about accountability and decision-making can be reframed. (shrink)