Strangers you may know: social surveillance and intimacy online

Online social networks like LinkedIn and Facebook regularly use People You May Know (PYMK) algorithms to encourage connectivity among their users. We argue that these algorithms have the unintended effect of making users’ interactions more visible, which can deter users from being intimate online. To test this theory, we analyze the database of a large online social network that lets users buy and exchange electronic greeting cards (eCards) with each other over the network. We find that users are more likely to buy eCards when they have more connections, but less likely to buy them if they have formed connections to friends of friends. We attribute the latter effect to the increased social visibility that comes with connecting to friends of friends, which PYMK algorithms encourage. Our study has implications for how privacy and intimacy interact online, and calls for a deeper investigation into the unintended consequences of algorithms

Social Media Broadcasts and the Maintenance of Diverse Networks

Social media platforms like Facebook, Twitter, and LinkedIn let people broadcast messages to their entire network of contacts all at once. As the number of users and the amount of information they broadcast grow, platform managers face an increasingly pressing problem – which broadcasts should they show their users? To address this question, I study the database of a social media company that started charging its users to receive broadcasts about their contacts. By relating purchase rates to properties of users’ social networks, I identify which ties are most valuable to maintain through broadcasts. I find that strong ties increased purchase rates more than weak ties. However, purchase rates also increased with the structural diversity of users’ ties. Social media platforms should thus prioritize broadcasts from ties that are either strong or structurally diverse

Strangers you may know: social surveillance and intimacy online

Online social networks like LinkedIn and Facebook regularly use People You May Know (PYMK) algorithms to encourage connectivity among their users. We argue that these algorithms have the unintended effect of making users’ interactions more visible, which can deter users from being intimate online. To test this theory, we analyze the database of a large online social network that lets users buy and exchange electronic greeting cards (eCards) with each other over the network. We find that users are more likely to buy eCards when they have more connections, but less likely to buy them if they have formed connections to friends of friends. We attribute the latter effect to the increased social visibility that comes with connecting to friends of friends, which PYMK algorithms encourage. Our study has implications for how privacy and intimacy interact online, and calls for a deeper investigation into the unintended consequences of algorithms

How Partisan Online Environments Shape Communication with Political Outgroups

Social media provide opportunities to consume and share political news in echo chambers, but also to communicate with members of political outgroups. Exposure to political outgroups is often portrayed as the normatively desirable option, although empirically it has mixed effects. With an experimental study, we find that participants who regularly interact with political outgroups on social media share more politically moderate news articles when we assign them to an audience of mostly outgroup versus ingroup members. On the other hand, those who are accustomed to an online echo chamber subsequently polarize when faced with an outgroup audience. Our study holds implications for how a person's online social setting can shape downstream political interactions, and, more broadly, our findings highlight the importance of incorporating pretreatment measures to understand how online environments influence political behavior.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Using social media to promote academic research: Identifying the benefits of twitter for sharing academic work

To disseminate research, scholars once relied on university media services or journal press releases, but today any academic can turn to Twitter to share their published work with a broader audience. The possibility that scholars can push their research out, rather than hope that it is pulled in, holds the potential for scholars to draw wide attention to their research. In this manuscript, we examine whether there are systematic differences in the types of scholars who most benefit from this push model. Specifically, we investigate the extent to which there are gender differences in the dissemination of research via Twitter. We carry out our analyses by tracking tweet patterns for articles published in six journals across two fields (political science and communication), and we pair this Twitter data with demographic and educational data about the authors of the published articles, as well as article citation rates. We find considerable evidence that, overall, article citations are positively correlated with tweets about the article, and we find little evidence to suggest that author gender affects the transmission of research in this new media.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]