Social media

About: Social media is a research topic. Over the lifetime, 76058 publications have been published within this topic receiving 1189027 citations.

Political Polarization on Twitter

Abstract: In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.

Predicting Depression via Social Media

Abstract: Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health.

Finding high-quality content in social media

Abstract: The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans

Social Media Update 2016

Abstract: Over the past decade, Pew Research Center has documented the wide variety of ways in which Americans use social media to seek out information and interact with others. A majority of Americans now say they get news via social media, and half of the public has turned to these sites to learn about the 2016 presidential election. Americans are using social media in the context of work (whether to take a mental break on the job or to seek out employment), while also engaging in an ongoing effort to navigate the complex privacy issues that these sites bring to the forefront. In addition to measuring the broad impact and meaning of social media, since 2012 the Center has also tracked the specific sites and platforms that users turn to in the course of living their social lives online. In that context, a national survey of 1,520 adults conducted March 7-April 4, 2016, finds that Facebook continues to be America’s most popular social networking platform by a substantial margin: Nearly eight-in-ten online Americans (79%) now use Facebook, more than double the share that uses Twitter (24%), Pinterest (31%), Instagram (32%) or LinkedIn (29%). On a total population basis (accounting for Americans who do not use the internet at all), that means that 68% of all U.S. adults are Facebook users, while 28% use Instagram, 26% use Pinterest, 25% use LinkedIn and 21% use Twitter.