Existing studies of how information diffuses across social networks have thus far concentrated on analysing and recovering the spread of deterministic innovations such as URLs, hashtags, and group membership. However investigating how mentions of real-world entities appear and spread has yet to be explored, largely due to the computationally intractable nature of performing large-scale entity extraction. In this paper we present, to the best of our knowledge, one of the first pieces of work to closely examine the diffusion of named entities on social media, using Reddit as our case study platform. We first investigate how named entities can be accurately recognised and extracted from discussion posts. We then use these extracted entities to study the patterns of entity cascades and how the probability of a user adopting an entity (i.e. mentioning it) is associated with exposures to the entity. We put these pieces together by presenting a parallelised diffusion model that can forecast the probability of entity adoption, finding that the influence of adoption between users can be characterised by their prior interactions -- as opposed to whether the users propagated entity-adoptions beforehand. Our findings have important implications for researchers studying influence and language, and for community analysts who wish to understand entity-level influence dynamics.
The study of information adoption and sharing was undertaken by @cite_23 who conducted a large-scale randomised controlled trial to examine the effects of on information diffusion, using the Facebook platform. The authors were able to assign Facebook users with a @math probability to a group, and the remainder to a group and then information (i.e. status posts) posted within the latter's group. found that users who were to information (i.e. those in the feed group) from their friends are more likely to share it on -- implying that such exposure has an influential effect.
@cite_23
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We measure users' attitudes toward interpersonal privacy concerns on Facebook and measure users' strategies for reconciling their concerns with their desire to share content online. To do this, we recruited 260 Facebook users to install a Facebook application that surveyed their privacy concerns, their friend network compositions, the sensitivity of posted content, and their privacy-preserving strategies. By asking participants targeted questions about people randomly selected from their friend network and posts shared on their profiles, we were able to quantify the extent to which users trust their "friends" and the likelihood that their content was being viewed by unintended audiences. We found that while strangers are the most concerning audience, almost 95 of our participants had taken steps to mitigate those concerns. At the same time, we observed that 16.5 of participants had at least one post that they were uncomfortable sharing with a specific friend---someone who likely already had the ability to view it---and that 37 raised more general concerns with sharing their content with friends. We conclude that the current privacy controls allow users to effectively manage the outsider threat, but that they are unsuitable for mitigating concerns over the insider threat---members of the friend network who dynamically become inappropriate audiences based on the context of a post. Discovering groups of online friends who go to the same physical places has numerous potential applications including privacy management, friend recommendation, and contact grouping as in Google+ circles. Until recently, little information was available about places visited by users of online social networking services, so community detection on the social graph could not take this into account. With the rise of services such as Foursquare, Gowalla, and Facebook Places, where users check in to named venues and share their location with their friends, we now have the right data to make this possible. In this work, we propose a way to extract place-focused communities from the social graph by annotating its edges with check-in information. Using traces from two online social networks with location sharing, we show that we can extract groups of friends who meet face-to-face, with many possible benefits for online social services. Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decision process. In online systems, product designers can define how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing. The consequences of these mechanisms are of substantial practical and theoretical interest: passive sharing may increase total peer exposure but active sharing may expose higher quality products to peers who are more likely to adopt. We examine selection effects in online sharing through a large-scale field experiment on Facebook that randomizes whether or not adopters share Offers (coupons) in a passive manner. We derive and estimate a joint discrete choice model of adopters' sharing decisions and their peers' adoption decisions. Our results show that active sharing enables a selection effect that exposes peers who are more likely to adopt than the population exposed under passive sharing. We decompose the selection effect into two distinct mechanisms: active sharers expose peers to higher quality products, and the peers they share with are more likely to adopt independently of product quality. Simulation results show that the user-level mechanism comprises the bulk of the selection effect. The study's findings are among the first to address downstream peer effects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly. Exposure to news, opinion and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using de-identified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks, and examine the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed, and further studied users’ choices to click through to ideologically discordant content. Compared to algorithmic ranking, individuals’ choices about what to consume had a stronger effect limiting exposure to cross-cutting content.