People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.
This dual motivation between and has also been found to drive sharing activities in online contexts. Research in online word-of-mouth referrals has shown individuation and altruism as two dominant motivations for sharing @cite_17 @cite_27 @cite_30 . Studies of knowledge sharing in online professional communities reveal a similar pattern: people share knowledge to enhance their professional reputation or when they enjoy helping others @cite_36 . When sharing items such as movies, these two motivations of individuation and altruism can be mapped to sharing based on one's own interests or the audience's. These, in turn, can be estimated from the rich preference data available online, then used to study the relative influence of these factors.
@cite_30 @cite_27 @cite_36 @cite_17
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Despite the increasing popularity of viral marketing, factors critical to such a new communication medium remain largely unknown. This paper examines one of the critical factors, namely Internet users' motivations to pass along online content. Conceptualizing the act of forwarding online content as a special case of a more general communication behavior, we identify four potential motivations: (1) the need to be part of a group, (2) the need to be individualistic, (3) the need to be altruistic, and (4) the need for personal growth. Using a survey of young adults, we examine the relationship between these motivations and the frequency of passing along online content. We also investigate if high trait curiosity can indirectly lead to more forwarding by increasing the amount of online content consumed. Results show that Internet users, who are more individualistic and or more altruistic, tend to forward more online content than others. This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8 , and inference quality of explicit interests by up to 101 . The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size. The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers.