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.
When communicating with others, people customize their message based on their estimation of the audience's knowledge or attitudes @cite_26 . At the same time, much word-of-mouth sharing is driven by people's desire to share items that closely align with or enhance their self-image @cite_7 @cite_37 . Our first question addreses how people balance customization with self-expression.
@cite_37 @cite_26 @cite_7
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ABSTRACTMarketers have long understood that consumers’ self-concepts influence the products they purchase; conversely, products purchased influence people’s self-concepts. Might the same self-enhancement framework apply in to shared online advertisements? Using the symbolic interactionist perspective of identity theory, this study empirically tests the proposition that online consumers use electronic word of mouth, and specifically the sharing of online advertising, to construct and express their self-concepts. The results suggest that self-brand congruity, entertainment value, and product category involvement increase the self-expressiveness of online ads, which then increase the likelihood of sharing those ads. These findings have both theoretical and managerial implications. A key aspect of word of mouth marketing are emotions. Emotions in texts help propagating messages in conventional advertising. In word of mouth scenarios, emotions help to engage consumers and incite to propagate the message further. While the function of emotions in offline marketing in general and word of mouth marketing in particular is rather well understood, online marketing can only offer a limited view on the function of emotions. In this contribution we seek to close this gap. We therefore investigate how emotions function in social media. To do so, we collected more than 30,000 brand marketing messages from the Google+ social networking site. Using state of the art computational linguistics classifiers, we compute the sentiment of these messages. Starting out with Poisson regression-based baseline models, we seek to replicate earlier findings using this large data set. We extend upon earlier research by computing multi-level mixed effects models that compare the function of emotions across different industries. We find that while the well known notion of activating emotions propagating messages holds in general for our data as well. But there are significant differences between the observed industries. We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246 increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98 increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper was accepted by Pradeep Chintagunta and Preyas Desai, special issue editors. This paper studies the way that word-of-mouth communication aggregates the information of individual agents. We find that the structure of the communication process determines whether all agents end up making identical choices, with less communication making this conformity more likely. Despite the players' naive decision rules and the stochastic decision environment, word-of-mouth communication may lead all players to adopt the action that is on average superior. These socially efficient outcomes tend to occur when each agent samples only a few others.