Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
ACM Conference on Human Factors in Computing Systems
Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution.
In particular, we find support for social learning: newcomers who see their friends contributing go on to share more content themselves. For newcomers who are initially inclined to contribute, receiving feedback and having a wide audience are also predictors of increased sharing. On the other hand, singling out appears to affect only those newcomers who are not initially inclined to share. The paper concludes with design implications for motivating newcomer sharing in online communities.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
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Barlas Oğuz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih