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The massive amounts of data that social media generates has facilitated the study of online human behavior on a scale unimaginable a few years ago. At the same time, the much discussed apparent randomness with which people interact online makes it appear as if these studies cannot reveal predictive social behaviors that could be used for developing better platforms and services. We use two large social databases to measure the mutual information entropy that both individual and group actions generate as they evolve over time. We show that user's interaction sequences have strong deterministic components, in contrast with existing assumptions and models. In addition, we show that individual interactions are more predictable when users act on their own rather than when attending group activities.

The authors designed and ran an experiment to measure social influence in online recommender systems, specifically, how often people’s choices are changed by others’ recommendations when facing different levels of confirmation and conformity pressures. In this experiment, participants were first asked to provide their preference from pairs of items. They were then asked to make second choices about the same pairs with knowledge of other people’s preferences. The results show that other people’s opinions significantly sway people’s own choices. The influence is stronger when people are required to make their second decision sometime later (22.4%) rather than immediately (14.1%). Moreover, people seem to be most likely to reverse their choices when facing a moderate, as opposed to large, number of opposing opinions. Finally, the time people spend making the first decision significantly predicts whether they will reverse their decisions later on, whereas demographics such as age and gender do not. These results have implications for consumer behavior research as well as online marketing strategies. [ABSTRACT FROM PUBLISHER]

We show through an analysis of a massive data set from YouTube that the productivity exhibited in crowdsourcing exhibits a strong positive dependence on attention, measured by the number of downloads. Conversely, a lack of attention leads to a decrease in the number of videos uploaded and the consequent drop in productivity, which in many cases asymptotes to no uploads whatsoever. Moreover, short-term contributors compare their performance to the average contributor's performance while long-term contributors compare it to their own media. [ABSTRACT FROM AUTHOR]