Abstract
As social animals, social interactions play a fundamental role in shaping our emotional well-being. The emergence of online social networks over the past decade has allowed us to study human social behavior at a previously unimaginable scale and level of detail through the availability of extensive detailed social records for billions of individuals. In this chapter we review several recent results on the structure of the social networks of which we are all a part of. In particular we will analyze how simple mechanisms of network formation such as Preferential Attachment result in broad tailed degree distributions and assortativity that are characteristic of this kind of network. A result of positive degree assortativity is the fact that hubs are necessarily connected to by lower degree nodes, a fact that leads to the so-called Friendship Paradox. Finally, we consider a novel measure of Subjective Well Being or happiness that is derived from a longitudinal sentiment analysis of the content written by each user over an extended period of time. We find that such Happiness is correlated with Popularity within social networks resulting in a so-called Happiness Paradox. In other words, individuals with many friends are indeed happier and their happiness gives a large number of people the impression that they are less happy than their friends on average. This finding may point to explanations of other paradoxical results; in spite of satisfying a primordial human need for social relations social media use has been found to lead to higher levels of psychological and social dysfunction.
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Bollen J, Gonçalves B, Ruan G, Mao H (2011) Happiness is assortative in online social networks. Artif Life 17(3):237–251. arxiv:1103.0784, https://doi.org/10.1162/artl_a_00034
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Bollen J, Gonçalves B, Ruan G, Mao H (2011) Happiness is assortative in online social networks. Artif Life 17:237–251
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Bollen, J., Gonçalves, B. (2018). Network Happiness: How Online Social Interactions Relate to Our Well Being. In: Lehmann, S., Ahn, YY. (eds) Complex Spreading Phenomena in Social Systems. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-77332-2_14
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DOI: https://doi.org/10.1007/978-3-319-77332-2_14
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