Publication: Mobility and interaction patterns in social networks
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2016-07
Defense date
2016-07-21
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Abstract
The question of analyzing the predictability of human behavior has been widely studied
in literature, to unveil how individuals move, how they can be mobilized and, more
philosophically, to understand to what extent our decisions are random or whether we
are free to choose. As a consequence of humans relate to each other, we also tend to
live in groups at different hierarchies in a social way so it is interesting to analyze how
individual features and choices affect the global structure of a society.
In this work, we explore the limits of human predictability in terms of shopping behavior,
observing that, even when we are constrained to a limited set of possible places where we
can make a purchase, predicting where the next purchase will happen is not accurately
possible to do by only observing the past. The next question is to study how individual
decisions affect emergent phenomena such as the economy or information diffusion across a country. We analyze the contents, temporal and mobility patterns extracted from
users’ social media publications to build a profile of the geographical regions that allow
to predict the unemployment rate. Finally, we also use a mobile phone call dataset
to test whether the dynamics at the urban level, how people create and destroy links
within a city, affect the inter-urban diffusion of diseases, virus or rumors. Our results
suggest that inter-regional structure is robust and does not vary significantly on time so
diffusion processes can be well modeled in terms of static properties of the inter-urban
network.
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Keywords
Social networks, Behavior models, Markov models, Geo-tagged