SOCIAl Networks and Media
The digital revolution has placed us in a unique position in history. We have now access to high-resolution datasets proxies of our activities, interactions, and communication. We have used these datasets to develop models of human behavior and dynamics. We studied how we share our limited attention and how this shapes the evolution of our online social circles and communication [1, 2]. We also used Twitter data to study the predictability of social events as simple popularity contests [3] and usage of language on Twitter [4]. We have studied the dynamics of information exchange through social media [5]. We developed a method for the identification of the characteristic time scale of social events as they develop, gather force, and burst into the public eye. I have also characterized the structural signature typical of the mediated communication dynamics providing one of the first principled methods for early prediction of social coordination. We have tackled the challenges of including complex and realistic properties of socio-technical systems in models of social contagion on networks [6]. In particular, we defined a framework to characterize the dynamics of rumour spreading model in structured populations, and analytically find a previously uncharacterized dynamical phase transition that separates the local and global contagion regimes.
The low cost of interacting with multiple people, simultaneously or asynchronously, without geographical constrains, facilitates the exchange of information at a faster pace and with a diverse set of contacts. Despite such unprecedented possibilities for interactions and consumption of information, we are still bounded by cognitive and temporal constraints. As a consequence, ideas, memes, individuals, companies and institutions compete for our limited attention which, in the current landscape, has acquired a real economic value. In this context, social platforms use algorithms tailored to improve the online experience by ordering and filtering information judged relevant to a particular individual (or social media user). Seemingly innocuous, algorithms are designed to keep users engaged and select which information is presented to them. They act as gatekeepers and intermediaries of information, functions that traditionally have been covered by news papers and thus by hand curated editorial choices. The shift towards algorithmic curation seems a natural consequence of the digital revolution. However, its short and long term societal effects are far from clear and matter of a heated debate.
The low cost of interacting with multiple people, simultaneously or asynchronously, without geographical constrains, facilitates the exchange of information at a faster pace and with a diverse set of contacts. Despite such unprecedented possibilities for interactions and consumption of information, we are still bounded by cognitive and temporal constraints. As a consequence, ideas, memes, individuals, companies and institutions compete for our limited attention which, in the current landscape, has acquired a real economic value. In this context, social platforms use algorithms tailored to improve the online experience by ordering and filtering information judged relevant to a particular individual (or social media user). Seemingly innocuous, algorithms are designed to keep users engaged and select which information is presented to them. They act as gatekeepers and intermediaries of information, functions that traditionally have been covered by news papers and thus by hand curated editorial choices. The shift towards algorithmic curation seems a natural consequence of the digital revolution. However, its short and long term societal effects are far from clear and matter of a heated debate.
