Our research goal is facing the new challenge of giving an all-around picture of people’s social behavior.
Individuals are currently used to express their sociality through multiple layers, each associated to a specific medium: from the pletora of online social networks to on-phone communications and offline meetings in mobility.
To this aim we are performing the research activities detailed below.
Misinformation in Social Media
The rising attention to the spreading of fake news and unsubstantiated rumors on online social media and the pivotal role played by confirmation bias led researchers to investigate different aspects
of the phenomenon. Experimental evidence showed that confirmatory information gets accepted even if containing deliberately false claims while dissenting information is mainly ignored or might even increase group polarization. It seems reasonable that, to address misinformation problem properly, we have to understand the main determinants behind content consumption and the emergence of narratives on online social media.
Multiplex Networks from mobile phone data
We are analyzing the communication and the co-location networks of a million subscribers for a two-month period in the area of Milan through a collaboration with the mobile operator H3G.
Users’ temporal patterns on multilayer networks
We are analying the time-series associated to the posting activity of single users across multiple social platforms in terms of level of burstiness.
Multidimensional networks from online social networks
To investigate the adoption and the usage of multiple social platforms, we collected two datasets. The first dataset contains about 19.000 profiles and their activities on several social media crawled from the social media aggregator Alternion. Unlike other datasets in literature, this collection is the largest in terms of number of profiles and the availability of the posting activities. Moreover time-series of the activities of the users across today social media can be extracted. The second dataset collects Facebook and Google+ profiles of about 8.000 people. We gathered this collection through a crawling of one million public profiles on Google+. When links to other profiles in different social media were available, we stored the matched profile and their information retrieved by APIs.
Link strength prediction in online social networks
The interactions occurring on OSN links are becoming an important research topic, in particular their predictability has been partially investigated. In our work we ask whether the interactivity of two connected users can be predicted a) without requiring that users label links with a perceived tie strength; b) requiring that the prediction happens as soon as possible, i.e. at the creation of the link, namely imposing a zero-knowledge about the history of the interactions; and c) assuming no additional information except the timestamp for each interaction.