VIRALITY OF FLASE RUMOURS IN ONLINE SOCIAL MEDIA Our work addresses the diffusion and consumption patterns of information belonging to different narratives and their effect on shaping communities on a

dataset of 1,2 million of individuals. The applied methodology is an important contribution for the validation of existing theories coming from cognitive and social sciences. The join with data science and complex networks allows an unprecedented level of accuracy in terms of sample (million of users) and techniques. In this case we focus on the users behaviour with respect to information belonging to different narratives. The growth of knowledge fostered by an interconnected world together with the unprecedented acceleration of the scientific progress has exposed society to an increasing level of complexity to explain reality and its phenomena. At the same time, a shift of paradigm in the production and fruition of contents has occurred, utterly changing the quality of information. Everyone on the Web can access and produce a variety of contents by actively participating in the creation, diffusion and reinforcement of different world-views. Such a large heterogeneity of information fostered massive recruitment of people around common interests, world-views and narratives and thus affected the mechanisms behind the public opinion evolution.

Despite the enthusiastic rhetoric about the wisdom of crowds – i.e., collective decisions are wiser than judgements of individuals - the role of socio-technical system in enforcing informed debates and their effects on the public opinion still remain unclear. We show how users afferent to different narratives behave similarly with the information compliant with their system of belief.


Consumption patterns of polarized users.} Cumulative distribution function (CDF) for likes and comments of polarized users (more than 95% of likes on one of the two categories). Information consumption patterns of users belonging to different narratives (conspiracy and scientific news) is nearly comparable 

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