According to the World Economic Forum, massive digital misinformation is one of the main threats to our society [1]. Our recent studies [2] [3] [4] show that users online tend to select information by confirmation bias and to join virtual echo chambers where they reinforce and polarize their beliefs. Considered as a liberalizing technology as exemplified by Arab Spring to Maidan, hopes were high that social media might bring innovation and democracy across the world. However, social media have the power to inform, engage or mobilize, as well as the power to misinform, manipulate or control.

In such a disintermediated environment, the public deals with a large amount of misleading information generated by nationalists, populists and conspirators, that is corrupting reliable sources at the heart. This project aims to study, quantitatively characterize, and model both the process of  spreading news and the consumption of news for the early detection of trends in public opinion.The project architecture is three-fold: the first layer will focus on the implementation of data gathering software and tools used in spreading news; the second will implement a dashboard with visual analytics tools for predicting social trends from news; the third layer will implement services for the mobile application market to exploit the sensing platform.

The main concern is that if people select information according to their personal beliefs and emotional states (confirmation bias) we need to better understand the process of news spreading and consumption to address the design of efficient communication strategies accounting for the cognitive needs of users. In fact, the process of accepting a claim (whether documented or not) may be altered by normative social influence or by the coherence with one’s individual system of beliefs as is well documented in the literature on cognitive and social psychology of communication. At the extreme of the spectrum, conspiracy theorists tend to explain significant social or political aspects as plots conceived by powerful individuals or organizations, and they share an important characteristic with so-called “urban legends”: the object of the narratives inevitably threaten the established social order or well being and focus on what communities and social groups deeply fear. These phenomena can be considered as a sort of “thermometer” of social mood. As these kinds of arguments can sometimes reject logic or science, alternative explanations are invoked to replace evidence. For instance, people who reject the link between HIV and AIDS generally believe that AIDS was created by the U.S. Government to control the African American population. Since unsubstantiated claims are proliferated on the Internet, what could happen if they come to be used as the basis for policy making?

We aim to develop a permanent observatory of news spreading, accounting for news consumption on social media that will implement models, metrics and tools that support journalists, press rooms, and policy makers with data driven techniques. Tools will be made accessible through web services to stimulate the development of mobile and web applications that can exploit data and models. We will connect online behaviours (likes, check-ins, mentions, hashtags, queries, clicks on Facebook, FourSquare, Twitter, Google, and Wikipedia) to social trend data obtained from official statistical bureaus.  We will consider multiple geographical granularities, providing a new approach to “nowcast” and similar measures in near real-time. and at exceptional resolutions. To achieve this goal, we will study structural properties of social networks formed by users online, how they correlate with “offline” social networks (e.g., social networks maintained through face-to-face interactions) and how these structures impact information diffusion and opinion dynamics in online social environments. Models will allow simulations of the impact of certain modifications to any of these elements on the others, thus potentially highlighting critical features to be safeguarded, as well as driving effective intervention.

[1] "How does misinformation spread online? | World Economic Forum." 2016. 21 Apr. 2016 <https://www.weforum.org/agenda/2016/01/q-a-walter-quattrociocchi-digital-wildfires/>

[2] Bessi, Alessandro et al. "Science vs conspiracy: Collective narratives in the age of misinformation." PloS one 10.2 (2015): e0118093.

[3] Bessi, Alessandro et al. "Viral misinformation: The role of homophily and polarization." Proceedings of the 24th International Conference on World Wide Web Companion 18 May. 2015: 355-356.

[4] Del Vicario, Michela et al. "The spreading of misinformation online." Proceedings of the National Academy of Sciences 113.3 (2016): 554-559.