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Public discourse and news consumption on online social media: A quantitative, cross-platform analysis of the Italian Referendum

Del Vicario, Michela and Gaito, Sabrina and Quattrociocchi, Walter and Zignani, Matteo and Zollo, Fabiana Public discourse and news consumption on online social media: A quantitative, cross-platform analysis of the Italian Referendum. Working Paper arXiv (Submitted)

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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. In this paper we address such a challenge by focusing on the discussion around the Italian Constitutional Referendum by conducting a quantitative, cross-platform analysis on both Facebook public pages and Twitter accounts. We observe the spontaneous emergence of well-separated communities on both platforms. Such a segregation is completely spontaneous, since no categorization of contents was performed a priori. By exploring the dynamics behind the discussion, we find that users tend to restrict their attention to a specific set of Facebook pages/Twitter accounts. Finally, taking advantage of automatic topic extraction and sentiment analysis techniques, we are able to identify the most controversial topics inside and across both platforms. We measure the distance between how a certain topic is presented in the posts/tweets and the related emotional response of users. Our results provide interesting insights for the understanding of the evolution of the core narratives behind different echo chambers and for the early detection of massive viral phenomena around false claims.

Item Type: Working Paper (Working Paper)
Identification Number: arXiv:1702.06016
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 18 Apr 2017 08:40
Last Modified: 18 Apr 2017 08:40
URI: http://eprints.imtlucca.it/id/eprint/3687

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