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Social Media Stories. Event detection in heterogeneous streams of documents applied to the study of information spreading across social and news media

Abstract : Social Media, and Twitter in particular, has become a privileged source of information for journalists in recent years. Most of them monitor Twitter, in the search for newsworthy stories. This thesis aims to investigate and to quantify the effect of this technological change on editorial decisions. Does the popularity of a story affects the way it is covered by traditional news media, regardless of its intrinsic interest?To highlight this relationship, we take a multidisciplinary approach at the crossroads of computer science and economics: first, we design a novel approach to collect a representative sample of 70% of all French tweets emitted during an entire year. Second, we study different types of algorithms to automatically discover tweets that relate to the same stories. We test several vector representations of tweets, looking at both text and text-image representations, Third, we design a new method to group together Twitter events and media events. Finally, we design an econometric instrument to identify a causal effect of the popularity of an event on Twitter on its coverage by traditional media. We show that the popularity of a story on Twitter does have an effect on the number of articles devoted to it by traditional media, with an increase of about 1 article per 1000 additional tweets.
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https://tel.archives-ouvertes.fr/tel-02987720
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Submitted on : Wednesday, November 4, 2020 - 10:21:08 AM
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  • HAL Id : tel-02987720, version 1

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Béatrice Mazoyer. Social Media Stories. Event detection in heterogeneous streams of documents applied to the study of information spreading across social and news media. Social and Information Networks [cs.SI]. Université Paris-Saclay, 2020. English. ⟨NNT : 2020UPASC009⟩. ⟨tel-02987720⟩

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