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Your search keyword '"del Gobbo, Emiliano"' showing total 18 results
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18 results on '"del Gobbo, Emiliano"'

7. On the Perceptions of Online Learning Due to COVID-19 Pandemic. Case Study: University of Foggia, Italy

Author

del Gobbo, Emiliano, Guarino, Alfonso, Cafarelli, Barbara, Grilli, Luca, Limone, Pierpaolo, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Limone, Pierpaolo, editor, Di Fuccio, Raffaele, editor, and Toto, Giusi Antonia, editor

Published
2022
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10. Facebook Debate on Sea Watch 3 Case: Detecting Offensive Language Through Automatic Topic Mining Techniques

Author

Tontodimamma, Alice, del Gobbo, Emiliano, Russo, Vanessa, Sarra, Annalina, Fontanella, Lara, Gaul, Wolfgang, Managing Editor, Vichi, Maurizio, Managing Editor, Weihs, Claus, Managing Editor, Baier, Daniel, Editorial Board Member, Critchley, Frank, Editorial Board Member, Decker, Reinhold, Editorial Board Member, Diday, Edwin, Editorial Board Member, Greenacre, Michael, Editorial Board Member, Lauro, Carlo Natale, Editorial Board Member, Meulman, Jacqueline, Editorial Board Member, Monari, Paola, Editorial Board Member, Nishisato, Shizuhiko, Editorial Board Member, Ohsumi, Noboru, Editorial Board Member, Opitz, Otto, Editorial Board Member, Ritter, Gunter, Editorial Board Member, Schader, Martin, Editorial Board Member, Mariani, Paolo, editor, and Zenga, Mariangela, editor

Published
2021
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14. Inside Trending Topic Algorithm: How Do Human Interactions Drive Public Opinion in an Artificial Environment.

Author

Russo, Vanessa and del Gobbo, Emiliano

Subjects
*PUBLIC opinion, *SOCIAL interaction, *PUBLIC opinion polls, *SOCIAL network analysis, *TREND setters
Abstract

The object of this research is to exploit the algorithm of Twitter's trending topic (TT) and identify the elements capable of guiding public opinion in the Italian panorama. The underlying hypotheses that guide the whole article, confirmed by the research results, concern the existence of (a) a limited number of elements at the base of each popular hashtag with very high viral power and (b) hashtags transversal to the themes detected by the Twitter algorithm that define specific opinion polls. Through computational techniques, it was possible to extract and process data sets from six specific hashtags highlighted by TT. In a first step through social network analysis, we analyzed the hashtag semantic network to identify the hashtags transversal to the six TTs. Subsequently, we selected for each data set the contents with high sharing power and created a "potential opinion leader" index to identify users with influencer characteristics. Finally, a cross section of social actors able to guide public opinion in the Twittersphere emerged from the intersection between potentially influential users and the viral contents. [ABSTRACT FROM AUTHOR]

Published
2023
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