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Social Network Analysis and Mining

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01.12.2020 | Original Article

A Twitter-based approach of news media impartiality in multipartite political scenes

verfasst von: Sotirios Gyftopoulos, George Drosatos, Giorgos Stamatelatos, Pavlos S. Efraimidis

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2020

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Abstract

News dissemination in the modern world deploys online social networks (OSNs) to instantly and freely convey facts and opinions to Internet users worldwide. Recent research studies the structure of the graph formed by the relationships between news readers and media outlets in OSNs to investigate the profile of the media and derive their political leanings. In this work, we focus on the notion of political impartiality in multipartite political scenes. Our aim is to describe the graph-theoretic attributes of the ideal outlet that exhibits an impartial stance towards all political groups and propose a methodology based on Twitter, an OSN with profound informative and political profile, to algorithmically approximate this ideal medium and evaluate the deviation of popular outlets from it. The magnitude of deviation is used to rank the existing outlets based on their political impartiality and, hence, tackle the bewildering question: Which are the most impartial news media in a political scene?. We utilize our techniques on a snapshot of the Twitter subgraph concerning the Greek political and news media scene in April 2018. The results of our approach are juxtaposed with the findings of a survey provided to a group of political scientists and the efficiency of our proposed methodology is soundly confirmed.

Vorheriger Artikel Towards establishing the effect of self-similarity on influence maximization in online social networks

Nächster Artikel Lifetime distribution of information diffusion on simultaneously growing networks

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Titel: A Twitter-based approach of news media impartiality in multipartite political scenes
verfasst von: Sotirios Gyftopoulos
George Drosatos
Giorgos Stamatelatos
Pavlos S. Efraimidis
Publikationsdatum: 01.12.2020
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2020
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-020-00642-x

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