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Title: Journalistic Relevance Classification in Social Network Messages: an Exploratory Approach
Authors: Miguel Oliveira Sandim
Paula Teixeira Fortuna
Álvaro Figueira
Luciana Gomes Oliveira
Issue Date: 2017
Abstract: Social networks are becoming a wide repository of information, some of which may be of interest for general audiences. In this study we investigate which features may be extracted from single posts propagated throughout a social network, and that are indicative of its relevance, from a journalistic perspective. We then test these features with a set of supervised learning algorithms in order to evaluate our hypothesis. The main results indicate that if a text fragment is pointed out as being interesting, meaningful for the majority of people, reliable and with a wide scope, then it is more likely to be considered as relevant. This approach also presents promising results when validated with several well-known learning algorithms.
metadata.dc.type: conferenceObject
Appears in Collections:CRACS - Indexed Articles in Conferences
CSIG - Indexed Articles in Conferences

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