Open AccessInvestigating evaluation and news values in news items that are shared through social media
Author(s) -
Monika Bednarek
Publication year - 2016
Publication title -
corpora
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.284
H-Index - 14
eISSN - 1755-1676
pISSN - 1749-5032
DOI - 10.3366/cor.2016.0093
Subject(s) - news values , mainstream , news media , negativity effect , focus (optics) , social media , journalism , variety (cybernetics) , value (mathematics) , corpus linguistics , linguistics , computer science , sociology , psychology , political science , media studies , world wide web , social psychology , natural language processing , artificial intelligence , philosophy , physics , optics , law , machine learning
The sharing of news through social media platforms is now a significant part of mainstream online media use and is an increasingly important consideration in journalism practice and production. This paper analyses the linguistic characteristics of online news sharing on Facebook, with a focus on evaluation and news values in a corpus of the 100 ‘most shared’ news items from ‘heritage’ English-language news media organisations. Analyses combine corpus linguistic techniques (semantic tagging, frequency analysis, concordancing) with manual, computer-aided annotation. The main focus is on discursive news values analysis (DNVA), which examines how news values are established through semiotic resources, enabling new empirical insights into shared news and adding a specific linguistic focus to the emerging literature on news sharing. Results suggest that all ‘traditional’ news values appear to be construed in the shared news corpus and that there is variety in terms of the items that are widely shared. At the same time, the news values of Eliteness, Superlativeness, Unexpectedness, Negativity and Timeliness seem especially important in the corpus. The findings also indicate that ‘unexpected’ and ‘affective’ news items may be shared more, and that Negativity is a more important news value than Positivity.