News algorithms not only help the authors to efficiently navigate the sea of available information, but also frame information in ways that influence public discourse and citizenship. Indeed, the likelihood that readers will be exposed to and read given news articles is structured into news algorithms. Thus, ensuring that news algorithms uphold journalistic values is crucial. In this regard, the purpose of this paper is to quantify journalistic values to make them readable by algorithms through taking an exploratory approach to a question that has not been previously investigated.
The author matched the textual indices (extracted from natural language processing/automated content analysis) with human conceptions of journalistic values (derived from survey analysis) by implementing partial least squares path modeling.
The results suggest that the numbers of words or quotes news articles contain have a strong association with the survey respondent assessments of their balance, diversity, importance and factuality. Linguistic polarization was an inverse indicator of respondents’ perception of balance, diversity and importance. While linguistic intensity was useful for gauging respondents’ perception of sensationalism, it was an ineffective indicator of importance and factuality. The numbers of adverbs and adjectives were useful for estimating respondents’ perceptions of factuality and sensationalism. In addition, the greater numbers of quotes, pair quotes and exclamation/question marks in news headlines were associated with respondents’ perception of lower journalistic values. The author also found that the assessment of journalistic values influences the perception of news credibility.
This study has implications for computational journalism, credibility research and news algorithm development.
It represents the first attempt to quantify human conceptions of journalistic values with textual indices.
This work was supported by a grant from Kyung Hee University in 2018 (KHU-20180926). In addition, the author is grateful to the Korea Press Foundation’s News Trust Committee, of which the author was a founding member, for helping inspire this study.
This paper forms part of a special section “Social media mining for journalism”.
CitationDownload as .RIS
Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited