The compelling argument concept, one of the least studied components of attribute agenda setting, suggests that some attributes increase the salience of an object on the public agenda of issues. By conducting two studies, this article examines the compelling argument concept applying both manual content analysis (Study 1) and computerized-analysis tools (Study 2), considering frequency and degree centrality as measures of attribute salience. Results show that the application of computer-aided methods and mathematical techniques can efficiently identify attributes and estimate degree centrality, which are the core elements of the second and third level of the agenda-setting theory, respectively. Also, findings indicate that absolute frequency, rather than the presence or absence of an attribute in a news story, is a more parsimonious measure of redundancy to identify compelling arguments in news stories. This study proposes methodological innovations that further expand the scope of attribute agenda setting in the big data landscape.