PREDICTING THE STRENGTH OF
ONLINE NEWS FRAMES

Hrvoje Jakopovi?

Faculty of Political Science, University of Zagreb
Zagreb, Croatia

INDECS 15(3), 209-221, 2017
DOI 10.7906/indecs.15.3.5
Full text available here.
 

Received: 9th July 2017.
Accepted: 23rd October 2017.
Regular article

ABSTRACT

Framing theory is one of the most significant approaches to understanding media and their potential impact on publics. Leaving aside that fact, the author finds that publicity effects seem to be dispersed and difficult to catch for public relations (PR). This paper employs a specific research design, which could be applied to public relations practice, namely with a view to observing correlations between specific media frames and individual frames. The approach is based on the Semetko and Valkenburg (2000) typology of news frames. The author attributes negative, positive and neutral determinants to the types of frames in his empirical research. Online news regarding three transport organizations and the accompanying user comments (identified as negative, positive and neutral) are analysed by means of the method of content and sentiment analysis. The author recognizes user comments and reviews as individual frames that take part in the creation of online image. Furthermore, he identifies the types of media frames as well as individual frames manifested as image, and undertakes correlation research in order to establish their prediction potential. The results expose the most frequently used types of media frames concerning the transport domain. The media are keen to report through the attribution of responsibility frame, and after that, through the economic frame and the conflict frame, but, on the other hand, they tend to neglect the human interest frame and the morality frame. The results show that specific types of news frames enable better prediction of user reactions. The economic frame and the human interest frame therefore represent the most predictable types of frame.

KEY WORDS

framing theory, user comments, online news, transport, sentiment analysis

CLASSIFICATION

JEL:L82


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