City Research Online

Selecting and tailoring of images for visual impact in online journalism

Frankowska-Takhari, S., MacFarlane, A., Goker, A. S. & Stumpf, S. (2017). Selecting and tailoring of images for visual impact in online journalism. Information Research, 22(1), article number 1619.


Introduction. Images have a strong presence in online journalism and are used to attract readers’ attention to news content. Images are predominantly sourced from online collections offering access to ready imagery. Yet, the literature shows that image retrieval systems fall short of meeting users’ needs. This study investigated image selection and use in online journalism, in order to propose improvements to the effectiveness of image retrieval.

Method. Twelve image professionals working in online journalism participated in semi-structured interviews. Eight professionals were observed while performing real illustration tasks in situ. Thematic analysis was used on data to identify common themes. Illustrations created in this study were included in the data, and a social visual semiotics approach was used for the interpretation of visual meaning.

Results. A trend in how images were selected and tailored for online news content emerged, and a set of recurring image features was identified in illustrations used for online news content.

Conclusions. The primary contribution of this study is the identification of recurring image features and linking them to visual impact. It is expected that when applied as search functionality, these features will improve the effectiveness of retrieval of visually engaging images as required in online journalism.

Publication Type: Article
Subjects: P Language and Literature > PN Literature (General)
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments: School of Science & Technology > Computer Science
SWORD Depositor:
[thumbnail of Selecting and tailoring of images for visual impact in online journalism.pdf]
Text - Published Version
Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0.

Download (5MB) | Preview


Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email


Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login