Document Type
Article
Publication Date
2017
Journal
Proceedings of the 45th Annual Conference of The Canadian Association for Information Science/L’Association canadienne des sciences de l’information (CAIS/ACSI2017), Ryerson University, Toronto, May 31 - June 2, 2017.
Abstract
Clickbait is “content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page” (“clickbait,” n.d.). The term is also generally used to refer specifically to the attention-grabbing headlines. Critics of clickbait argue that clickbait is shallow, misleading, and ubiquitous – “a new word that has become synonymous with online journalism” (Frampton, 2015). It is the subject of a small, but growing number of studies in disciplines ranging from linguistics, communications, and information sciences. Palau-Sampio (2016) analyzed linguistic strategies associated with tabloid journalism in the Spanish digital newspaper Elpais.com, concluding that there is a trend towards lower quality news reporting. In their research on Danish news sites, Blom & Hansen (2015) identified forward-referencing, specifically the use of empty pronouns to create an information gap, as a feature of clickbait headlines. Chen, Conroy & Rubin (2015) proposed that automatic identification of clickbait could draw upon three types of features: a) lexico-semantic and pragmatic linguistic patterns (e.g. unresolved pronouns, affective and suspenseful language, action words, overuse of numerals, and reverse narratives), b) incongruent image placement with a possible emotional load, and c) user reading and commenting behavior. An effort in automated identification of clickbait by Potthast, et al. (2016) achieved 79% accuracy on Twitter tweets. But debate still rages over what the word actually means (Gardiner, 2015).
Citation of this paper:
Chen, Y. and Rubin V. L. (2017). Perceptions of Clickbait: A Q-Methodology Approach. In the Proceedings of the 45th Annual Conference of The Canadian Association for Information Science/L’Association canadienne des sciences de l’information (CAIS/ACSI2017), Ryerson University, Toronto, May 31 - June 2, 2017