Multiperspectivity in online news

An analysis of how reading behaviour is affected by viewpoint diverse news recommendations and how they are presented

Master thesis (2020)

Authors

M. Mulder Electrical Engineering, Mathematics and Computer Science

Contributors

J.A. Pouwelse Data-Intensive Systems - EEMCS (graduation committee member)
C. Lofi Web Information Systems - EEMCS (graduation committee member)
N. Tintarev Web Information Systems - EEMCS (graduation committee member)
O. Inel Web Information Systems - EEMCS (mentor)
J.E.G. Oosterman Blendle Research (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Mats Mulder
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Published Date

02-09-2020

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Previous research on diversity in recommender systems define diversity as the opposite of similarity and propose methods that are based on topic diversity. Diversity in news media, however, is understood as multiperspectivity and scholars generally agree that fostering diversity is the key responsibility of the press in a democratic society. Therefore, a novel viewpoint diversification method was developed, based on the reranking of recommendation lists within the topic using framing aspects. Among other results, an offline evaluation indicated that the proposed method is capable of enhancing the viewpoint diversity of recommendation lists according to a metric from literature. However, to truly enable multiperspectivity in automatic online news environments, users should also be willing to consume viewpoint diverse news recommendation. Therefore, an online study was conducted, assessing how viewpoint diverse recommendations and their presentation characteristics affect the reading behaviour of Blendle users. During a two-week experiment, two groups of 1038 users were presented a set of three recommendations below the content of two articles every day. Thereby, one group received recommendations based on relevance to the original article, while the other group received viewpoint diverse recommendations. Three implicit and one explicit measure of the reading behaviour were analysed. Additionally, the influence of the presentation characteristics of the recommendation on the reading behaviour was analysed. Generally, no major differences were found in the reading behaviour of both user groups. Only the results of the click-through rate calculated per recommendation set indicated a significant difference of 6.5% to the advantage of the baseline users. For the other measures of the reading behaviour, no significant differences were found between the baseline and diverse users. However, the results do show that multiple presentation characteristics have a significant influence on the reading behaviour. Therefore, these results suggest that future research on how recommendation can be presented is just as important as novel viewpoint diversification methods to truly achieve multiperspectivity in automated online news environments.