Ranking News Feed Updates on Social Media: A Review and Expertise-Aware Approach

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Author(s): Sami Belkacem (LSI Laboratory, University of Science and Technology Houari Boumediene, Algiers, Algeria)and Kamel Boukhalfa (LSI Laboratory, University of Science and Technology Houari Boumediene, Algiers, Algeria)
Copyright: 2021
Volume: 17
Issue: 1
Pages: 24
Source title: International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/IJDWM.2021010102

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Social media are used by hundreds of millions of users worldwide. On these platforms, any user can post and share updates with individuals from his social network. Due to the large amount of data, users are overwhelmed by updates displayed chronologically in their newsfeed. Moreover, most of them are irrelevant. Ranking newsfeed updates in order of relevance is proposed to help users quickly catch up with the relevant updates. In this work, the authors first study approaches proposed in this area according to four main criteria: features that may influence relevance, relevance prediction models, training and evaluation methods, and evaluation platforms. Then the authors propose an approach that leverages another type of feature which is the expertise of the update's author for the corresponding topics. Experimental results on Twitter highlight that judging expertise, which has not been considered in the academic and the industrial communities, is crucial for maximizing the relevance of updates in newsfeeds.

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Ranking News Feed Updates on Social Media: A Review and Expertise-Aware Approach

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