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Trespassing the gates of research: identifying algorithmic mechanisms that can cause distortions and biases in academic social media

Luciana Monteiro-Krebs (Graduate Program in Communication and Information (PPGCOM), UFRGS, Porto Alegre, Brazil) (Meaningful Interactions Lab (Mintlab), Faculty of Social Sciences, KU Leuven, Leuven, Belgium)
Bieke Zaman (Meaningful Interactions Lab (Mintlab), Faculty of Social Sciences, KU Leuven, Leuven, Belgium)
Sonia Elisa Caregnato (Graduate Program in Communication and Information (PPGCOM), UFRGS, Porto Alegre, Brazil)
David Geerts (Meaningful Interactions Lab (Mintlab), Faculty of Social Sciences, KU Leuven, Leuven, Belgium)
Vicente Grassi-Filho (Faculdade de Informática, PUCRS, Porto Alegre, Brazil)
Nyi-Nyi Htun (Human-Computer Interaction (HCI), Department of Computer Science, KU Leuven, Leuven, Belgium)

Online Information Review

ISSN: 1468-4527

Article publication date: 21 December 2021

Issue publication date: 16 August 2022

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Abstract

Purpose

The use of recommender systems is increasing on academic social media (ASM). However, distinguishing the elements that may be influenced and/or exert influence over content that is read and disseminated by researchers is difficult due to the opacity of the algorithms that filter information on ASM. In this article, the purpose of this paper is to investigate how algorithmic mediation through recommender systems in ResearchGate may uphold biases in scholarly communication.

Design/methodology/approach

The authors used a multi-method walkthrough approach including a patent analysis, an interface analysis and an inspection of the web page code.

Findings

The findings reveal how audience influences on the recommendations and demonstrate in practice the mutual shaping of the different elements interplaying within the platform (artefact, practices and arrangements). The authors show evidence of the mechanisms of selection, prioritization, datafication and profiling. The authors also substantiate how the algorithm reinforces the reputation of eminent researchers (a phenomenon called the Matthew effect). As part of defining a future agenda, we discuss the need for serendipity and algorithmic transparency.

Research limitations/implications

Algorithms change constantly and are protected by commercial secrecy. Hence, this study was limited to the information that was accessible within a particular period. At the time of publication, the platform, its logic and its effects on the interface may have changed. Future studies might investigate other ASM using the same approach to distinguish potential patterns among platforms.

Originality/value

Contributes to reflect on algorithmic mediation and biases in scholarly communication potentially afforded by recommender algorithms. To the best of our knowledge, this is the first empirical study on automated mediation and biases in ASM.

Keywords

Acknowledgements

The authors thank Garima Singh for generously sharing knowledge that facilitated the web code inspection.

Funding: This research is funded by Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) - Financing Code 001.

Citation

Monteiro-Krebs, L., Zaman, B., Caregnato, S.E., Geerts, D., Grassi-Filho, V. and Htun, N.-N. (2022), "Trespassing the gates of research: identifying algorithmic mechanisms that can cause distortions and biases in academic social media", Online Information Review, Vol. 46 No. 5, pp. 993-1013. https://doi.org/10.1108/OIR-01-2021-0042

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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