Using agent-based modelling to evaluate the impact of algorithmic curation on social media
File(s)
Accepted version
Author(s)
Gausen, Anna
Luk, Wayne
Guo, Ce
Type
Journal Article
Abstract
Social media networks have drastically changed how people communicate and seek information. Due to the scale of information
on these platforms, newsfeed curation algorithms have been developed to sort through this information and curate what users see.
However, these algorithms are opaque and it is difficult to understand their impact on human communication flows. Some papers have
criticised newsfeed curation algorithms that, while promoting user engagement, heighten online polarization, misinformation, and
the formation of echo chambers. Agent-based modelling offers the opportunity to simulate the complex interactions between these
algorithms, what users see, and the propagation of information on social media. This paper uses agent-based modelling to compare
the impact of four different newsfeed curation algorithms on the spread of misinformation and polarization. This research has the
following contributions. (1) Implementing newsfeed curation algorithm logic on an agent-based model. (2) Comparing the impact of
different curation algorithm objectives on misinformation and polarization. (3) Calibration and empirical validation using real Twitter
data. This research provides useful insights into the impact of curation algorithms on how information propagates and on content
diversity on social media. Moreover, we show how agent-based modelling can reveal specific properties of curation algorithms, which
can be used in improving such algorithms.
on these platforms, newsfeed curation algorithms have been developed to sort through this information and curate what users see.
However, these algorithms are opaque and it is difficult to understand their impact on human communication flows. Some papers have
criticised newsfeed curation algorithms that, while promoting user engagement, heighten online polarization, misinformation, and
the formation of echo chambers. Agent-based modelling offers the opportunity to simulate the complex interactions between these
algorithms, what users see, and the propagation of information on social media. This paper uses agent-based modelling to compare
the impact of four different newsfeed curation algorithms on the spread of misinformation and polarization. This research has the
following contributions. (1) Implementing newsfeed curation algorithm logic on an agent-based model. (2) Comparing the impact of
different curation algorithm objectives on misinformation and polarization. (3) Calibration and empirical validation using real Twitter
data. This research provides useful insights into the impact of curation algorithms on how information propagates and on content
diversity on social media. Moreover, we show how agent-based modelling can reveal specific properties of curation algorithms, which
can be used in improving such algorithms.
Date Issued
2023-03-01
Date Acceptance
2022-05-25
Citation
ACM Journal of Data and Information Quality, 2023, 15 (1), pp.1-24
ISSN
1936-1955
Publisher
Association for Computing Machinery (ACM)
Start Page
1
End Page
24
Journal / Book Title
ACM Journal of Data and Information Quality
Volume
15
Issue
1
Copyright Statement
©ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Journal of Data and Information Quality, http://dx.doi.org/10.1145/3546915
Publication Status
Published
Article Number
ARTN 2
Date Publish Online
2022-12-28