Communicating with Algorithms: A Transfer Entropy Analysis of Emotions-based Escapes from Online Echo Chambers
Open accessAbstract
Online algorithms have received much blame for polarizing emotions during the 2016 U.S. presidential election. We use transfer entropy to measure directed information flows from human emotions to YouTube’s video recommendation engine, and back, from recommended videos to users’ emotions. We find that algorithmic recommendations communicate a statistically significant amount of positive and negative affect to humans. Joy is prevalent in emotional polarization, while sadness and fear play significant roles in emotional convergence. These findings can help to design more socially responsible algorithms by starting to focus on the emotional content of algorithmic recommendations. Employing a computational-experimental mixed method approach, the study serves as a demonstration of how the mathematical theory of communication can be used both to quantify human-machine communication, and to test hypotheses in the social sciences.
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Little is known about the social and political implications of search algorithms.
“Despite these increasing concerns and debates, little is known about the social and political implications of search algorithms (Hilbert et al., 2018;Thorson & Wells, 2015).”
From Do Search Algorithms Endanger Democracy? An Experimental Investigation of Algorithm Effects on Political Polarization (2020) · cites this paper as Hilbert et al., 2018
Initial network creation by recommendation systems can embed users in echo chambers depending on their initial interactions.
“Hilbert et al. [87] emphasized the influence of initial network creation by recommendation systems, which can embed users in echo chambers depending on their initial interactions.”
From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [87]
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- Authors
- Hilbert, M., Ahmed, S., Cho, J., Liu, B., & Luu, J.
- Affiliations
- University of California, Davis (US)
- Corresponding author
- Hilbert, Martin
- Venue
- Communication Methods and Measures
- Publisher
- Informa UK Limited
- Published
- 2018-06-11
- Volume
- 12
- Issue
- 4
- Pages
- 260-275
- ISSN
- 1931-2458 (ISSN-L)
- 1931-2466
- OpenAlex
- W2808704453
- Cited by
- 49
- References
- 57
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 3 · 2023: 7 · 2022: 12 · 2021: 7 · 2020: 5 · 2019: 5 · 2018: 1
- FWCI
- 10.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communication Methods and Measures (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- algorithmic recommendation, YouTube, echo chambers, emotional polarization, affective computing, transfer entropy, human-machine communication, 2016 US presidential election, positive and negative affect
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