2018/06/11 by Martin Hilbert, Saifuddin Ahmed, Jaeho Cho +2 · 3 citations
Physics and Astronomy · Social Sciences · #Opinion Dynamics and Social Influence #Misinformation and Its Impacts #Complex Network Analysis Techniques
paper · doi:10.1080/19312458.2018.1479843
openalex publication_date 2018/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.