The concept of network effects is easy to describe: the more people use a service, the more valuable it becomes (Banton, 2024). This is why they play a significant role in the growth of products and services. What most people don’t realize is that these algorithms play a dominant role in determining what people get to see on their screens. Content doesn’t ‘just appear’ on screens.
Algorithms are designed to push popular or highly engaging content onto more feeds and ensure visibility. While this mechanism helps platforms grow, it also carries a darker side. The danger lies in the fact that algorithms don’t distinguish between positive and negative content. Engagement-driven algorithms prioritize strong emotional reaction above objective news articles. This means that fake news or misinformation spreads faster than accurate information (Vosoughi et al.,2018). As awareness of misinformation grows, public trust in platforms declines. Instead of reinforcing network effects, this process creates negative network effects, where the products or services become less valuable as more people use it. Feed algorithms classify user preferences by collecting behavioral data and matches users with precise and continuous information. This creates powerful driving forces for group polarization, which highly contributes to the formation of echo chambers. Thus, echo chambers are fueled by algorithms. Recent studies show that these echo chambers can promote the spread of misleading information, fake news and rumors (Gao et al.,). For example, Youtube’s recommendation system has been criticized for pushing viewers toward increasingly radical content. The algorithms seem to consistently recommend more extreme versions of things users are watching, just like if you start watching running videos, you end up with videos about ultramarathons.
Algorithms have a significant impact on the formation of negative network effects by prioritizing engagement-driven data over objective data, the risks of algorithms can cause a harmful loop that damages trust and spreads misinformation.
References
Banton, C. (2024, 22 augustus). What Is the Network Effect? Investopedia. https://www.investopedia.com/terms/n/network-effect.asp
Gao, Y., Liu, F., & Gao, L. (2023). Echo chamber effects on short video platforms. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-33370-1
Tufekci, Z. (2018, 10 maart). YouTube, the great radicalizer. The New York Times. https://www.nytimes.com/2018/03/10/opinion/sunday/youtube-politics-radical.html
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559
