The Impact of Social Media Algorithms on "Social Learning"

Social media has become a new frontier for social learning, but the influence of algorithms controlled by tech companies is causing trouble, according to researchers William Brady, Joshua Conrad Jackson, Björn Lindström, and M.J. Crockett. In their recent paper, they highlight the perils of social learning in the digital age and argue that platform algorithms interfere with the strategies people typically use for social learning. This interference leads to misperceptions, the spread of misinformation, and the amplification of extreme views. However, Brady suggests that adjustments to algorithms can mitigate these harms while still providing engaging content for users. In social learning, humans rely on PRIME information, which stands for prestigious, in-group, moral, and emotional. These types of information help us determine what is important and shape our understanding of norms. However, online environments disrupt the usefulness of PRIME information due to algorithmic amplification. Social media algorithms are designed to maximize engagement, resulting in a flood of PRIME information that is neither rare nor diagnostic. This leads to the distortion of social learning, as prestigious sources may be faked, in-group information fosters groupthink and extremism, and extreme views are amplified and perceived as more common than they actually are. The consequences of this breakdown in PRIME information can be seen in real-world events, such as the January 6 insurrection. Fringe views gain legitimacy and critical mass through algorithmic amplification, allowing people to organize around them. Moreover, social media platforms contribute to the perception of increased polarization by exposing users to extreme posts from their political out-group. The negative, moralized, and emotional commentary accompanying these posts further skews users' understanding of the other side. To address this issue, Brady and his coauthors propose two alternative solutions. One is to increase the transparency of social media algorithms, providing users with information about why they see certain posts. The other solution is "bounded diversification," which involves tweaking algorithms to limit the amount of PRIME information users are exposed to. By penalizing PRIME content and prioritizing non-PRIME content that still engages users, social media platforms can offer a more diverse range of views without promoting extreme content. Implementing these changes would require a shift in the algorithms used by social media companies. However, Brady believes that platforms can find a balance between keeping users engaged and reducing the dominance of PRIME information. By doing so, social media can become a better tool for social learning, fostering a more informed and nuanced understanding of the world.

Hot Take: Social Media Algorithms and the US Business Market

The influence of social media algorithms on "social learning" has significant implications for the US business market and newly formed companies. As researchers Brady, Jackson, Lindström, and Crockett argue, these algorithms interfere with traditional social learning strategies, leading to misperceptions and the spread of misinformation. This can have a profound impact on businesses, particularly startups, who rely on accurate information and public perception to establish their brand and build customer trust. Moreover, the amplification of extreme views by social media algorithms can create a polarized business environment, making it difficult for companies to navigate consumer expectations and societal norms. This polarization can also lead to reputational risks if businesses are associated with these extreme views, either directly or indirectly. However, the proposed solutions, including increased transparency and "bounded diversification," offer hope for businesses. By understanding why certain posts appear on their feeds, businesses can better navigate the social media landscape and mitigate the risks associated with misinformation and polarization. Furthermore, by limiting the amount of PRIME information users are exposed to, social media platforms can foster a more diverse and balanced online environment. This can help businesses reach a broader audience and promote a more nuanced understanding of their brand. In conclusion, while the impact of social media algorithms on social learning presents challenges for the US business market, it also offers opportunities for growth and innovation. By understanding these impacts and adapting accordingly, businesses can leverage social media as a powerful tool for success.
Original Story By: Kellogg School of Management at Northwestern University
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