Social Drivers and Algorithmic Mechanisms on Digital Media.

Area of Science:

• Digital Media Studies
• Sociology of Technology
• Human-Computer Interaction

Background:

• Ubiquitous algorithms on digital platforms raise concerns for individual and collective well-being.
• Algorithmic feedback loops with social drivers complicate research into their true impact.
• The rapid, unregulated adoption of these technologies necessitates critical examination.

Purpose of the Study:

• To review current evidence on algorithmic effects on well-being, misinformation, and polarization.
• To propose research questions for improving digital platform algorithms.
• To explore the risks and opportunities of algorithmically curated media.

Main Methods:

• Literature review of existing evidence on algorithms and societal impacts.
• Analysis of the interplay between algorithmic mechanisms and social drivers.
• Conceptual framework for understanding algorithmic influence within broader societal contexts.

Main Results:

• Algorithmic roles in well-being, misinformation, and polarization are complex and not fully understood.
• Evidence indicates algorithms primarily reinforce pre-existing social drivers.
• Societal factors like individualism and populism are intertwined with algorithmic effects.

Conclusions:

• Further empirical research is crucial to disentangle algorithmic influence from social phenomena.
• Algorithms should be considered within their larger societal context.
• Designing algorithms for societal flourishing, not just profit, presents both risks and opportunities.