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Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption.

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PubMed
Summary
This summary is machine-generated.

Online content is shaped by algorithms and human choices. Understanding this complex relationship requires more data access for researchers to ensure safer, more beneficial algorithms for the public.

Keywords:
algorithmscognitionsocial cognitionsocial media

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Area of Science:

  • * Computational Social Science
  • * Human-Computer Interaction
  • * Digital Media Studies

Background:

  • * Online content consumption is largely mediated by proprietary algorithms from social media and search engines.
  • * The interaction between users and these algorithms is complex and bidirectional.
  • * Current research is limited by a lack of access to platform data.

Purpose of the Study:

  • * To explore the interplay between human agency and algorithmic curation.
  • * To analyze the continuum of human demand from implicit to explicit.
  • * To understand the long-term societal effects of human-algorithm entanglement.

Main Methods:

  • * Conceptual analysis of human-algorithm interaction.
  • * Examination of the feedback loops between user behavior and algorithmic modification.
  • * Discussion of research limitations and future needs.

Main Results:

  • * Human-algorithm interactions shape immediate user experiences.
  • * These interactions can alter social network structures, leading to long-term effects.
  • * The mutually shaping nature of these systems creates a complex entanglement.

Conclusions:

  • * Greater transparency and data sharing are crucial for understanding human-algorithm entanglement.
  • * Enhanced researcher access to platform data is needed.
  • * Improved understanding is essential for developing beneficial and low-risk algorithms.