Human-algorithm interactions help explain the spread of misinformation.

Area of Science:

• Psychology
• Social Media Algorithms
• Information Ecosystems

Background:

• Human attention is biased towards moral and emotional information, both online and offline.
• Social media algorithms prioritize content that maximizes user engagement, often amplifying attention-grabbing material.
• This creates an online information ecosystem where emotionally charged and moralistic content is prevalent.

Purpose of the Study:

• To review evidence on the interaction between human attention biases and content algorithms.
• To propose a framework explaining how misinformation exploits these human-algorithm interactions.
• To suggest a dual-pronged approach for effective misinformation interventions.

Main Methods:

• Review of existing evidence on human attention biases.
• Analysis of how content curation algorithms function on social media platforms.
• Theoretical framework development integrating psychological and algorithmic factors.

Main Results:

• Human attention biases toward moral and emotional content are amplified by social media algorithms.
• Misinformation leverages these amplified biases to spread effectively within the online information ecosystem.
• Current interventions may be insufficient without addressing both user-level and system-level factors.

Conclusions:

• A comprehensive strategy is needed to combat misinformation, integrating person-centered and design-centered interventions.
• Future research should explore psychological aspects of misinformation sharing within human-algorithm interaction frameworks.
• Understanding this interplay is crucial for mitigating the negative impacts of misinformation online.