Social media feedback and extreme opinion expression

Area of Science:

• Social Media Studies
• Computational Social Science
• Behavioral Economics

Background:

• Social media platforms provide asymmetric feedback, with positive reactions easily visible but negative ones often hidden.
• This design feature may influence the type and extremity of opinions expressed by content creators.

Purpose of the Study:

• To investigate how the asymmetry of feedback on social media platforms impacts the expression of extreme opinions.
• To compare the effects of visible versus hidden negative reaction counts on opinion expression.

Main Methods:

• Utilized simulations of a learning model to compare two distinct feedback environments.
• Conducted analyses of Twitter data and multiple online experiments to validate model assumptions and predictions.

Main Results:

• Expressed opinions tend to be more extreme in environments where negative reaction counts are not visible compared to environments where they are.
• Empirical data from Twitter and online experiments support the model's predictions.

Conclusions:

• The availability of negative reaction counts on social media influences the extremity of expressed opinions.
• A potential design modification, such as revealing negative feedback, could mitigate the expression of extreme opinions under specific circumstances.