Average is boring: how similarity kills a meme's success

Area of Science:

• Information Science
• Social Computing
• Computational Social Science

Background:

• Memes compete for limited attention resources within social networks.
• Previous research focused on network structure and attention limits for meme success.
• The intrinsic characteristics of memes influencing their success remain underexplored.

Purpose of the Study:

• To investigate the role of meme similarity in determining meme popularity and persistence.
• To propose and validate a novel explanation for meme success based on uniqueness.
• To develop a predictive measure for meme success using similarity.

Main Methods:

• Developed a meme similarity measure utilizing semantic text analysis.
• Incorporated computer vision techniques to assess visual meme characteristics.
• Analyzed the relationship between meme similarity scores and meme success metrics.

Main Results:

• Higher similarity between memes significantly disadvantages their potential popularity.
• Successful memes are found to be outliers in the meme similarity space.
• The proposed meme similarity measure demonstrates predictive power for meme success.

Conclusions:

• Meme uniqueness is a critical factor for success, contrary to assumptions of competition for attention.
• Similarity acts as a disadvantage, suggesting novelty drives meme propagation.
• The developed similarity measure offers a promising tool for predicting meme virality.