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AI analyzed 630K paintings to decode 600 years of economic history through emotion extraction
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A team of economists has used artificial intelligence to analyze over 630,000 European paintings spanning 600 years, discovering that collective shifts in artistic mood often aligned with historical moments of prosperity, hardship, or upheaval. The research demonstrates how AI can extract emotional signals from art to enhance traditional economic data, particularly for periods where standard historical records are scarce.

How it works: The researchers trained AI to detect nine emotions—including sadness, fear, anger, awe, contentment, and amusement—across paintings from 1400 to 2000.

  • The dataset was sourced from Google Arts and Culture, WikiData, and WikiArt, featuring predominantly traditional, figurative European painting across all genres.
  • Human annotators from different regions and backgrounds provided emotional labels to train the AI, with 15 to 25 people rating each painting to create a “wisdom-of-the-crowd effect.”
  • The model’s accuracy was measured against these human-provided emotional labels, though researchers acknowledged potential biases in the annotation process.

Key findings: Emotional patterns in paintings correlated strongly with historical events and economic conditions across 12 countries.

  • Fear increased during times of social and economic turbulence, while contentment dominated during periods of stability.
  • Hardship correlated with higher incidences of sadness, but amusement and excitement returned when living standards improved.
  • These emotional shifts were more pronounced in paintings depicting subjects from lower socio-economic backgrounds, who were more vulnerable to broader changes.

The big picture: The study reveals how technological innovations affected society through artistic expression, with different technologies producing distinct emotional responses.

  • As U.S. steamship cargo increased during the 1830s, paintings showed increased expressions of awe, while disagreement declined following steamship adoption.
  • Radio adoption from 1900 to 1950 corresponded with increased depictions of extreme emotions like anger, disgust, and excitement at the expense of contentment.
  • “That being said, the effect of the radio could be partly driven by its usage in some countries, like France and Germany, prior to World War II,” noted Yanos Zylberberg, an economics professor at Bristol University and co-author.

What makes this significant: The research bridges art history and economics by treating paintings as data sources that capture societal moods and structural shifts.

  • “Paintings capture dimensions of historical experience typically absent from standard data sources,” the researchers noted in their National Bureau of Economic Research paper.
  • The method offers a “higher-frequency view of living standards and cultural sentiment across European societies” compared to traditional economic indicators.
  • Artists working in the same time and place, like 19th-century French painters Gustave Courbet and William-Adolphe Bouguereau, provide different emotional perspectives on the same historical moment.

Limitations and future potential: The current study focuses exclusively on Western European figurative art, leaving out marginalized artistic production and avant-garde movements.

  • Researchers acknowledged the dataset skews heavily toward Western artists who have been collected, curated, and canonized.
  • As more museums digitize their collections, future studies could expand to cover more movements, regions outside Europe, and lesser-known artists.
  • The approach could eventually account for variations based on artists’ social class, gender, religion, or ethnicity to better understand economic inequality dynamics.
How Economists Are Mining Art With A.I. to Track Major Social Shifts

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