# Social influence leads to the formation of diverse local trends

arXiv: https://arxiv.org/abs/2108.07437 · HTML: https://mlsanity.com/paper/2108.07437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 68 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [GANalyze: Toward Visual Definitions of Cognitive Image Properties](https://mlsanity.com/paper/1906.10112/) (2019)
- [VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository](https://mlsanity.com/paper/1905.04616/) (2019)
- [Me, My Echo Chamber, and I: Introspection on Social Media Polarization](https://mlsanity.com/paper/1803.01731/) (2018)
- [Predictability of Popularity: Gaps between Prediction and Understanding](https://mlsanity.com/paper/1603.09436/) (2016)
- [The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify](https://mlsanity.com/paper/2003.08203/) (2020)
- [Interpolating GANs to Scaffold Autotelic Creativity](https://mlsanity.com/paper/2007.11119/) (2020)
- [Random Voting Effects in Social-Digital Spaces: A case study of Reddit Post Submissions](https://mlsanity.com/paper/1506.01977/) (2015)
- [Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds](https://mlsanity.com/paper/1604.01105/) (2016)

## Built on by (most cited first, 1 of 1)

- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)

## Abstract (arXiv)

How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase both the inequality and unpredictability of its success, but has only been shown in the context of music listening. To further examine the effect of social influence on media popularity, we extend this research to the context of algorithmically-generated images by re-adapting Salganik et al's Music Lab experiment. On a digital platform where participants discover and curate AI-generated hybrid animals, we randomly assign both the knowledge of other participants' behavior and the visual presentation of the information. We successfully replicate the Music Lab's findings in the context of images, whereby social influence leads to an unpredictable winner-take-all market. However, we also find that social influence can lead to the emergence of local cultural trends that diverge from the status quo and are ultimately more diverse. We discuss the implications of these results for platform designers and animal conservation efforts.