AI Can Enhance Creativity in Social Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909424759603200 |
|---|---|
| author | Baten, Raiyan Abdul Bangash, Ali Sarosh Veera, Krish Ghoshal, Gourab Hoque, Ehsan |
| author_facet | Baten, Raiyan Abdul Bangash, Ali Sarosh Veera, Krish Ghoshal, Gourab Hoque, Ehsan |
| contents | Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environments). We trained a model that predicts people's ideation performances using semantic and network-structural features in an online platform. Using this model, we built SocialMuse, which maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperforming AI-agnostic control networks in several creativity measures. The treatment networks were more decentralized than the control, as SocialMuse increasingly emphasized network-structural features at large network sizes. This decentralization spreads people's inspiration sources, helping inspired ideas stand out better. Our study provides actionable insights into building intelligent systems for elevating creativity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15264 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI Can Enhance Creativity in Social Networks Baten, Raiyan Abdul Bangash, Ali Sarosh Veera, Krish Ghoshal, Gourab Hoque, Ehsan Artificial Intelligence Human-Computer Interaction Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environments). We trained a model that predicts people's ideation performances using semantic and network-structural features in an online platform. Using this model, we built SocialMuse, which maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperforming AI-agnostic control networks in several creativity measures. The treatment networks were more decentralized than the control, as SocialMuse increasingly emphasized network-structural features at large network sizes. This decentralization spreads people's inspiration sources, helping inspired ideas stand out better. Our study provides actionable insights into building intelligent systems for elevating creativity. |
| title | AI Can Enhance Creativity in Social Networks |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.15264 |