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"Is a picture really worth a thousand words?": A case study on classifying user attributes on Instagram
Junho Song1, Kyungsik Han2, Dongwon Lee3
1Department of Computer Science and Engineering, Hanyang University, Seoul, Republic of Korea.
Plos One
|October 6, 2018
Summary
This study reveals that user age and gender significantly influence Instagram content choices. Image analysis accurately predicts user demographics, outperforming human judgment.
Area of Science:
- Social Media Analytics
- Computational Social Science
- Computer Vision
Background:
- Social media platforms like Instagram are integral to daily life.
- User-shared content often implicitly reflects personal characteristics.
- Understanding user demographics is crucial for platform analysis.
Purpose of the Study:
- To investigate the influence of age and gender on Instagram user engagement.
- To develop and evaluate models for classifying user age and gender based on Instagram content.
- To compare the effectiveness of image versus tag data for demographic characterization.
Main Methods:
- Analysis of user-shared images and associated tags on Instagram.
- Development of machine learning models for age and gender classification.
- Evaluation of model performance using F1 scores and comparison with human raters.
Main Results:
- Significant differences in Instagram content (topics, style) were observed based on age and gender.
- Developed age and gender classification models achieved high accuracy (up to 88% F1 score for age, 74% for gender).
- Image-based characterization proved more effective than tag-based characterization for user demographics.
- Models demonstrated superior performance compared to human raters on unseen data.
Conclusions:
- Age and gender are strong determinants of Instagram content and usage patterns.
- Image analysis is a powerful tool for inferring user demographics on social media.
- Future research should leverage visual data more extensively for richer user characterization.
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