A Selfie is Worth a Thousand Words: Mining Personal Patterns behind User Selfie-posting Behaviours
Tianlang Chen, Yuxiao Chen, Jiebo Luo

TL;DR
This paper investigates personal patterns behind selfie-posting behaviors on WeChat, developing unsupervised classification and user feature extraction methods to predict and analyze user preferences and habits with high accuracy.
Contribution
It introduces an unsupervised approach to classify selfies and constructs user-level features to predict and interpret selfie-posting behaviors, revealing deep personal patterns.
Findings
Classification accuracy for selfie addict vs. non-addict reaches 89.36%
Image-based features outperform text-based features in behavior prediction
User patterns correlate strongly with high-level attribute preferences
Abstract
Selfies have become increasingly fashionable in the social media era. People are willing to share their selfies in various social media platforms such as Facebook, Instagram and Flicker. The popularity of selfie have caught researchers' attention, especially psychologists. In computer vision and machine learning areas, little attention has been paid to this phenomenon as a valuable data source. In this paper, we focus on exploring the deeper personal patterns behind people's different kinds of selfie-posting behaviours. We develop this work based on a dataset of WeChat, one of the most extensively used instant messaging platform in China. In particular, we first propose an unsupervised approach to classify the images posted by users. Based on the classification result, we construct three types of user-level features that reflect user preference, activity and posting habit. Based on…
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Taxonomy
TopicsMisinformation and Its Impacts · Complex Network Analysis Techniques · Sentiment Analysis and Opinion Mining
