A Selfie is Worth a Thousand Words: Mining Personal Patterns behind User Selfie-posting Behaviours

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 these features, for a series of selfie related tasks, we build classifiers that can accurately predict two sets of users with opposite selfie-posting behaviours. We have found that people's interest, activity and posting habit have a great influence on their selfie-posting behaviours. For example, the classification accuracy between selfie-posting addict and nonaddict reaches 89.36%. We also prove that using user's image information to predict these behaviours achieve better performance than using text information. More importantly, for each set of users with a specific selfie-posting behaviour, we extract and visualize significant personal patterns about them. In addition, we cluster users and extract their high-level attributes, revealing the correlation between these attributes and users' selfie-posting behaviours. In the end, we demonstrate that users' selfie-posting behaviour, as a good predictor, could predict their different preferences toward these high-level attributes accurately.