Exploring the Relationship between User Activities and Profile Images on Twitter through Machine Learning Techniques
Social media profile images are one of many visual components of users.
Moreover, user activities such as posting or chatting are regarded as self-expression behaviors.
In this study, we examine Japanese Twitter users to explore the relationship between user activities and profile images.
Logistic regression analysis is used to statistically identify and quantify relationships, leading us to conclude that several profile image categories significantly correlate with user activities.
Furthermore, we use machine learning techniques (logistic regression, random forest, and support vector machine) to predict whether or not a user belongs to a specific profile image category.
Each model's performance is evaluated and compared for all profile image categories.
Primary results show that users whose profile image includes others' faces are more likely to use a replying function but less likely to add url links to their tweets, and that it is the easiest for machine learning models to find their category from their user activities.
In short, our findings indicate that visual expression correlates with social media user behavior.
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