"Why tag me?": Detecting motivations of comment tagging in Instagram. (15th September 2022)

• Record Type: Journal Article
• Authors: Kang, Jiwon; Yoon, Jeewoo; Park, Eunil; Han, Jinyoung
• Is Part Of: Expert systems with applications. Volume 202(2022)
• Publication Date: 2022-09-15
• Subjects: Instagram -- Comment tagging -- User tagging -- Comment -- Online conversation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117171
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We collected a large-scale comment data with user tagging data in Instagram. We analyzed the comments with tagging, compared to those without user tagging. The tagging motivation includes information-, relationship-, and discussion-oriented motivation. We proposed and evaluated several user tagging classifiers with social media features. Abstract: Tagging a friend in a comment is one of the main mechanisms to lead user interaction in social media. This paper investigates the current practice of user tagging in Instagram by collecting large-scale data that includes 9K uploaded posts and associated 4M comments shared by 3M users. Our analysis reveals that 54.8% of the comment contains user tagging, meaning that user tagging is widely used in Instagram. By analyzing the comment texts, we observe that the comments with user tagging tend to have more social and fewer negative words than those without user tagging, suggesting that user tagging is often used for friendly conversations. Based on lessons learned, we propose a learning-based model to classify the motivation of user tagging into one of the following categories: information-, relationship-, and discussion-oriented motivation. The proposed model can achieve a high f1-score of 83.72% in identifying the motivations for user tagging, which can provide considerable insights into user responses. We then apply our classification model to the user tagging comments in our dataset, and find that 44.08%, 47.74%, and 8.18% of comments are information-, relationship-, and discussion-oriented comments, respectively, which reveals that user tagging is frequently used to socialize with other friends.