TY - JOUR
T1 - “Why tag me?”
T2 - Detecting motivations of comment tagging in Instagram
AU - Kang, Jiwon
AU - Yoon, Jeewoo
AU - Park, Eunil
AU - Han, Jinyoung
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/9/15
Y1 - 2022/9/15
N2 - 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.
AB - 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.
KW - Comment
KW - Comment tagging
KW - Instagram
KW - Online conversation
KW - User tagging
UR - https://www.scopus.com/pages/publications/85129553255
U2 - 10.1016/j.eswa.2022.117171
DO - 10.1016/j.eswa.2022.117171
M3 - Review article
AN - SCOPUS:85129553255
SN - 0957-4174
VL - 202
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 117171
ER -