The metrics of keywords to understand the difference between Retweet and Like in each category

TL;DR

This study introduces new metrics based on word appearance and statistical tests to distinguish the different roles of retweets and Likes across categories on Twitter, revealing how content type influences user engagement.

Contribution

The paper proposes novel metrics using chi-square tests to analyze the differences between retweets and Likes by category, surpassing simple counts and TF-IDF in extracting meaningful insights.

Findings

Different RT and Like tendencies for tweets with news URLs.

Category-specific differences in RT and Like behaviors.

Temporal trends linking RT and Like content to current events.

Abstract

The purpose of this study is to clarify what kind of news is easily retweeted and what kind of news is easily Liked. We believe these actions, retweeting and Liking, have different meanings for users. Understanding this difference is important for understanding people's interest in Twitter. To analyze the difference between retweets (RT) and Likes on Twitter in detail, we focus on word appearances in news titles. First, we calculate basic statistics and confirm that tweets containing news URLs have different RT and Like tendencies compared to other tweets. Next, we compared RTs and Likes for each category and confirmed that the tendency of categories is different. Therefore, we propose metrics for clarifying the differences in each action for each category used in the $\chi$-square test in order to perform an analysis focusing on the topic. The proposed metrics are more useful than simple counts and TF-IDF for extracting meaningful words to understand the difference between RTs and Likes. We analyzed each category using the proposed metrics and quantitatively confirmed that the difference in the role of retweeting and Liking appeared in the content depending on the category. Moreover, by aggregating tweets chronologically, the results showed the trend of RT and Like as a list of words and clarified how the characteristic words of each week were related to current events for retweeting and Liking.