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

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 χ-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.

[1]  Bernard J. Jansen,et al.  Stylistic Features Usage: Similarities and Differences Using Multiple Social Networks , 2019, SocInfo.

[2]  Mitsuo Yoshida,et al.  Analysis of User Dwell Time by Category in News Application , 2018, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[3]  Bernard J. Jansen,et al.  Predicting Audience Engagement Across Social Media Platforms in the News Domain , 2019, SocInfo.

[4]  Mohand Boughanem,et al.  A Priori Relevance Based On Quality and Diversity Of Social Signals , 2015, SIGIR.

[5]  Kazumi Saito,et al.  Network Analysis of Three Twitter Functions: Favorite, Follow and Mention , 2012, PKAW.

[6]  Richard Fletcher,et al.  The Impact of Trust in the News Media on Online News Consumption and Participation , 2017 .

[7]  Panayotis Antoniadis,et al.  Faving Reciprocity in Content Sharing Communities: A Comparative Analysis of Flickr and Twitter , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[8]  Ismail Badache,et al.  Exploring Differences in the Impact of Users’ Traces on Arabic and English Facebook Search , 2019, 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[9]  Damian Trilling,et al.  UvA-DARE (Digital Academic Repository) From newsworthiness to shareworthiness How to predict news sharing based on article characteristics , 2017 .

[10]  Max L. Wilson,et al.  More than Liking and Bookmarking? Towards Understanding Twitter Favouriting Behaviour , 2014, ICWSM.

[11]  M. Thelwall,et al.  Academic information on Twitter: A user survey , 2018, PloS one.

[12]  Qing Yang,et al.  Analyzing User Retweet Behavior on Twitter , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[13]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.