Japanese "Abe" Tweets from 2019-02-10 to 2020-10-07 (15,407,811 tweets and 114,231,250 retweets)

Abstract (our paper)

To examine conservative–liberal differences in the extent to which partisan tweets reach less partisan moderate users in a nonwestern context, we analyzed a network of retweets about former Japanese Prime Minister Shinzo Abe. The analyses consistently demonstrated that partisan tweets originating from the conservative cluster reach a wider range of moderate users than those from the liberal cluster. Network analyses revealed that while the conservative and the liberal clusters’ internal structures were similar, the conservative cluster reciprocated the follows from moderate accounts at a higher rate than the liberal cluster. In addition, moderate accounts reciprocated the conservative cluster’s following at a higher rate than they did for the liberal cluster. The analysis of tweet content showed no difference in the frequency of hashtag use between conservatives and liberals, but there were differences in the use of emotion words and linguistic expressions. In particular, emotion words related to the propagation of messages, such as those expressing “dislike”, were used more frequently by conservatives, while the use of adjectives by conservatives was closer to that of moderate users, indicating that conservative tweets are more palatable for moderate users than liberal tweets.

Data

Abe.tsv.gz:
The first column is the tweet id, the second column is the tweet id of the retweet source, and the third column is the date and time (JST) when the tweet was posted.
This data was collected by giving the query "安倍 OR アベ" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets. The second column is empty if the tweet is not a retweet.

Publication

This data set was created for our study. If you make use of this data set, please cite:
Mitsuo Yoshida, Takeshi Sakaki, Tetsuro Kobayashi, Fujio Toriumi. Japanese conservative messages propagate to moderate users better than their liberal counterparts on Twitter. Scientific Reports. vol.11, article no.19224, 2021.
https://doi.org/10.1038/s41598-021-98349-2