Abstract
With social media sites, such as Twitter, providing a visual record of the daily interests and concerns of users in the form of tweets and tweeting behaviors, there is growing demand among users, such as corporations, to identify other interested users. However, accurately determining whether users who receive information (such as tweets) from enterprise users have a genuine interest in it can be difficult. In this study, the user behavior of resending information received on Twitter (retweeting) is analyzed with the aim of developing a method for constructing a model for predicting retweeting behavior using the content of past tweeting history via evaluation indices of words and phrases in the users’ tweets. This paper analyzes the tweets sent by large online retail websites and by the followers who receive them, comparing the feature words obtained from the retweets with those in the tweets sent by the followers. This paper also discusses the feasibility of constructing a behavior prediction model by extracting temporal patterns of evaluation indices that are created from the usage frequencies of feature words and phrases obtained from followers’ tweets.
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Notes
- 1.
Hereafter, words and phrases are called ‘term’. Each term consists of one or more words.
- 2.
This IPA dictionary is a Japanese morpheme dictionary made by the project run by the Information-Technology Promoting Agency in Japan.
- 3.
Due to restrictions in the Twitter API, these users were the users who met the criteria from the randomly acquired 5,000 users.
- 4.
Considering more realistic situation, the gathered tweets are not re-retrieved in the prior period after listing the followers who tweeted the tweets containing the feature words and phrases listed in Table 3.
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Acknowledgement
This work was supported by JSPS KAKENHI Grant Numbers 24500175 and 26240036.
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Abe, H. (2015). Analyzing User Behaviors Based on Temporal Patterns of Sequential Pattern Evaluation Indices on Twitter. In: Li, XL., Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D. (eds) Trends and Applications in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science(), vol 9441. Springer, Cham. https://doi.org/10.1007/978-3-319-25660-3_15
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