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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Komori, M. Matsumura, N. Miura, A. Nagaoka, C. |
| Copyright Year | 2012 |
| Abstract | Twitter messages are real-time, spontaneous reports of what the users are feeling, thinking, and doing. The frequency of posting gTweetsh oscillates periodically in one-day and seven-day cycles. These periodic patterns may be related to the individual users' baseline affective state. In order to investigate individual periodic behavior in social media, we performed a Fourier series expansion and PCA on intra-week Tweet-frequency changes of 11,570 individuals. Moreover, the relationships between the users' baseline mood and the principal component scores were investigated. High frequency in daytime tweets on weekdays was found to be linked to a low positive affective state. The larger number of posting tweets was related to the negative affective state. |
| Starting Page | 405 |
| Ending Page | 410 |
| File Size | 506036 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467321204 |
| DOI | 10.1109/SNPD.2012.39 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-08-08 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Mood tweet patterns Rhythm Twitter Harmonic analysis Time frequency analysis Fourier series affect measurement Principal component analysis |
| Content Type | Text |
| Resource Type | Article |
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