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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li, Yingle Wu, Zhen Yu, Hongtao Liu, Lixiong |
| Copyright Year | 2013 |
| Description | Author affiliation: National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450002, Henan, China (Li, Yingle; Yu, Hongtao; Liu, Lixiong) || National Computer Network and Information Security Administration Center, Beijing 100031, China (Wu, Zhen) |
| Abstract | Twitter is emerging social media. There is an important practical significance to study its communication effect in many aspects of the Marketing Management, Public Sentiment, Hot Extraction and so on. The retweeting scale is an important indicator to reflect the communication effect. Based on analyzing the factors, a prediction algorithm based on SVM was proposed for retweeting with five features: publisher influence, acceptor activity, interest level, content importance and intimacy. Furthermore, the prediction algorithm for retweeting scale was proposed based above, the method to evaluate the prediction accuracy was given. At last the experiment with Twitter data showed a good result that the prediction accuracy of retweeting scale was up to 86.63%. |
| Starting Page | 541 |
| Ending Page | 545 |
| File Size | 825337 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467351379 |
| e-ISBN | 9781467327640 |
| DOI | 10.1109/ICIST.2013.6747607 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-03-23 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Accuracy Media Prediction algorithms Twitter Feature extraction Classification algorithms |
| Content Type | Text |
| Resource Type | Article |
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