Popularity Prediction for Single Tweet Based on Heterogeneous Bass Model

Predicting the popularity of a single tweet is useful for both users and enterprises. However, adopting existing topic or event prediction models cannot obtain satisfactory results. The reason is that one topic or event that consists of multiple tweets, has more features and characteristics than a s...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2021-05, Vol.33 (5), p.2165-2178
Main Authors: Gao, Xiaofeng, Zheng, Zuowu, Chu, Quanquan, Tang, Shaojie, Chen, Guihai, Deng, Qianni
Format: Article
Language:English
Subjects:
Blogs
Clustering
Context modeling
Data models
Heterogeneous bass model
Model accuracy
Performance enhancement
Popularity
Prediction models
Predictions
Predictive models
single tweet popularity
Social networking (online)
Social networks
time series prediction
Twitter social network
Video on demand
Web sites
Weight
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Summary:Predicting the popularity of a single tweet is useful for both users and enterprises. However, adopting existing topic or event prediction models cannot obtain satisfactory results. The reason is that one topic or event that consists of multiple tweets, has more features and characteristics than a single tweet. In this article, we propose two variations of Heterogeneous Bass models ( HBass ), originally developed in the field of marketing science, namely Spatial-Temporal Heterogeneous Bass Model ( ST-HBass ) and Feature-Driven Heterogeneous Bass Model ( FD-HBass ), to predict the popularity of a single tweet at the early stage and the stable stage. We further design an Interaction Enhancement to improve the performance, which considers the competition and cooperation from different tweets with the common topic. In addition, it is often difficult to depict popularity quantitatively. We design an experiment to get the weight of favorite, retweet and reply, and apply the linear regression to calculate the popularity. Furthermore, we design a clustering method to bound the popular threshold. Once the weight and popular threshold are determined, the status whether a tweet will be popular or not can be justified. Our model is validated by conducting experiments on real-world Twitter data, and the results show the efficiency and accuracy of our model, with less absolute percent error and the best Precision and F-score . In all, we introduce Bass model into social network single-tweet prediction to show it can achieve excellent performance.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2019.2952856