Multiple Time-Series Data Analysis for Rumor Detection on Social Media
| Autor: | Xishuang Dong, Lijun Qian, Chandra M. M. Kotteti |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Majority rule
business.industry Computer science Gaussian 02 engineering and technology Rumor Machine learning computer.software_genre symbols.namesake Naive Bayes classifier 020204 information systems 0202 electrical engineering electronic engineering information engineering symbols Multiple time 020201 artificial intelligence & image processing Social media Artificial intelligence business computer |
| Zdroj: | IEEE BigData |
| DOI: | 10.1109/bigdata.2018.8622631 |
| Popis: | Rumor detection becomes increasingly important in social media. The effects of rumor propagation are dreadful in case of time-critical events, for example, during natural disasters. In this paper, we proposed a multiple time-series data analysis model to detect rumors on Twitter. Instead of checking the contents of the tweets, the proposed method only uses temporal properties of the tweets. As a result, the computational complexity measured by the training time and prediction time has been reduced significantly, which allows quick detection of rumors. Experimental results show that the proposed model combined with Gaussian Naive Bayes classifier achieved a high precision score of 94%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|