[Objective] This paper aims to identify rumors automatically with the help of sentiment analysis. [Methods] First, we chose high-quality and low-quality information sources. Then, we calculated the sentiment value and difference between the information from different sources. Based on the assumption that the information from high-quality source was more reliable, information from low-quality channels could be listed as rumor if the sentiment difference between them exceeded the pre-set threshold. [Results] We applied the proposed method to information on food and health as well as health and medical issues, and then successfully identified twenty-three rumors from thirty suspected cases. The accuracy rate of rumor detection was 76.67%, the F-value was 83.34%, the recall and precision was 71.42% and 100%, respectively. For non-rumor message, the F-value, recall, and precision were 72.73%, 100% and 57.14%. [Limitations] We did not extract the data automatically from different sources and the sample size was relatively small. [Conclusions] Sentiment analysis could help us identify rumors effectively.
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