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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (7): 44-51    DOI: 10.11925/infotech.2096-3467.2017.0479
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Detecting Online Rumors with Sentiment Analysis
Shou Huanrong, Deng Shuqing, Xu Jian()
School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
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Abstract  

[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.

Key wordsSentiment Analysis      Sentiment Lexicon      Rumor Identification      Rumor Detection     
Received: 27 May 2017      Published: 12 July 2017
ZTFLH:  G350  

Cite this article:

Shou Huanrong,Deng Shuqing,Xu Jian. Detecting Online Rumors with Sentiment Analysis. Data Analysis and Knowledge Discovery, 2017, 1(7): 44-51.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0479     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I7/44

实际是谣言 实际不是谣言
预测是谣言 15 0
预测不是谣言 6 8
实际是谣言 实际不是谣言
预测是谣言 A B
预测不是谣言 C D
指标
Pr 100%
Rr 71.42%
F1r 83.34%
Pn 57.14%
Rn 100%
F1n 72.73%
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