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カワイ ユキコ
KAWAI YUKIKO
河合 由起子 所属 京都産業大学 情報理工学部 情報理工学科 職種 教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2018/09 |
| 形態種別 | 研究論文 |
| 査読 | 査読あり |
| 標題 | Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study |
| 執筆形態 | その他 |
| 掲載誌名 | JMIR Public Health Surveillance |
| 掲載区分 | 国外 |
| 巻・号・頁 | 4(3),pp.e65 |
| 著者・共著者 | Shoko Wakamiya,Yukiko Kawai,Eiji Aramaki |
| 概要 | BACKGROUND: The recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor-based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results. OBJECTIVE: This study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts. METHODS: We presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information. RESULTS: The estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points). CONCLUSIONS: The proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification. |
| DOI | 10.2196/publichealth.8627 |
| PMID | 30274968 |
| PermalinkURL | https://publichealth.jmir.org/2018/3/e65/ |