Epidemic zone of COVID-19 from social media using hypergraph with weighting factor (HWF).
| Autor: | Pradeepa S; School of Computing, Sastra Deemed University, Thanjavur, 613401 India., Manjula KR; School of Computing, Sastra Deemed University, Thanjavur, 613401 India. |
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| Jazyk: | angličtina |
| Zdroj: | The Journal of supercomputing [J Supercomput] 2021; Vol. 77 (10), pp. 11738-11755. Date of Electronic Publication: 2021 Mar 29. |
| DOI: | 10.1007/s11227-021-03726-3 |
| Abstrakt: | Online social network is one of the most prominent media that holds information about society's epidemic problem. Due to privacy reasons, most of the users will not disclose their location. Detecting the location of the tweet users is required to track the geographic location of the spreading diseases. This work aims to detect the spreading location of the COVID-19 disease from the Twitter users and content discussed in the tweet. COVID-19 is a disease caused by the "novel coronavirus." About 80% of confirmed cases recover from the disease. However, one out of every six people who get COVID-19 can become seriously ill, stated by the World health organization. Inferring the user location for identifying the spreading location for the disease is a very challenging task. This paper proposes a new technique based on a hypergraph model to detect the Twitter user's locations based on the spreading disease. This model uses hypergraph with weighting factor technique to infer the spreading disease's spatial location. The accuracy of prediction can be improved when a massive volume of streaming data is analyzed. The Helly property of the hypergraph was applied to discard less potential words from the text analysis, which claims this work of unique nature. A weighting factor was introduced to calculate the score of each location for a particular user. The location of each user is predicted based on the one that possesses the highest weighting factor. The proposed framework has been evaluated and tested for various measures like precision, recall and F-measure. The promising results obtained have substantiated the claim for this work compared to the state-of-the-art methodologies. (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.) |
| Databáze: | MEDLINE |
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