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Conference Papers Year : 2021

Recognition of Epidemic Cases in Social Web texts

Abstract

Since December 2019, Covid-19 has been spreading rapidly across the world. Unsurprisingly, conversation in social networks about Covid-19 is increasing as well. The aim of this study is to identify tentative Covid-19 infection cases through social networks and, specifically, on Twitter, using machine learning techniques. Tweets were collected using the data set “Covid-19 Twitter”, between November 1, 2020 and December 30, 2020, and manually marked by the authors of this study as positive (describing a tentative Covid-19 infection case) or negative (pertaining to any other Covid-19 related issue) cases of Covid-19, creating a smaller but more focused dataset. This study was conducted in three phases: a. data collection and data cleaning, b. processing and analysis of tweets by machine learning techniques, and c. evaluation and qualitative/quantitative analysis of the achieved results. The implementation was based on Gradient Boosting Decision Trees, Support Vector Machines (SVM) and Deep Learning algorithms.
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hal-03789023 , version 1 (27-09-2022)

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Eleftherios Alexiou, Apostolos Antonakakis, Nemanja Jevtic, Georgios Sideras, Eftichia Farmaki, et al.. Recognition of Epidemic Cases in Social Web texts. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.446-454, ⟨10.1007/978-3-030-79157-5_36⟩. ⟨hal-03789023⟩
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