Textual Analysis of Twitter Posts in the First Months of The Covid-19 Pandemic.

Year 2022, , 136 - 148, 31.10.2022

Başak Eliaçık

https://doi.org/10.55517/mrr.1141436

Cited By: 1

Abstract

Aim: This study aimed to analyze the content of tweets about the coronavirus to improve our understanding of public emotions and thoughts during the pandemic. Methods: Our study was conducted between 11th March 2020 to 5th May 2020. Using a Java-based software application, the data was extracted from Twitter on pre-defined English and Turkish keywords. The data collected was examined and divided according to 5 pre-established categories (measures to be taken for COVID-19, COVID-19 symptoms, COVID-19 current, and future treatments, conspiracy theories about the COVID-19 pandemic, and economic consequences of the COVID-19 outbreak) using a word-based Levenshtein distance algorithm with focus on the treatment and measures category. Results: 87.264.342 tweets were analyzed with machine learning techniques and algorithms. After excluding retweets and advertisements a total of 5,529,891 tweets related to coronavirus were included in the study. Of the selected data, 32.3% (n = 1786000) was categorized as measures to be taken for COVID-19 and 15.7% (n = 867403) as sentiments about current and future treatments. Our findings suggest that most posts in the treatment and prevention methods category in the first two weeks of the study are related to informal and unscientific content. Conclusion: Authorities should focus on disseminating reliable and precise information about precautions and treatment research during this period and ensure that non-scientific resources do not become viral about a pandemic such as COVID-19.

Keywords

Noval Coronavirus, COVID-19, Pandemics, Twitter, Social media

Covid-19 Pandemisinin İlk Aylarında Twitter Gönderilerinin Metinsel Analizi.

Abstract

Amaç: Bu çalışmanın amacı, pandemi sırasında toplumun duygu ve düşünceleri konusundaki anlayışımızı geliştirmek için korona virüs ile ilgili tweet'lerin içeriğini analiz etmektir. Yöntem: Çalışmamız 11 Mart 2020-5 Mayıs 2020 tarihleri arasında gerçekleştirildi. Veriler Java tabanlı bir yazılım uygulaması kullanılarak önceden tanımlanmış İngilizce ve Türkçe anahtar kelimeler üzerinden çıkarıldı. Toplanan veriler tedavi ve önlemler kategorilerine odaklanan kelime tabanlı Levenshtein mesafe algoritması ile incelenerek önceden belirlenmiş 5 kategoriye (COVID-19 için alınacak önlemler, COVID-19 semptomları, COVID-19 güncel ve gelecekteki tedavileri, COVID-19 pandemisi ile ilgili komplo teorileri ve COVID-19 salgınının ekonomik sonuçları) ayrıldı. Bulgular: Toplam 87.264.342 tweet, makine öğrenme teknikleri ve algoritmaları ile analiz edildi. Retweet, reklamlar ve kurumsal tweetler hariç tutulduktan sonra, korona virüs ile ilgili toplam 5.529.891 tweet çalışmaya dahil edildi. Seçilen verilerin %32,3'ü (n = 1786000) COVID-19 için alınacak önlemler olarak ve %15,7'si (n = 867403) mevcut ve gelecekteki tedavilerle ilgili düşünceler olarak kategorize edilmiştir. Bulgularımız, çalışmanın ilk 2 haftasındaki tedaviler yöntemler kategorisindeki paylaşımların çoğunluğunun gayri resmi ve bilimsel olmayan içeriklerle ilgili olduğunu göstermektedir. Sonuç: Yetkililer, bu dönemde alınması gereken önlemler ve resmi tedavi araştırmaları hakkında güvenilir ve kesin bilgi yaymaya odaklanmalı ve COVID-19 gibi bir pandemi hakkında bilimsel olmayan kaynakların viral haline gelmemesini sağlamalıdır.

Keywords

Yeni Koronavirüs, COVID-19, Pandemi, Twitter, Sosyal medya.

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