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

Year 2022, Volume: 5 Issue: 3, 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.

References

• Singhal T. A Review of Coronavirus Disease-2019 (COVID-19). Indian journal of pediatrics. 2020;87(4):281-286.
• Dünya Sağlık Örgütü World Health Orgasization. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020. Erişim tarihi 25 Mayıs, 2020.
• Castelli L, Di Tella M, Benfante A, Romeo A. The Spread of COVID-19 in the Italian Population: Anxiety, Depression, and Post-traumatic Stress Symptoms. Canadian journal of psychiatry Revue canadienne de psychiatrie. 2020; 65(10): 731-732.
• Ozamiz-Etxebarria N, Dosil-Santamaria M, Picaza-Gorrochategui M, Idoiaga-Mondragon N. Stress, anxiety, and depression levels in the initial stage of the COVID-19 outbreak in a population sample in the northern Spain. Cadernos de saude publica. 2020;36(4):e00054020.
• The Lancet. COVID-19: fighting panic with information. Lancet. 2020 Feb 22;395(10224):537.
• Mahase E. Covid-19: death rate is 0.66% and increases with age, study estimates. Bmj. 2020 Apr 1;369:m1327.
• Thames G. Twitter as an educational tool. Journal of child and adolescent psychiatric nursing : official publication of the Association of Child and Adolescent Psychiatric Nurses, Inc. 2009 ;22(4):235.
• Budhwani H, Sun R. Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the "Chinese virus" on Twitter: Quantitative Analysis of Social Media Data. Journal of medical Internet research. 2020;22(5):e19301.
• Park HW, Park S, Chong M. Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea. Journal of medical Internet research. 2020;22(5):e18897.
• Ghosh P, Schwartz G, Narouze S. Twitter as a powerful tool for communication between pain physicians during COVID-19 pandemic. Regional anesthesia and pain medicine. 2020.
• Kouzy R, Abi Jaoude J, Kraitem A, ve ark. Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter. Cureus. 2020;12(3):e7255.
• Rosenberg H, Syed S, Rezaie S. The Twitter pandemic: The critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic. Cjem. 2020; 22(4):1-4.
• Ahmed W, Bath Peter A, Demartini G. Using Twitter as a Data Source: An Overview of Ethical, Legal, and Methodological Challenges. In: Kandy W, editor. The Ethics of Online Research: Emerald Publishing Limited; 2017; 4(2):79-107.
• Levenshtein VI. Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady. 1966;10 (8):707-710.
• Bohn A, Feinerer I, Hornik K, Mair P. Content-Based Social Network Analysis of Mailing Lists. R J. 2011;3(1):11-18.
• Jácome D, Tapia F, Lascano JE, Fuertes W. Contextual Analysis of Comments in B2C Facebook Fan Pages Based on the Levenshtein Algorithm. 2019; Cham: Springer International Publishing; 2019; 918:528-538.
• Wang A. Don't Follow Me - Spam Detection in Twitter; 2010. Sunulan Bildiri SECRYPT 2010 The International Joint Conference on e-Business and Telecommunications Athens, Greece, July 26-28, 2010
• Wang AH. Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach. 2010; Berlin, Heidelberg: Springer Berlin Heidelberg; 2010; 6166: 335-342.
• Commons A. Apache License, Version 2.0. https://commons.apache.org/proper/commons-bsf/license.html Erişim tarihi 20 Haziran, 2020
• Commons A. Apache Commons Text. https://commons.apache.org/proper/commons-text/ Erişim tarihi 20 Haziran, 2020
• Liang H, Fung IC, Tse ZTH, ve ark. How did Ebola information spread on twitter: broadcasting or viral spreading? BMC public health. 2019;19(1):438.
• Ahmed W, Vidal-Alaball J, Downing J, Lopez Segui F. COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data. Journal of medical Internet research. 2020;22(5):e19458.
• Fung IC, Duke CH, Finch KC, ve ark. Ebola virus disease and social media: A systematic review. American journal of infection control. 2016;44(12):1660-1671.
• Hossain L, Kam D, Kong F, Wigand RT, Bossomaier T. Social media in Ebola outbreak. Epidemiology and infection. 2016 ;144(10):2136-2143.
• Odlum M, Yoon S. What can we learn about the Ebola outbreak from tweets? American journal of infection control. 2015;43(6):563-571.
• Oyeyemi SO, Gabarron E, Wynn R. Ebola, Twitter, and misinformation: a dangerous combination? Bmj. 2014;349:g6178.
• Stefanidis A, Vraga E, Lamprianidis G, ve ark. Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts. JMIR public health and surveillance. 2017;3(2):e22.
• Wakefield AJ. MMR vaccination and autism. Lancet. 1999;354(9182):949-950.
• Guidry JP, Carlyle K, Messner M, Jin Y. On pins and needles: how vaccines are portrayed on Pinterest. Vaccine. 2015;33(39):5051-5056.
• Kang GJ, Ewing-Nelson SR, Mackey L, ve ark. Semantic network analysis of vaccine sentiment in online social media. Vaccine. 2017;35(29):3621-3638.
• Meadows CZ, Tang L, Liu W. Twitter message types, health beliefs, and vaccine attitudes during the 2015 measles outbreak in California. American journal of infection control. 2019;47(11):1314-1318.
• Türkiye Bilimler Akademisi. Turkey Academy of Sciences. Covid-19 Pandemi Değerlendirme Raporu. http://www.tuba.gov.tr/files/images/2020/kovidraporu/Covid-19%20Raporu-Final+.pdf. Erişim tarihi 23 Haziran, 2020