Covid-19 Salgını ile İlgili Paylaşımlar Üzerinde Veri Analizi

Yıl 2022, Cilt: 15 Sayı: 1, 13 - 23, 31.01.2022

Nur Tuna , Aslı Sebatlı Sağlam , Fatih Çavdur

https://doi.org/10.17671/gazibtd.928990

Cited By: 4

Öz

Tüm Dünya’yı etkisi altına alan Covid-19 salgını, Twitter sosyal medya platformunda salgın ile ilgili konularda büyük veri kümelerinin oluşumuna sebep olmuştur. Oluşan bu veri kümeleri, toplumun konuya yaklaşımını belirlemek adına veri analiz çalışmaları için zengin bir veri kaynağı teşkil etmektedir. Bu çalışmada, Covid-19 salgını ile ilgili Twitter paylaşımları üzerinde R programlama dili kullanılarak çeşitli veri analizleri yapılmıştır. Bu uygulamalar genel olarak metin analizi, ağ analizi ve duygu analizi şeklinde sınıflandırılabilir. Çalışmada, “#covid19”, “#covid-19” ve “#coronavirus” etiketlerine sahip İngilizce dilinde 09.12.2020 ve 20.03.2021 tarihleri arasında yapılan 110.883 paylaşım toplanarak temizlenmiştir. Çalışma kapsamında yapılan analizlerde, konu ile ilgili en çok paylaşım yapılan kullanıcı lokasyon bilgileri, birlikte en sık kullanılan kelime ve kelime çiftleri ile olumlu ve olumsuz kelimeler tespit edilmiştir. Yapılan çalışmanın, toplumun sosyal medyada paylaştığı çeşitli fikir ve düşüncelerinin hangi yönde olduğunu görmek açısından önemli olduğu düşünülmektedir. Elde edilen sonuçlar incelendiğinde, insanların duygu ve düşüncelerinin yanı sıra, ihtiyaç ve beklentilerini de sosyal ağlar aracılığıyla dile getirdiği görülmüştür. Ayrıca Twitter sosyal medya platformunun toplumu etkileyen güncel olaylar hakkında anında bilgi almak amacıyla kullanılabilecek olan en önemli sosyal ağlardan biri olduğu bir kez daha anlaşılmıştır.

Anahtar Kelimeler

covid-19, tweet analizi, metin analizi, ağ analizi, kelime ağı, duygu analizi

Data Analysis on the Covid-19 Pandemic-Related Posts

Öz

The Covid-19 pandemic affected the whole world caused the formation of large data sets on pandemic-related issues on the Twitter social media platform. These data sets constitute a rich data source for data analysis studies in order to determine the approach of the society to the subject. In this study, some data analyzes are carried out using R programming language on the Twitter posts (tweets) related to the Covid-19 pandemic. These operations can be generally classified as text analysis, network analysis and sentiment analysis. In this study, 110,883 tweets posted between 09.12.2020 and 20.03.2021 in English with “#covid19”, “#covid-19” and “#coronavirus” hashtags are collected and cleaned. The most tweeted user location information, the most frequently used words and word pairs, positive and negative words are analyzed in the study. The study is important in the sense that it allows us to see the directions of the various ideas and thoughts of the society on social media. It is observed from our finding that the society expressed its needs and expectations as well as its feelings and thoughts through social networks. It is also once again understood that Twitter is one of the most important social media platforms that we can use to receive instant information about the current events affecting the society.

Anahtar Kelimeler

covid-19, tweet analysis, text mining, network analysis, word network, sentiment analysis

Kaynakça

• Internet: Hootsuite, Digital 2020, https://www.hootsuite.com/resources/digital-2020, 27.09.2021.
• Internet: Pbworks, Hashtag, http://twitter.pbworks.com, 08.01.2021.
• Internet: I. Lunden, Analyst: Twitter Passed 500M Users In June 2012, 140M Of Them In US; Jakarta ‘Biggest Tweeting City’, TechCrunch, https://techcrunch.com/2012/07/30/analyst-twitter-passed-500m-users-in-june-2012-140m-of-them-in-us-jakarta-biggest-tweeting-city/, 08.01.2021.
• Internet: L. D’Monte, Swine flu's tweet causes online flutter, Business Standard, https://www.business-standard.com/article/technology/swine-flu-s-tweet-tweet-causes-online-flutter-109042900097_1.html, 08.01.2021.
• Internet: J. Clement, Number of monthly active Twitter users worldwide from 1st quarter 2010 to 1st quarter 2019, Statistica, https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users/, 30.01.2021.
• Internet: M. Iqbal, Twitter Revenue and Usage Statistics (2020), BusinessOfApps, https://www.businessofapps.com/data/twitter-statistics/, 12.03.2021.
• Internet: DSÖ (Dünya Sağlık Örgütü), WHO Coronavirus Disease (COVID-19) Dashboard, https://covid19.who.int/, 22.09.2021.
• R. Li, K. H. Lei, R. Khadiwala, K. C. C. Chang, “Tedas: A twitter-based event detection and analysis system”, 2012 IEEE 28th International Conference on Data Engineering, Arlington, VA, USA, 1273-1276, 1-5 April, 2012.
• W. He, S. Zha, L. Li, “Social media competitive analysis and text mining: A case study in the pizza industry”, International journal of information management, 33(3), 464-472, 2013.
• S. M. Al-Daihani, A. Abrahams, “A text mining analysis of academic libraries' tweets”, The journal of academic librarianship, 42(2), 135-143, 2016.
• A. Chatfield, U. Brajawidagda, “Twitter Tsunami Early Warning Network: A Social Network Analysis of Twitter Information Flows”, 23rd Australasian Conference on Information Systems, Deakin University, Australia, 1-10, 3-5 December, 2012.
• A. G. F. Sert, S. Tüzüntürk, N. Gürsakal, “NodeXL ile Sosyal Ağ Analizi: #akademikzam Örneği”, 15. Uluslararası Ekonometri, Yöneylem Araştırmaları ve İstatistik Sempozyumu, Süleyman Demirel Üniversitesi, Isparta, Türkiye, 464-482, 22-25 Mayıs, 2014.
• L.S. Elkin, K. Topal, G. Bebek, “Network based model of social media big data predicts contagious disease diffusion”, Information discovery and delivery, 45(3), 110-120, 2017.
• D. A. Broniatowski, A. M. Jamison, S. Qi, L. AlKulaib, T. Chen, A. Benton, S. C. Quinn, M. Dredze, “Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate”, American journal of public health, 108(10), 1378-1384, 2018.
• A. Agarwal, B. Xie, I. Vovsha, O. Rambow, R. J. Passonneau, “Sentiment analysis of twitter data”, Workshop on Language in Social Media (LSM 2011), Portland, Oregon, USA, 30-38, 23 June, 2011.
• J. Bollen, H. Mao, X. Zeng, “Twitter mood predicts the stock market”, Journal of computational science, 2(1), 1-8, 2011.
• H. Wang, D. Can, A. Kazemzadeh, F. Bar, S. Narayanan, “A system for real-time twitter sentiment analysis of 2012 us presidential election cycle”, ACL 2012 System Demonstrations, Jeju Island, Korea, 115-120, 10 July, 2012.
• J. Bian, K. Yoshigoe, A. Hicks, J. Yuan, Z. He, M. Xie, Y. Guo, M. Prosperi, R. Salloum, F. Modave, “Mining Twitter to assess the public perception of the “Internet of Things””, PloS one, 11(7), e0158450, 2016.
• M. Kaya, G. Fidan, I. H. Toroslu, “Sentiment analysis of turkish political news”, 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Macau, China, 174-180, 4-7 December, 2012.
• R. Dehkharghani, B. A. Yanikoglu, Y. Saygin, K. Oflazer, “Sentiment analysis in Turkish at different granularity levels”, Natural Language Engineering, 23(4), 535-559, 2017.
• Internet: C. G. Healey, Tweet Sentiment Visualization App., https://www.csc2.ncsu.edu/faculty/healey/tweet_viz/, 08.01.2021.
• B. Karaöz, U. T. Gürsoy, “Adaptif Öğrenme Sözlüğü Temelli Duygu Analiz Algoritması Önerisi”, Bilişim Teknolojileri Dergisi, 11(3), 245-253, 2018.
• A. Gruzd, P. Mai, “Going viral: How a single tweet spawned a covid-19 conspiracy theory on twitter”, Big Data & Society, 7(2), 2053951720938405, 2020.
• W. Ahmed, J. Vidal-Alaball, J. Downing, F. L. Segui, “Covid-19 and the 5G conspiracy theory: social network analysis of twitter data”, Journal of medical internet research, 22(5), e19458, 2020.
• H. W. Park, S. Park, M. Chong, “Conversations and medical news frames on twitter: Infodemiological study on covid-19 in south korea”, Journal of Medical Internet Research, 22(5), e18897, 2020.
• B. Kim, “Effects of social grooming on incivility in COVID-19”, Cyberpsychology, Behaviour, and Social Networking, 23(8), 519-525, 2020.
• A. Abd-Alrazaq, D. Alhuwail, M. Househ, M. Hamdi, Z. Shah, “Top concerns of tweeters during the COVID-19 pandemic: infoveillance study”, Journal of medical Internet research, 22(4), e19016, 2020.
• M. O. Lwin, J. Lu, A. Sheldenkar, P. J. Schulz, W. Shin, R. Gupta, Y. Yang, Y. “Global sentiments surrounding the COVID-19 pandemic on Twitter: analysis of Twitter trends”, JMIR Public Health and Surveillance, 6(2), e19447, 2020.
• C. de Las Heras-Pedrosa, P. Sanchez-Nunez, J. I. Pelaez, “Sentiment analysis and emotion understanding during the covid-19 pandemic in spain and its impact on digital ecosystems”, International Journal of Environmental Research and Public Health, 17(15), 5542, 2020.
• Y. Su, J. Xue, X. Liu, P. Wu, J. Chen, C. Chen, T. Liu, W. Gong, T. Zhu, “Examining the impact of COVID-19 lockdown in Wuhan and Lombardy: a psycholinguistic analysis on Weibo and Twitter”, International Journal of Environmental Research and Public Health, 17(12), 4552, 2020.