COVID-19 Pandemisi ile İlgili Yanlış Bilgiler Üzerine Bir Metin Madenciliği Analizi

Öz

COVID-19 pandemisinin başından beri pandemi ile ilgili çok büyük miktarda veri üretilmiştir ve bunun önemli bir kısmı genellikle sosyal medya tarafından yayılmış doğrulanmamış veridir. Pandemi ile ilgili birçok komplo teorisinin propagandasını yapan "plandemic" adlı bir video yayımlanması ardından, insanlar pandemi ile ilgili yanlış bilgileri bu etikete sahip tweetler atarak paylaşmıştır. Bu çalışmada, bu etiket ve buna benzer bir etiket olan "scamdemic" yardımıyla binlerce tweet toplanarak bir çalışma grubu oluşturulmuştur. Ayrıca pandemi ile ilgili daha genel bilgiler içeren tweetler toplanarak bir kontrol grubu oluşturulmuştur. Çalışma grubundaki tweetlerde verilen internet kaynaklarının çok daha az güvenilir olduğu gösterilmiştir. İlaveten, Hedonometer ve VADER kullanılarak iki duygu analizi gerçekleştirilmiştir. Hedonometer göstermiştir ki, sahte haber yayan tweetler önemli derecede daha fazla negatif duyguya sahiptir. Bu, VADER’in emoji ya da büyük harfler gibi sözcüksel olmayan yapıları da dikkate alması gerçeği ile ilişkilendirilebilir.

Anahtar Kelimeler

• Metin Madenciliği
• Duygu Analizi
• SARS-CoV-2
• COVID-19 Pandemisi
• Twitter Analizi

A Text Mining Analysis on Misinformation Regarding the COVID-19 Pandemic

Öz

Since the outset of COVID-19 pandemic, a massive amount of information has been generated about the pandemic, where a great deal of it contains less verifiable information disseminated especially via social media. A video propagating various conspiracy theories about the pandemic, called plandemic, was launched, and people started to share posts addressing this issue with this hashtag thereafter. For this research, we collected thousands of tweets using this hashtag, and then combined this collection with a collection of tweets with a similar hashtag #scamdemic to build a study group. Also, we collected tweets that convey more general thoughts about the pandemic, which served as a control group. We showed that the web sources provided in the tweets in the study group tend to be much less credible. Furthermore, we performed two sentiment analysis using Hedonometer and VADER. Hedonometer showed that the average happiness level in tweets spreading misinformation about COVID -19 is almost the same as in regular COVID -19 tweets. However, VADER showed that the tweets spreading the misinformation have significantly more negative sentiment. This could be related to the fact that the VADER also takes into account non-lexical items, such as emoticons and capital letters.

Anahtar Kelimeler

• Text Mining
• Sentiment Analysis
• SARS-CoV-2
• COVID-19 Pandemic
• Twitter Analysis

Kaynakça

1. World Health Organization. (2021). COVID-19 Weekly Epidemiological Update. https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---22-june-2021.
2. Wajahat, H. (2020). Role of social media in covid-19 pandemic. The International Journal of Frontier Sciences, 4(2), 59-60.
3. Singh, L., Bansal, S., Bode, L., Budak, C., Chi, G., Kawintiranon, K., ... & Wang, Y. (2020). A first look at COVID-19 information and misinformation sharing on Twitter. arXiv preprint arXiv:2003.13907.
4. Lewandowsky, S., & Cook, J. (2020). The Conspiracy Theory Handbook https://www.climatechangecommunication.org/wpcontent/uploads/2020/03/ConspiracyTheoryHandbook.pdf
5. Tagliabue, F., Galassi, L., & Mariani, P. (2020). The “pandemic” of disinformation in COVID-19. SN comprehensive clinical medicine, 2(9), 1287-1289.
6. Radu, R. (2020). Fighting the ‘Infodemic’: Legal Responses to COVID-19 Disinformation. Social Media+ Society, 6(3).
7. Brennen, J. S., Simon, F., Howard, P. N., & Nielsen, R. K. (2020). Types, sources, and claims of COVID-19 misinformation. Reuters Institute, 7, 3-1.
8. Baker, S. A. (2020). Tackling Misinformation and Disinformation in the Context of COVID-19. In Cabinet Office C19 Seminar Series. Cabinet Office.

9. Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, 8(1).
10. Aggarwal, C. C., & Zhai, C. (Eds.). (2015). Mining text data. Springer Science & Business Media.
11. Isah, H., Trundle, P., & Neagu, D. (2014, September). Social media analysis for product safety using text mining and sentiment analysis. In 2014 14th UK workshop on computational intelligence (UKCI), 1-7.
12. Zhang, Q., Yi, G. Y., Chen, L. P., & He, W. (2021). Text mining and sentiment analysis of COVID-19 tweets. arXiv preprint arXiv:2106.15354.
13. Jianqiang, Z., Xiaolin, G., & Xuejun, Z. (2018). Deep convolution neural networks for twitter sentiment analysis. IEEE Access, 6, 23253-23260.
14. Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019). Systematic literature review on the spread of health-related misinformation on social media. Social Science & Medicine, 240, 112552.
15. Sharma, K., Seo, S., Meng, C., Rambhatla, S., & Liu, Y. (2020). Covid-19 on social media: Analyzing misinformation in twitter conversations. arXiv e-prints, arXiv-2003.
16. Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E., ... & Baddour, K. (2020). Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter. Cureus, 12(3).
17. Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological science, 31(7), 770-780.
18. Huang, B., & Carley, K. M. (2020). Disinformation and misinformation on twitter during the novel coronavirus outbreak. arXiv preprint arXiv:2006.04278.
19. Koh, J. X., & Liew, T. M. (2020). How loneliness is talked about in social media during COVID-19 pandemic: text mining of Twitter feeds. Journal of psychiatric research, 4,492.
20. Mansour, S. (2018). Social media analysis of user’s responses to terrorism using sentiment analysis and text mining. Procedia Computer Science, 140, 95-103.
21. Ampofo, L., Collister, S., O'Loughlin, B., Chadwick, A., Halfpenny, P. J., & Procter, P. J. (2015). Text mining and social media: When quantitative meets qualitative and software meets people. Innovations in digital research methods, 161-192.
22. Gruzd, A., & Mai, P. (2020). Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter. Big Data & Society, 7(2).
23. Ahmed, W., Vidal-Alaball, J., Downing, J., & Seguí, F. L. (2020). COVID-19 and the 5G conspiracy theory: social network analysis of Twitter data. Journal of medical internet research, 22(5).
24. Kawchuk, G., Hartvigsen, J., Harsted, S., Nim, C. G., & Nyirö, L. (2020). Misinformation about spinal manipulation and boosting immunity: an analysis of Twitter activity during the COVID-19 crisis. Chiropractic & manual therapies, 28(1), 1-13.
25. Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PloS one, 6(12).
26. Cody, E. M., Reagan, A. J., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2015). Climate change sentiment on Twitter: An unsolicited public opinion poll. PloS one, 10(8), e0136092.
27. Frank, M. R., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2013). Happiness and the patterns of life: A study of geolocated tweets. Scientific reports, 3(1), 1-9.
28. Schwartz, A. J., Dodds, P. S., O'Neil‐Dunne, J. P., Danforth, C. M., & Ricketts, T. H. (2019). Visitors to urban greenspace have higher sentiment and lower negativity on Twitter. People and Nature, 1(4), 476-485.
29. Pano, T., & Kashef, R. (2020). A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19. Big Data and Cognitive Computing, 4(4), 33.
30. Bhaumik, U., & Yadav, D. K. (2021). Sentiment Analysis Using Twitter. In Computational Intelligence and Machine Learning, Springer, Singapore, 59-66.
31. Valdez, D., Ten Thij, M., Bathina, K., Rutter, L. A., & Bollen, J. (2020). Social media insights into US mental health during the COVID-19 pandemic: longitudinal analysis of twitter data. Journal of medical Internet research, 22(12), e21418.
32. Cui, L., & Lee, D. (2020). Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885.