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

Abstract

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.

Keywords

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

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

Abstract

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.

Keywords

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

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