Assessing the Surge in COVID-19-Related Cyberbullying on Twitter: A Generalized Additive Model Approach

COVID-19 salgınının küresel başlangıcı, evde kalma talimatlarına yol açarak dijital etkileşimlerde artışa yol açmıştır. Bu artan dijital katılımın, başta siber zorbalık olmak üzere siber güvenlik tehditlerini artırdığı varsayılmaktadır. Bu araştırma, COVID-19 salgınının Twitter'daki siber zorbalık eğilimleri üzerindeki etkisini niceliksel olarak ölçmeyi amaçlamaktadır. Veri çıkarmak için Python kitaplıklarını kullanarak 1 Ocak 2020'den 12 Eylül 2020'ye kadar uzanan, herkese açık 126.348 tweet'ten oluşan bir veri kümesi topladık. 'Çevrimiçi zorbalık', 'siber zorbalık' ve 'Twitter zorbalığı' gibi siber zorbalığa bağlı 18 spesifik anahtar kelimeye odaklanarak, ilgili siber zorbalık örneklerini belirlemeye çalıştık. Analitik çalışmalarımızda, karmaşık dalgalanmaları yakalamada yetersiz kalabilecek geleneksel bir değişim noktası modelini benimsemek yerine, spline tabanlı yumuşatıcılara sahip bir Genelleştirilmiş Toplama Modeli (GAM) kullandık. Bu yaklaşım, Mart ortasından itibaren siber zorbalık faaliyetlerinde belirgin bir artışı ustaca ortaya çıkarmıştır ve evde kalma protokollerinin küresel uygulamasıyla uyumlu hale geldiği tespi edilmiştir. Gözlemlenen bu eğilim, odak noktasının toplu anahtar kelime sayısı mı yoksa tek tek anahtar kelime örnekleri mi olduğuna bakılmaksızın doğrulanmıştır. Analizimizi daha da zenginleştirerek gecikmeye dayalı değerlendirmeleri dikkate aldık ve seçtiğimiz GAM metodolojisini alternatif modelleme stratejileriyle karşılaştırdık. Toplu olarak, içgörülerimiz, pandemiyle ilgili kısıtlamaların uygulanması ile Twitter'daki siber zorbalıktaki artış arasındaki güçlü bağlantının altını çizmektedir ve küresel krizlerin ortasında siber uyanıklığın artırılmasına yönelik acil ihtiyacı vurgulamaktadır.

Anahtar Kelimeler:

Covid19, Siber Zorbalık, Twitter, GAM

Assessing the Surge in COVID-19-Related Cyberbullying on Twitter: A Generalized Additive Model Approach

The COVID-19 pandemic's onset and the subsequent lockdowns drastically amplified digital interactions worldwide. These unparalleled shifts in online behavior birthed concerns about potential surges in cybersecurity threats, particularly cyberbullying. Our research aimed to explore these proposed trends on Twitter. Utilizing a dataset of 126,348 tweets from January 1st to September 12th, 2020, we honed in on 27 cyberbullying-related keywords, like 'online bullying' and 'cyberbullying'. Recognizing the limitations of traditional change-point models, we opted for a Generalized Additive Model (GAM) with spline-based smoothers. The results were revealing. A significant uptick in cyberbullying instances emerged starting mid-March, correlating with the global lockdown mandates. This consistent trend was evident across all our targeted keywords. To bolster our findings, we conducted lag-based assessments and compared the GAM against other modeling approaches. Our conclusions robustly indicate a strong association between the enforcement of pandemic lockdowns and a heightened prevalence of cyberbullying on Twitter. The implications are clear: global crises necessitate intensified cyber vigilance, and the digital realm's safety becomes even more paramount during such challenging times.

Keywords:

covid19, Cyberbullying, Twitter, GAM,

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