Analyzing Like and Comment Tendencies through Traffic Accident Videos

Abstract

The processes of using communication tools by individuals and societies may differ due to culture or spoken language. However, learning about reactions to similar content may offer important ideas for different mediums. The purpose of this study is to provide an understanding of how similar content can create an environment for the interaction of users watching in different languages. Therefore, the study focused on traffic accident-oriented videos that can be assumed to have similar responses globally. For the sample groups, the top 50 traffic accident channels with the most subscribers and 30 Turkish traffic accident channels were selected from YouTube. The channels and the videos were tested separately through two different sets of hypotheses. For the first hypothesis group, the comments and like rates of all channels were calculated; then, these rates were weighted by views to get channel ratios. For the second hypothesis group, the videos of the first 4 channels among the top 50 channels were selected in order to compare closely with the video counts of Turkish channels. Outliers of all data were calculated using the box-plot method. After the exclusion of outliers, Shapiro-Wilk and Kolmogorov-Smirnov normality tests were performed for channels and videos. Then, Welch’s T-Test was applied for channels (n1=47 and n2=28; p=0,041) and Mann-Whitney U Test (n3=586 and n4=579; p=0,00001) was applied for videos. Results showed that channels and videos had different averages. It was concluded that viewers of Turkish content tend to leave comments while leaving likes, compared to other groups.

Keywords

• Traffic Accidents
• Dash Cam
• Engagement Rate
• Mann-Whitney U Test
• Welch’s T-Test

Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi

Abstract

Bireylerin ve toplumların iletişim araçlarını kullanma süreçleri kültür ya da konuşulan dil dolayısıyla farklılıklar gösterebilmektedir. Bununla birlikte, benzer içeriklere gösterilen tepkilerin öğrenilmesi farklı araçlar için teoride önemli fikirler sunabilir. Bu çalışmanın amacı da, benzer bir içeriğin farklı dillerde izleyen kullanıcılarda nasıl bir etkileşim ortamı oluşturabileceğinin anlaşılmasını sağlamaktır. Bu yüzden, çalışma, küresel anlamda benzer tepkiler gösterileceği varsayılabilecek trafik kazası videolarına odaklanmıştır. Örneklem grupları için YouTube’da en çok aboneye sahip ilk 50 trafik kazası kanalı ile 30 adet Türkçe yayın yapan trafik kazası kanalı seçilmiştir. İki farklı hipotez grubuyla, kanallar ve kanalların videoları ayrı ayrı testlere tabi tutulmuşlardır. İlk hipotez grubu için tüm kanalların yorum ve beğeni oranları hesaplanmış; ardından izlenme sayıları ile ağırlıklı oranlamalar üzerinden kanalların ortalamaları elde edilmiştir. İkinci hipotez grubu için de, Türkçe kanalların video sayıları ile yakın sayıda bir örneklem grubu karşılaştırması yapabilmek için, ilk 50 kanal arasından ilk 4 kanalın videoları alınmıştır. Tüm verilerin box-plot yöntemiyle aykırı değerleri hesaplanmıştır. Çıkarılan aykırı değerler sonrasında, kanallar için Shapiro-Wilk, videolar için de Kolmogorov-Smirnov normallik testleri gerçekleştirilmiştir. Bu iki süreç sonrasında hipotez testlerine geçilmiş olup, kanallar için Welch’in T-Testi (n1=47 ve n2=28; p=0,041); videolar için Mann-Whitney U Testi (n3=586 ve n4=579; p=0,00001) uygulanmıştır. Sonuçlar hem kanallar için hem de videolar için farklı ortalamalara sahip olunduğunu göstermiştir. Türkçe içerik izleyicilerinin, diğer gruplara oranla, beğeni bırakırken aynı zamanda yorum yapma eğiliminde de olduğu tespit edilmiştir.

Keywords

• Trafik Kazaları
• Araç Kamerası
• Etkileşim Oranı
• Mann-Whitney U Testi
• Welch'in T-Testi

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