Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi

Ali Efe İralı

doi:10.31123/akil.1144768

EN TR

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

References

Abdelkader, O. A. (2021). A proposed benchmark guide for customer engagement rating via YouTube channels. Turkish Journal of Computer and Mathematics Education, 12(13), 6286-6296.
Adamová, V. (2020). Dashcam as a Device to Increase the Road Safety Level. In: Hájek, P. & Ondřej, V. (Eds.), CBU International Conference on Innovations in Science and Education (Natural Sciences and ICT), 18-20 March 2020 (p. 1-5), Prague, Cezch Republic.
Akarsu, H. & Sever, N. S. (2019). Türkiye’de Ad Engagement Kavramı: Akademi ve Uzman Perspektifinden Bir Değerlendirme. Erciyes İletişim Dergisi: Uluslararası Dijital Çağda İleitşim Sempozyumu Özel Sayısı, 203-224.
Anderson, D. R., Sweeney, D. J. & Williams, T. A. (2016). Essentials of Modern Business Statistics with Microsoft Excel. Boston, USA: Cengage Learning.
Bazilinskyy, P., Eisma, Y. B., Dodou, D. & de Winter, J. F. (2020). Risk perception: A study using dashcam videos and participants from different world regions. Traffic Injury Prevention, 21(6), 347-353.
Bienvenido, H. P. & Ruiz, M. F. (2013). User generated content: A situated production of video walkthroughts on You- tube. In: K. Mitgutsch, S. Huber, J. Wimmer, M. G. Wagner, & H. Rosenstingl (Eds.), Context Material: Exploring and Reframing Games in Context: Proceedings of the 7th Vienna Games Conference (FROG), 27-29 September 2013 (p. 136-147), Vienna, Austria.
Braitman, K. A., McCartt, A. T., Zuby, D. S. & Singer, J. (2010). Volvo and Infiniti Drivers’ Experiences With Select Crash Avoidance Technologies. Traffic Injury Prevention, 11, 270-278.
Breen, L. J. (2006). Silenced Voices: Experiences of Grief Following Road Traffic Crashes in Western Australia. (Unpub- lished Doctoral dissertation). Edith Cowan University.
Chatzopoulou, G., Sheng, C., & Faloutsos, M. (2010). A First Step Towards Understanding Popularity in YouTube. 2010 INFOCOM IEEE Conference on Computer Communications Workshops, 15-19 March 2010 (p. 1-6), San Diego, USA.
Chelaru, S. V., Orellana-Rodriguez, C., & Altingovde, I. S. (2012). Can Social Features Help Learning to Rank YouTube Videos? In: X. S. Wang, I. Cruz, A. Delis & G. Huang (Eds.), Lecture Notes in Computer Science (LNCS 7651) (pp. 552- 566), Dordrecht, London & New York: Springer-Verlag Berlin Heidelberg.

Conche, F. & Tight, M. (2006). Use of CCTV to determine road accident factors in urban areas. Accident Analysis & Prevention, 38, 1197-1207.
Cvijikj, I. P. & Michahelles, F. (2013). Online engagement factors on Facebook brand pages. Social Network ANalysis and Mining, 3, 843-861.
Davis, G. & Pecar, B. (2013). Business Statistics Using Excel®. Oxford University Press.
De Winter, J. F. (2013). Using Student’s t-test with extremely small sample sizes. Practical Assessment, Research & Evaluation, 18(10), 1-12.
Derrick, B., Toher, D. & White, P. (2016). Why Welch’s test is Type I error robust. The Quantitative Methods for Psychol- ogy, 12(1), 30-38.
Dever, A. (2019). Modern Sporda Gözetim: Büyük Spor Organizasyonlarında Bir Panoptikon Olarak CCTV Kameralar.Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 9(2), 687-700.
Dimitrova, D. S., Kaishev, V. K. & Tan, S. (2020). Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software, 95(10), 1-42.
Djerf-Pierre, M., Lindgren, M. & Budinski, M. A. (2019). The Role of Journalism on YouTube: Audience Engagement with ‘Superbug’ Reporting. Media and Communication, 7(1), 235-247.
Dopfer, A., & Wang, C. C. (2013, December 2-4). What can we learn from accident videos?. CACS International Auto- matic Control Conference (p.68-73), 2-4 December 2013, Sun Moon Lake, Taiwan.
Doruk, Ö. T. (2022). Covid-19’un Finansal Performansa Etkisi: Gıda Sektörü Firmaları İçin Karşılaştırmalı Bir Değerlendirme. Muahsebe ve Denetime Bakış, 66, 67-82.
Eğri, A. & Güney, C. (2020). Telekomünikasyon sektöründe kullanılan ek odası kapaklarının sokak düzeyi görüntülerin- den tespit edilme başarımının değerlendirilmesi. Jeodezi ve Jeoinformasyon Dergisi, 7(2), 133-154.
Goobie, G. C., Guler, S. A., Johannson, K. A., Fisher, J. H. & Ryerson, C. J. (2019). YouTube Videos as a Source of Misinformation on Idiopathic Pulmonary Fibrosis. Annals of the American Thoracic Society, 16(5), 572-579.
Güzel, Ş. & Uyar, S. (2019). İşe Angaje Olmak Kavramı, Tükenmişlik ile İlişkisi ve Sonuçları. Sosyal Araştırmalar ve Yönetim Dergisi, 1, 44-52.
Hall, J. A. (2016). When is social media use social interaction? Defining mediated social interaction. New Media & So- ciety, 20(1), 1-18.
Heimgärtner, R. (2007). Cultural Differenecs in Human Computer Interaction: Results from Two Online Surveys. In: A.
Osswald, M. Stempfhuber, & C. Wolff (Eds.) Neue Perspektiven im Kontext von Information und Wissen: 10, Internation- alen Symposiums für Informationswissenschaft ISI (p. 145-157), 30 Mai -1 Juni 2007, Köln, Germany.
Jan, S. L. & Shieh, G. (2011). Optimal sample sizes for Welch’s test under various allocation and cost considerations.
Behavior Research Methods, 43, 1014-1022.
Ji, L. J. & An, S. (2020). Cultural Differences in People’s Responses to Accidents. Journal of Loss and Trauma, 26(3), 1-12.
Khan, M. L. (2017). Social media engagement: What motivates user participation and consumption on YouTube?. Com- puters in Human Behavior, 66, 236-247.
Kim, J., Park, S., & Lee, A. U. (2020). Dashcam Witness: Video Sharing Motives and Privacy Concerns Across Different Nations. IEEE Access, 8, 110425-110437.
Koç, E. (2010). Gabriel Marcel’e Göre Fanatizm. Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Sosyal Bilimler Dergisi, 22, 127-138.
Kujur, F. & Singh, S. (2018). Emotions as predictor for consumer engagement in YouTube advertisement. Journal of Advances in Management Research, 15(2), 184-197.
Li, M., Yan, S., Yang, D., Li, B. & Cui, W. (2019). YouTube™ as a source of information on food poisoning. BMC Public Health, 19(952), 1-6.
Liikkanen, L. A. & Salovaara, A. (2015). Music on YouTube: User engagement with traditional, user-appropriated and derivative videos. Computers in Human Behavior, 50, 108-124.
MacLeod, M. G., Hoppe, D. J., Smunovic, N., Bhandari, M., Philippon, M. J. & Ayeni, O. R. (2015). YouTube As an Infor- mation Source for Femoroacetabular Impingement: A Systematic Review of Video Content. The Journal of Arthroscopic and Related Surgery, 31(1), 136-142.
McKnight, P. E., & Najab, J. (2010). Mann-Whitney U Test. In: Weiner, I. B. & Craighead, W. E. (Eds.), The Corsini En- cyclopedia of Psychology, 960.
Meier, P. (1953). Variance of a Weighted Mean. Biometrics, 9(1), 59-73.
Mersey, R. D., Malthouse, E. C., & Calder, B. J. (2010). Engagement with Online Media. Journal of Media Business Studies, 7(2), 39-56.
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C. & Keshri, A. (2019). Descriptive Statistics and Normality Tests for Statistical Data. Annals of Cardiac Anaesthesia, 22(1), 67-72.
Mulders, M. & Wendt, J. (2022). Beyond Viewing Counts, Likes & Co: An Analysis of Intructional Videos on Youtube.Proceedings of the 14th International Conference on Computer Supported Education (CSEDU) (p. 236-241), 22-24 April 2022, Online.
Munaro, A. C., Barcelos, R. H., Maffezzolli, E. F., Rodrigues, J. S. & Paraiso, E. C. (2021). To engage or not engage? The features of video content on YouTube affecting digital consumer engagement. Journal of Consumer Behavior, 20(5), 1-17.
Munger, K., & Phillips, J. (2020). Right-Wing YouTube: A Supply and Demand Perspective. The International Journal of Press/Politics, 27(1), 1-34.
Nisa, M. U., Mahmood, D., Ahmed, G., Khan, S., Mohammed, M. A., & Damaševicius, R. (2021). Optimizing Prediction of YouTube Video Popularity Using XGBoost. Electronics, 10(23), 1-16.
Oghina, A., Breuss, M., Tsagkias, M., & de Rijke, M. (2012). Predicting IMDB Movie Ratings Using Social Media. In: R. E. Baeza-Yates, A. P. de Vries, H. Zaragoza, B. B. Cambazoğlu, V. Murdock, R. Lempel, & F. Silvestri (Eds.), Lecture Notes in Computer Science: European Conference on Information Retrieval ECIR 2012: Advances in Information Re- trieval (pp. 503-507), New York: Springer -Verlag Berlin Heidelberg.
Ovla, H. D., & Taşdelen, B. (2012). Aykırı Değer Yönetimi. Mersin Üniversitesi Sağlık Bilim Dergisi, 5(3), 1-8.
Özkan, T. (2006). The regional differences between countries in traffic safety: A cross-cultural study and Turkish Case (UnpublishedDoctoral dissertation), University of Helsinki & Middle East Technical University.
Pantti, M. (2015). Grassroots humanitarianism on YouTube: Ordinary fundraisers, unlikely donors, and global solidarity.The International Communication Gazette, 77(7), 622-636.
Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A. A., Jarawan, E., & Mathers, C. (2004). World report on road traffic injury prevention. Geneva: World Health Organization & The World Bank.
Pillai, M. S., Chaudhary, G., Khari, M., & Crespo, R. G. (2021). Real-time image enhancement for an autometic automo- bile accident detection through CCTV using deep learning. Soft Computing, 25, 11929–11940.
Racioppi, F., Eriksson, L., Tingvall, C., & Villaveces, A. (2004). Preventing Road Traffic Injury: A Public Health Perspec- tive for Europe. Copenhagen: World Health Organization.
Razali, N. M. & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson- Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-23.
Resnick, E. (2001). Defining Engagement. Journal of International Affairs, Rogue States: Isolation vs. Engagement in the 21st Century, 54(2), 551-566.
Rutter, R., Roper, S. & Lettice, F. (2016). Social media interaction, the university brand and recruitment performance. Journal of Business Research, 69(8), 3096-3104.
Sashi, C. M. (2012). Customer engagement, buyer-seller relationships, and social media. Management Decision, 50(2), 253-272.
Schultes, P., Dorner, V. & Lehner, F. (2013). Leave a Comment! An In-Depth Analysis of User Comments on YouTube. In: R. Alt, & B. Franczyk (Eds.), Proceedings of 11th International Conference on Wirtschaftsinformatik (p. 659-673), 27 February-1 March 2013, Leipzig, Germany.
Shah, A., Lamare, J. B., Anh, T. N. & Hauptmann, A. (2018). CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis. 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand, 1-9.
Soong, T. T. (2004). Fundamentals of Probability and Statistics for Engineers. West Sussex, England: John Wiley & Sons, Ltd.
Steinskog, D. J., Tjøstheim, D. B. & Kvamstø, N. G. (2007). Notes and Correspondence: A Cautionary Note on the Use of the Kolmogorov-Smirnov Test for Normality. American Meteorological Society, 135(3), 1151-1157.
Swearingen, K., & Sinha, R. (2002). Interaction Design for Recommender Systems. Designing Interactive Systems: Processes, Practices, Methods, and Techniques, DIS (p. 1-11), 25-28 June 2002, London, England.
Szmuda, T., Syed, M. T., Singh, A., Ali, S., Özdemir, C. & Słoniewski, P. (2020). YouTube as a source of patient informa- tion for Coronavirus Disease (COVID-19): A content-quality and audience engagement analysis. Reviews in Medical Virology, 30(5), 1-8.
Şenlik, A. (2021). Sosyal Medya Organlarında Canlı Yayın Yapma Alışkanlığı: Twitter›de Ölümlü Trafik Kazaları İçerikli İletilerin İncelenmesi. Journal of Communication Science Researches - IBAD, 1(1), 1-14.
Thelwall, M., Sud, P. & Vis, F. (2011). Commenting on YouTube Videos: From Guatemalan Rock to El Big Bang. Journal of the American Society for Information Science and Technology, 63(3), 616-629.
Vafaei-Zadeh, A., Ng, S. X., Hanifah, H., Teoh, A. P. & Nawaser, K. (2021). Safety Technology Adoption: Predicting Inten- tion to Use Car Dashcams in an Emerging Country. International Journal of Innovation and Technology Management, 18(5), 1-33.
Veletsianos, G., Kimmons, R., Larsen, R., Dousay, T. A. & Lowenthal, P. R. (2018). Public comment sentiment on educa- tional videos: Understanding the effects of presenter gneder, video format, threading, and moderation on YouTube TED talk comments. PLoS ONE, 13(6), 1-21.
Veni, S., Anand, R. & Santosh, B. (2021). Road Accident Detection and Severity Determination from CCTV Surveillance. In: A. K. Tripaty, M. Sarkar, J. P. Sahoo, K. C. Li, & S. Cinara (Eds.), Lecture Notes in Networks and Systems: Advances in Distributed Computing and Machine Learning 127 (pp. 247-256), Singapore: Springer.
Wang, C., Terken, J., Hu, J. & Rauterberg, M. (2016). “Likes” and “Dislikes” on the Road: A Social Feedback System for Imrpoving Driving Behavior. Proceedings of the 8th International Conference on Automotive User Interfaces and Interac- tive Vehicular Applications (p. 43-50), 24-26 October 2016, Ann Arbor, USA.
Watkins, B. A. (2016). Experimenting with dialogue on Twitter: An examination of the influence of the dialogic principles on engagement, interaction, and attitude. Public Relations Review, 43(1), 1-9.
Wikström, S. (1996). Value Creation by Company-Consumer Interaction. Journal of Marketing Management, 12, 359- 374.
Zhang, Z., He, Q., Gao, J. & Ni, M. (2018). A deep learning approach for detecting traffic accidents from social media data. Transportation Research Part C, 86, 580-596.
Chang, C. (2022, 05 12). “‘I made $245,000 in a month’: This 29-year-old got rejected from 15 medical schools—now he runs a $1.5 million business.” CNBC: https://www.cnbc.com/2022/05/12/how-this-29-year-old-built-a-million-dollar- business-after-getting-rejecte adresinden 04.06.2022 tarihinde erişilmiştir.
Elliott, V. (2022, 05 10). “How YouTube Can Rewrite the Past and Shape an Election: Philippine researcher Fatima Gaw says the platform has become a hub for pro-Marcos historical revisionism.” Wired: https://www.wired.com/story/ youtube-philippines-election/ adresinden 04.06.2022 tarihinde erişilmiştir.
Ganapathy, V. (t.y.). “Google Workspace Marketplace: YT Tracker: YouTube Stats & Analytics.” GoogleWorkplace:https:// workspace.google.com/marketplace/app/yt_tracker_youtube_stats_analytics/952783286913 adresinden 21.07.2021 tarihinde erişilmiştir.
Google Support. (t.y.). “Google, Engagement. Glossary.” Google: https://support.google.com/analytics/ answer/9355853?hl=en adresinden 02.07.2022 tarihinde erişilmiştir.
Google. (t.y.). “AdMob, YouTube and Blogger YouTube Partner Programme overview and eligibility.” Google AdMob: https://support.google.com/adsense/answer/72851?hl=en-GB adresinden 04.06.2022 tarihinde erişilmiştir.
Harmon, M. (2011). Nonparametric Testing in Excel: The Excel Statistical Master. https://books.google.com.tr/books?i d=LSLYjZlsWq4C&lpg=PP1&dq=Nonparametric%20Testing%20in%20Excel%3A%20The%20Excel%20Statistical%20 Master&hl=tr&pg=PP1#v=onepage&q&f=false 09.07.2022 tarihinde erişilmiştir.
Larsson, A. O. (2018). “Thumbs up, thumbs down? Likes and dislikes as popularity drivers of political YouTube videos.” First Monday: https://firstmonday.org/ojs/index.php/fm/article/view/8318 adresinden 09.07.2022 tarihinde erişilmiştir.
Maiorca, D. (2022, 05 07). “Should You Hide Your Subscriber Count on YouTube? The Pros and Cons.” MakeUseOf: https://www.makeuseof.com/hiding-youtube-subscriber-count-pros-cons/ adresinden 04.06.2022 tarihinde erişilmiştir.
MOBESE. (t.y.). “Kurumsal: Hakkımızda.” https://www.mobese.com.tr/tr/kurumsal/hakkimizda/1 adresinden 04.06.2022 tarihinde erişilmiştir.
The YouTube Team. (2021, Kasım 10). “YouTube Official Blog: An update to dislikes on YouTube.” YouTube: https://blog. youtube/news-and-events/update-to-youtube/ adresinden 04.06.2022 tarihinde erişilmiştir.
World Health Organization - WHO (2022, Haziran 20). “Road traffic injuries.” WHO: https://www.who.int/news-room/ fact-sheets/detail/road-traffic-injuries adresinden 09.10.2022 tarihinde erişilmiştir.

Details

Primary Language

Turkish

Subjects

Communication and Media Studies

Journal Section

Research Article

Authors

Ali Efe İralı ^*
0000-0001-5332-1367
Türkiye

Publication Date

December 7, 2022

Submission Date

July 17, 2022

Acceptance Date

November 16, 2022

Published in Issue

Year 2022 Number: 38

DOI

https://doi.org/10.31123/akil.1144768

IZ

https://izlik.org/JA64RW23CB

APA

İralı, A. E. (2022). Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, 38, 126-149. https://doi.org/10.31123/akil.1144768

AMA

1.İralı AE. Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi. Journal of Akdeniz University Faculty of Communication - JAUFC. 2022;(38):126-149. doi:10.31123/akil.1144768

Chicago

İralı, Ali Efe. 2022. “Beğeni Ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi”. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, nos. 38: 126-49. https://doi.org/10.31123/akil.1144768.

EndNote

İralı AE (December 1, 2022) Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi. Akdeniz Üniversitesi İletişim Fakültesi Dergisi 38 126–149.

IEEE

[1]A. E. İralı, “Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi”, Journal of Akdeniz University Faculty of Communication - JAUFC, no. 38, pp. 126–149, Dec. 2022, doi: 10.31123/akil.1144768.

ISNAD

İralı, Ali Efe. “Beğeni Ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi”. Akdeniz Üniversitesi İletişim Fakültesi Dergisi. 38 (December 1, 2022): 126-149. https://doi.org/10.31123/akil.1144768.

JAMA

1.İralı AE. Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi. Journal of Akdeniz University Faculty of Communication - JAUFC. 2022;:126–149.

MLA

İralı, Ali Efe. “Beğeni Ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi”. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, no. 38, Dec. 2022, pp. 126-49, doi:10.31123/akil.1144768.

Vancouver

1.Ali Efe İralı. Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi. Journal of Akdeniz University Faculty of Communication - JAUFC. 2022 Dec. 1;(38):126-49. doi:10.31123/akil.1144768

33280 33281

Journal of Akdeniz University Faculty of Communication is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC).

Analyzing Like and Comment Tendencies through Traffic Accident Videos

Abstract

Keywords

Beğeni ve Yorum Eğilimlerinin Trafik Kazası Videoları Üzerinden Analizi

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite