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Cross-country persistent tags discovery from YouTube trending video big dataset

Year 2023, Volume: 12 Issue: 4, 1538 - 1544, 15.10.2023
https://doi.org/10.28948/ngumuh.1330386

Abstract

YouTube is the primary video content platform among video sharing social media platforms with its easy-to-use interface and huge number of users. Due to the big data nature of YouTube video datasets, analyzing and extracting knowledge from these datasets would provide insights into researchers and government directors on social orientation and tendency YouTube users. However, analyzing YouTube big datasets is challenging due to the difficulty of image and speech processing applications, the hardness of utilizing semantic analysis methods on irregular YouTube contents, and big data nature of YouTube video datasets. Literature studies focus on video recommendation systems, semantic analysis on YouTube comments and trending video analysis. In this study, a new method and an algorithm are proposed to discover cross-country persistent tags over YouTube trending video big dataset for three countries (United States of America, Canada, and Great Britain). The discovered cross-country persistent tags show that some YouTube video tags are globally utilized on videos, while some certain tags are utilized for only one country.

Project Number

MMT2023/1-BAGEP

References

  • Oberlo, Oberlo YouTube statistics. https://www.oberlo.com/blog/youtube-statistics , Accessed 2023.
  • R. Novendri, A. S. Callista, D. N. Pratama, and C. E Puspita, Sentiment analysis of YouTube movie trailer comments using Naïve Bayes. Bulletin of Computer Science and Electrical Engineering, 1, 1, 26–32, 2020. https://doi.org/10.25008/bcsee.v1i1.5
  • M. Alkaff, A. R. Baskara and Y. H. Wicaksono, Sentiment analysis of Indonesian movie trailer on YouTube using Delta TF-IDF and SVM. 2020 5th International Conference on Informatics and Computing, pp. 1–5, Gorontalo, Indonesia,2020. https://doi.org/10.1109/ICIC50835.2020.9288579
  • S. Singh and G. Sikka, YouTube sentiment analysis on US elections 2020. ICSCCC 2021 -International Conference on Secure Cyber Computing and Communications, pp. 250–254, Jalandhar, India, 2021. https://doi.org/10.1109/ICSCCC51823.2021.9478128
  • M. Yan, J. Sang and C. Xu, Unified YouTube video recommendation via cross-network collaboration. ICMR 2015- Proceedings of the 2015 ACM International Conference on Multimedia Retrieval, pp. 19–26, Shanghai, China, 2015. https://doi.org/10.1145/2671188.2749344
  • P. Covington, J. Adams and E. Sargin, Deep neural networks for YouTube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198, Boston, Massachusetts, USA, 2016. https://doi.org/http://dx.doi.org/10.1145/2959100.2959190
  • R. Zhou, D. Xia, J. Wan and S. Zhang, An intelligent video tag recommendation method for improving video popularity in mobile computing environment. IEEE Access, 8, 6954–6967, 2020. https://doi.org/10.1109/ACCESS.2019.2961392
  • Q. Liu, R. Xie, L. Chen, S. Liu, K. Tu, P. Cui, B. Zhang and L. Lin, Graph neural network for tag ranking in Tag-enhanced video recommendation. International Conference on Information and Knowledge Management, Proceedings, 1, pp. 2613–2620, Ireland, 2020. https://doi.org/10.1145/3340531.3416021
  • S. Agarwal and A. Sureka, A focused crawler for mining hate and extremism promoting videos on YouTube. HT 2014- Proceedings of the 25th ACM Conference on Hypertext and Social Media, pp. 294–296, Santiago, Chile, 2014. https://doi.org/10.1145/2631775.2631776
  • A. Matamoros-Fernández, Platformed racism: the mediation and circulation of an Australian race-based controversy on Twitter, Facebook and YouTube. Information Communication and Society, 20, 6, 930–946, 2017. https://doi.org/10.1080/1369118X.2017.1293130
  • R. Ottoni, E. Cunha, G. Magno, P. Bernardina, W. Meira Jr. And V. Almeida, Analyzing Right-wing YouTube channels: Hate, violence and discrimination. Proceedings of the 10th ACM Conference on Web Science, pp. 323–332, Amsterdam, Netherlands, 2018. https://doi.org/10.1145/3201064.3201081
  • F. Figueiredo, F. Benevenuto and J. M. Almeida, The tube over time: Characterizing popularity growth of YouTube videos. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 745–754, Hong Kong, China, 2011. https://doi.org/10.1145/1935826.1935925
  • S. V. Chelaru, C. Orellana-Rodriguez and I. S. Altingovde, Can social features help learning to rank YouTube videos? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7651 LNCS, pp. 552–566, 2012. https://doi.org/10.1007/978-3-642-35063-4_40
  • A. Brodersen, S. Scellato and M. Wattenhofer, YouTube around the world: Geographic popularity of videos. WWW’12- Proceedings of the 21st Annual Conference on World Wide Web, pp. 241–250, Lyon France, 2012. https://doi.org/10.1145/2187836.2187870
  • A. Krishna, J. Zambreno and S. Krishnan, Polarity trend analysis of public sentiment on YouTube. The 19th International Conference on Management of Data (COMAD), pp. 125–128, Ahmedabad, India, 2013.
  • W. Hoiles, A. Aprem and V. Krishnamurthy, Engagement and popularity dynamics of YouTube videos and sensitivity to Meta-Data. IEEE Transactions on Knowledge and Data Engineering, 29, 7, 1426–1437, 2017. https://doi.org/10.1109/TKDE.2017.2682858
  • Y. L. Chen and C. L. Chang, Early prediction of the future popularity of uploaded videos. Expert Systems with Applications, 133, 59–74, 2019. https://doi.org/10.1016/j.eswa.2019.05.015
  • G. M. H. C. Gajanayake and T. C. Sandanayake, Trending pattern identification of youtube gaming channels using sentiment analysis. 20th International Conference on Advances in ICT for Emerging Regions, ICTer 2020- Proceedings, ICTer, pp. 149–154, Colombo, Sri Lanka, 2020. https://doi.org/10.1109/ICTer51097.2020.9325476
  • Y. Dokuz, Discovering popular and persistent tags from YouTube trending video big dataset. Mutimedia Tools and Applications, 1-19, 2023. https://link.springer.com/article/10.1007/s11042-023-16019-z
  • R. Sharma, YouTube Trending Video Dataset. https://www.kaggle.com/rsrishav/youtube-trending-video-dataset, Accessed 2022

YouTube trend büyük veri kümelerinden ülkeler arası kalıcı etiketlerin keşfi

Year 2023, Volume: 12 Issue: 4, 1538 - 1544, 15.10.2023
https://doi.org/10.28948/ngumuh.1330386

Abstract

YouTube kolay kullanılan arayüzü ve büyük miktarda kullanıcı sayısı ile video paylaşım sosyal medya platformları arasından birinci video paylaşım platformudur. YouTube video veri kümelerinin büyük veri doğasından dolayı bu veri kümelerinin analizi ve bilgi çıkarımı, araştırmacılar ve kurum yöneticilerine YouTube kullanıcılarının sosyal eğilimleri hakkında fikir vermektedir. Ancak, YouTube büyük verilerinin analizi, görüntü ve ses işleme uygulamalarının zorluğu, semantik analiz metotlarını düzensiz YouTube içeriklerine uygulamanın zorluğu ve YouTube video veri kümelerinin büyük veri özelliği nedeniyle zordur. Literatürdeki çalışmalar video tavsiye sistemleri, YouTube yorumlarından semantik analizler ve trend video analizleri üzerine odaklanmaktadır. Bu çalışmada, üç ülkeye ait YouTube trend video büyük verisi (Amerika Birleşik Devletleri, Kanada ve İngiltere) kullanılarak ülkeler arası kalıcı etiketlerin keşfi için yeni bir metot ve algoritma önerilmiştir. Keşfedilen ülkeler arası kalıcı etiketler, bazı YouTube video etiketlerinin küresel olarak kullanıldığı, ancak bazı etiketlerin ise yalnız bir ülkede kullanıldığını göstermektedir.

Supporting Institution

Niğde Ömer Halisdemir Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi Koordinatörlüğü

Project Number

MMT2023/1-BAGEP

Thanks

Bu araştırma Niğde Ömer Halisdemir Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi Koordinatörlüğü’nce desteklenmiştir. Proje No: MMT2023/1-BAGEP

References

  • Oberlo, Oberlo YouTube statistics. https://www.oberlo.com/blog/youtube-statistics , Accessed 2023.
  • R. Novendri, A. S. Callista, D. N. Pratama, and C. E Puspita, Sentiment analysis of YouTube movie trailer comments using Naïve Bayes. Bulletin of Computer Science and Electrical Engineering, 1, 1, 26–32, 2020. https://doi.org/10.25008/bcsee.v1i1.5
  • M. Alkaff, A. R. Baskara and Y. H. Wicaksono, Sentiment analysis of Indonesian movie trailer on YouTube using Delta TF-IDF and SVM. 2020 5th International Conference on Informatics and Computing, pp. 1–5, Gorontalo, Indonesia,2020. https://doi.org/10.1109/ICIC50835.2020.9288579
  • S. Singh and G. Sikka, YouTube sentiment analysis on US elections 2020. ICSCCC 2021 -International Conference on Secure Cyber Computing and Communications, pp. 250–254, Jalandhar, India, 2021. https://doi.org/10.1109/ICSCCC51823.2021.9478128
  • M. Yan, J. Sang and C. Xu, Unified YouTube video recommendation via cross-network collaboration. ICMR 2015- Proceedings of the 2015 ACM International Conference on Multimedia Retrieval, pp. 19–26, Shanghai, China, 2015. https://doi.org/10.1145/2671188.2749344
  • P. Covington, J. Adams and E. Sargin, Deep neural networks for YouTube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198, Boston, Massachusetts, USA, 2016. https://doi.org/http://dx.doi.org/10.1145/2959100.2959190
  • R. Zhou, D. Xia, J. Wan and S. Zhang, An intelligent video tag recommendation method for improving video popularity in mobile computing environment. IEEE Access, 8, 6954–6967, 2020. https://doi.org/10.1109/ACCESS.2019.2961392
  • Q. Liu, R. Xie, L. Chen, S. Liu, K. Tu, P. Cui, B. Zhang and L. Lin, Graph neural network for tag ranking in Tag-enhanced video recommendation. International Conference on Information and Knowledge Management, Proceedings, 1, pp. 2613–2620, Ireland, 2020. https://doi.org/10.1145/3340531.3416021
  • S. Agarwal and A. Sureka, A focused crawler for mining hate and extremism promoting videos on YouTube. HT 2014- Proceedings of the 25th ACM Conference on Hypertext and Social Media, pp. 294–296, Santiago, Chile, 2014. https://doi.org/10.1145/2631775.2631776
  • A. Matamoros-Fernández, Platformed racism: the mediation and circulation of an Australian race-based controversy on Twitter, Facebook and YouTube. Information Communication and Society, 20, 6, 930–946, 2017. https://doi.org/10.1080/1369118X.2017.1293130
  • R. Ottoni, E. Cunha, G. Magno, P. Bernardina, W. Meira Jr. And V. Almeida, Analyzing Right-wing YouTube channels: Hate, violence and discrimination. Proceedings of the 10th ACM Conference on Web Science, pp. 323–332, Amsterdam, Netherlands, 2018. https://doi.org/10.1145/3201064.3201081
  • F. Figueiredo, F. Benevenuto and J. M. Almeida, The tube over time: Characterizing popularity growth of YouTube videos. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 745–754, Hong Kong, China, 2011. https://doi.org/10.1145/1935826.1935925
  • S. V. Chelaru, C. Orellana-Rodriguez and I. S. Altingovde, Can social features help learning to rank YouTube videos? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7651 LNCS, pp. 552–566, 2012. https://doi.org/10.1007/978-3-642-35063-4_40
  • A. Brodersen, S. Scellato and M. Wattenhofer, YouTube around the world: Geographic popularity of videos. WWW’12- Proceedings of the 21st Annual Conference on World Wide Web, pp. 241–250, Lyon France, 2012. https://doi.org/10.1145/2187836.2187870
  • A. Krishna, J. Zambreno and S. Krishnan, Polarity trend analysis of public sentiment on YouTube. The 19th International Conference on Management of Data (COMAD), pp. 125–128, Ahmedabad, India, 2013.
  • W. Hoiles, A. Aprem and V. Krishnamurthy, Engagement and popularity dynamics of YouTube videos and sensitivity to Meta-Data. IEEE Transactions on Knowledge and Data Engineering, 29, 7, 1426–1437, 2017. https://doi.org/10.1109/TKDE.2017.2682858
  • Y. L. Chen and C. L. Chang, Early prediction of the future popularity of uploaded videos. Expert Systems with Applications, 133, 59–74, 2019. https://doi.org/10.1016/j.eswa.2019.05.015
  • G. M. H. C. Gajanayake and T. C. Sandanayake, Trending pattern identification of youtube gaming channels using sentiment analysis. 20th International Conference on Advances in ICT for Emerging Regions, ICTer 2020- Proceedings, ICTer, pp. 149–154, Colombo, Sri Lanka, 2020. https://doi.org/10.1109/ICTer51097.2020.9325476
  • Y. Dokuz, Discovering popular and persistent tags from YouTube trending video big dataset. Mutimedia Tools and Applications, 1-19, 2023. https://link.springer.com/article/10.1007/s11042-023-16019-z
  • R. Sharma, YouTube Trending Video Dataset. https://www.kaggle.com/rsrishav/youtube-trending-video-dataset, Accessed 2022
There are 20 citations in total.

Details

Primary Language Turkish
Subjects Big Data, Data Engineering and Data Science
Journal Section Articles
Authors

Yeşim Dokuz 0000-0001-7202-2899

Project Number MMT2023/1-BAGEP
Early Pub Date October 10, 2023
Publication Date October 15, 2023
Submission Date July 20, 2023
Acceptance Date September 27, 2023
Published in Issue Year 2023 Volume: 12 Issue: 4

Cite

APA Dokuz, Y. (2023). YouTube trend büyük veri kümelerinden ülkeler arası kalıcı etiketlerin keşfi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(4), 1538-1544. https://doi.org/10.28948/ngumuh.1330386
AMA Dokuz Y. YouTube trend büyük veri kümelerinden ülkeler arası kalıcı etiketlerin keşfi. NOHU J. Eng. Sci. October 2023;12(4):1538-1544. doi:10.28948/ngumuh.1330386
Chicago Dokuz, Yeşim. “YouTube Trend büyük Veri kümelerinden ülkeler Arası kalıcı Etiketlerin keşfi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12, no. 4 (October 2023): 1538-44. https://doi.org/10.28948/ngumuh.1330386.
EndNote Dokuz Y (October 1, 2023) YouTube trend büyük veri kümelerinden ülkeler arası kalıcı etiketlerin keşfi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12 4 1538–1544.
IEEE Y. Dokuz, “YouTube trend büyük veri kümelerinden ülkeler arası kalıcı etiketlerin keşfi”, NOHU J. Eng. Sci., vol. 12, no. 4, pp. 1538–1544, 2023, doi: 10.28948/ngumuh.1330386.
ISNAD Dokuz, Yeşim. “YouTube Trend büyük Veri kümelerinden ülkeler Arası kalıcı Etiketlerin keşfi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12/4 (October 2023), 1538-1544. https://doi.org/10.28948/ngumuh.1330386.
JAMA Dokuz Y. YouTube trend büyük veri kümelerinden ülkeler arası kalıcı etiketlerin keşfi. NOHU J. Eng. Sci. 2023;12:1538–1544.
MLA Dokuz, Yeşim. “YouTube Trend büyük Veri kümelerinden ülkeler Arası kalıcı Etiketlerin keşfi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 12, no. 4, 2023, pp. 1538-44, doi:10.28948/ngumuh.1330386.
Vancouver Dokuz Y. YouTube trend büyük veri kümelerinden ülkeler arası kalıcı etiketlerin keşfi. NOHU J. Eng. Sci. 2023;12(4):1538-44.

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