IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v67y2021ics0160791x21001652.html
   My bibliography  Save this article

Characterisation and comparative analysis of thematic video portals

Author

Listed:
  • Adib, Saif Ahmed
  • Mahanti, Aniket
  • Naha, Ranesh Kumar

Abstract

This paper provides a comprehensive measurement study on three video streaming websites with social media features - ‘TED Talks’, ‘xHamster’ and ‘XVideos’. We have analysed 2685 TED videos from 2006 to 2018 to characterise the service. For xHamster and XVideos, active measurements were used to collect unique metadata on almost 3405 and 6721 channels from 2012 to 2019 respectively, which were then analysed. Through these characterisations we gained insight into the main players of the websites – viewers, uploaders and website owners. Our analysis involved the studying of video streaming characteristics such as views, number of uploads, ratings, tags etc. By this we aim to give an overview of the services' current state and compare them with other traditional video streaming services. Our results showed some similar trends to be observed in all three websites such as TED videos and adult channels getting a high number of views despite low injection rate, maintaining a power-law behaviour due to front page recommendations and ratings being underutilised as a feature.Other observations include adult streaming services having a higher number of subscribers per channel. The characterisation results obtained are of value to network operators, content providers, and protocol designers. These results can also be used by content providers to measure what type of content is being watched on their websites. Our study provides a glimpse at how video streaming services function today and the trends they seem to follow.

Suggested Citation

  • Adib, Saif Ahmed & Mahanti, Aniket & Naha, Ranesh Kumar, 2021. "Characterisation and comparative analysis of thematic video portals," Technology in Society, Elsevier, vol. 67(C).
    • Handle: RePEc:eee:teinso:v:67:y:2021:i:c:s0160791x21001652
    DOI: 10.1016/j.techsoc.2021.101690
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X21001652
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2021.101690?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Engström, Emma & Strimling, Pontus, 2020. "Deep learning diffusion by infusion into preexisting technologies – Implications for users and society at large," Technology in Society, Elsevier, vol. 63(C).
    2. Liao, Shu-Hsien & Widowati, Retno & Hsieh, Yu-Chieh, 2021. "Investigating online social media users’ behaviors for social commerce recommendations," Technology in Society, Elsevier, vol. 66(C).
    3. Jung, Sang Hoon & Jeong, Yong Jin, 2020. "Twitter data analytical methodology development for prediction of start-up firms’ social media marketing level," Technology in Society, Elsevier, vol. 63(C).
    4. Shwartz-Asher, Daphna & Chun, Soon & Adam, Nabil R. & Snider, Keren LG., 2020. "Knowledge sharing behaviors in social media," Technology in Society, Elsevier, vol. 63(C).
    5. Nagaraj, Samala & Singh, Soumya & Yasa, Venkat Reddy, 2021. "Factors affecting consumers’ willingness to subscribe to over-the-top (OTT) video streaming services in India," Technology in Society, Elsevier, vol. 65(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mengchi Xing & Haojiang Deng & Rui Han, 2024. "A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data," Future Internet, MDPI, vol. 16(3), pages 1-19, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Johnson, Nathan & Turnbull, Benjamin & Reisslein, Martin, 2022. "Social media influence, trust, and conflict: An interview based study of leadership perceptions," Technology in Society, Elsevier, vol. 68(C).
    2. Xing, Yunfei & Wang, Xiwei & Qiu, Chengcheng & Li, Yueqi & He, Wu, 2022. "Research on opinion polarization by big data analytics capabilities in online social networks," Technology in Society, Elsevier, vol. 68(C).
    3. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    4. Yunhwan Kim, 2023. "Exploring Organizational Self-(re)presentations on Visual Social Media: Computational Analysis of Startups’ Instagram Photos Based on Unsupervised Learning," SAGE Open, , vol. 13(4), pages 21582440231, December.
    5. Agarwal, Reeti & Mehrotra, Ankit & Sharma, Veenu & Papa, Armando & Malibari, Areej, 2023. "Over-the-top (OTT) retailing in the post pandemic world. Unveiling consumer drivers and barriers using a qualitative study," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    6. Nguyen, Phan Dinh & Tran, Lobel Trong Thuy & Baker, John, 2021. "Driving university brand value through social media," Technology in Society, Elsevier, vol. 65(C).
    7. Yao Wu & Satish Nambisan & Jinghua Xiao & Kang Xie, 2022. "Consumer resource integration and service innovation in social commerce: the role of social media influencers," Journal of the Academy of Marketing Science, Springer, vol. 50(3), pages 429-459, May.
    8. Renqiang, Xie & Wende, Zhang, 2022. "An empirical study on the impact of platform environmental factors on knowledge sharing in virtual communities," Technology in Society, Elsevier, vol. 71(C).
    9. Park, Subin & Lee, Jongsu & Kim, Junghun, 2024. "Exploring the fittest choice model for consumer preference analysis on over-the-top service," Technology in Society, Elsevier, vol. 76(C).
    10. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    11. Nguyen, Tuyet-Mai & Viet Ngo, Liem & Paramita, Widya, 2022. "Turning lurkers into innovation agents: An interactionist perspective of self-determinant theory," Journal of Business Research, Elsevier, vol. 141(C), pages 822-835.
    12. Sætra, Henrik Skaug, 2023. "Generative AI: Here to stay, but for good?," Technology in Society, Elsevier, vol. 75(C).
    13. Lim, Chulmin & Kim, Seongcheol, 2023. "Why do Korean users intend to subscribe to global OTT service through their local IPTV service?," Telecommunications Policy, Elsevier, vol. 47(4).
    14. Camilleri, Mark Anthony & Kozak, Metin, 2022. "Interactive engagement through travel and tourism social media groups: A social facilitation theory perspective," Technology in Society, Elsevier, vol. 71(C).
    15. Haque, Md Ziaul & Qian, Aimin & Hoque, Md Rakibul & Lucky, Suraiea Akter, 2022. "A unified framework for exploring the determinants of online social networks (OSNs) on institutional investors’ capital market investment decision," Technology in Society, Elsevier, vol. 70(C).
    16. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
    17. Alsaad, Abdallah & Alam, Md. Moddassir & Lutfi, Abdalwali, 2023. "A sensemaking perspective on the association between social media engagement and pro-environment behavioural intention," Technology in Society, Elsevier, vol. 72(C).
    18. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    19. Zhang, Wenyao & Zhang, Wei & Daim, Tugrul U., 2023. "Investigating consumer purchase intention in online social media marketing: A case study of Tiktok," Technology in Society, Elsevier, vol. 74(C).
    20. Ma, Yonghong & Li, Baoxiang, 2022. "Effect of digitalization on knowledge transfer from universities to enterprises: Evidence from postdoctoral workstation of Chinese enterprises," Technology in Society, Elsevier, vol. 71(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:67:y:2021:i:c:s0160791x21001652. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.