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Video Popularity in Social Media: Impact of Emotions, Raw Features and Viewer Comments (2407.16272v1)

Published 23 Jul 2024 in cs.HC

Abstract: The Internet has significantly affected the increase of social media users. Nowadays, informative content is presented along with entertainment on the web. Highlighting environmental issues on social networks is crucial, given their significance as major global problems. This study examines the popularity determinants for short environmental videos on social media, focusing on the comparative influence of raw video features and viewer engagement metrics. We collected a dataset of videos along with associated popularity metrics such as likes, views, shares, and comments per day. We also extracted video characteristics, including duration, text post length, emotional and sentiment analysis using the VADER and text2emotion models, and color palette brightness. Our analysis consisted of two main experiments: one evaluating the correlation between raw video features and popularity metrics and another assessing the impact of viewer comments and their sentiments and emotions on video popularity. We employed a ridge regression classifier with standard scaling to predict the popularity, categorizing videos as popular or not based on the median views and likes per day. The findings reveal that viewer comments and reactions (accuracy of 0.8) have a more substantial influence on video popularity compared to raw video features (accuracy of 0.67). Significant correlations include a positive relationship between the emotion of sadness in posts and the number of likes and negative correlations between sentiment scores, and both likes and shares. This research highlights the complex relationship between content features and public perception in shaping the popularity of environmental messages on social media.

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References (29)
  1. “State of the Global Climate 2023 — wmo.int,” https://wmo.int/publication-series/state-of-global-climate-2023, [Accessed 21-04-2024].
  2. D. Nazarov and A. Nazarov, “A fuzzy model for assessing the content’s quality impact on the growth of users on the tiktok social network,” in 2021 IEEE 23rd Conference on Business Informatics (CBI), vol. 2.   IEEE, 2021, pp. 192–196.
  3. N. Agarwal, D. Mahata, and H. Liu, “Time-and event-driven modeling of blogger influence.” 2014.
  4. H. U. Khan, A. Daud, U. Ishfaq, T. Amjad, N. Aljohani, R. A. Abbasi, and J. S. Alowibdi, “Modelling to identify influential bloggers in the blogosphere: A survey,” Computers in Human Behavior, vol. 68, pp. 64–82, 2017.
  5. E. Shamoi, A. Turdybay, P. Shamoi, I. Akhmetov, A. Jaxylykova, and A. Pak, “Sentiment analysis of vegan related tweets using mutual information for feature selection,” PeerJ Computer Science, vol. 8, p. e1149, Dec. 2022.
  6. D. Amangeldi, A. Usmanova, and P. Shamoi, “Understanding environmental posts: Sentiment and emotion analysis of social media data,” IEEE Access, vol. 12, pp. 33 504–33 523, 2024.
  7. N. Oryngozha, P. Shamoi, and A. Igali, “Detection and analysis of stress-related posts in reddit’s acamedic communities,” IEEE Access, vol. 12, pp. 14 932–14 948, 2024.
  8. S. Ouyang, C. Li, and X. Li, “A peek into the future: Predicting the popularity of online videos,” IEEE Access, vol. 4, pp. 3026–3033, 2016.
  9. D. Vallet, S. Berkovsky, S. Ardon, A. Mahanti, and M. A. Kafaar, “Characterizing and predicting viral-and-popular video content,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, pp. 1591–1600.
  10. J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Annals of statistics, pp. 1189–1232, 2001.
  11. Z. Tan and Y. Zhang, “Predicting the top-n popular videos via a cross-domain hybrid model,” IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 147–156, 2018.
  12. X. Lin, X. Chen, L. Song, J. Liu, B. Li, and P. Jiang, “Tree based progressive regression model for watch-time prediction in short-video recommendation,” arXiv preprint arXiv:2306.03392, 2023.
  13. J. Chen, D. Liang, Z. Zhu, X. Zhou, Z. Ye, and X. Mo, “Social media popularity prediction based on visual-textual features with xgboost,” in Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 2692–2696.
  14. B. Wu, W.-H. Cheng, Y. Zhang, Q. Huang, J. Li, and T. Mei, “Sequential prediction of social media popularity with deep temporal context networks,” arXiv preprint arXiv:1712.04443, 2017.
  15. K. Ding, R. Wang, and S. Wang, “Social media popularity prediction: A multiple feature fusion approach with deep neural networks,” in Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 2682–2686.
  16. K. Xu, Z. Lin, J. Zhao, P. Shi, W. Deng, and H. Wang, “Multimodal deep learning for social media popularity prediction with attention mechanism,” in Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 4580–4584.
  17. Z. Zhang, X. Xie, M. Yang, Y. Tian, Y. Jiang, and Y. Cui, “Improving social media popularity prediction with multiple post dependencies,” arXiv preprint arXiv:2307.15413, 2023.
  18. N. De Siva and R. Rupasingha, “Classifying youtube videos based on their quality: A comparative study of seven machine learning algorithms,” in 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS).   IEEE, 2023, pp. 251–256.
  19. Jinna Lv, Wu Liu, Meng Zhang, He Gong, Bin Wu, and Huadong Ma, “Multi-feature Fusion for Predicting Social Media Popularity,” ACM Multimedia, 2017.
  20. Alexandros Vrochidis, N. Dimitriou, S. Krinidis, Savvas Panagiotidis, S. Parcharidis, and D. Tzovaras, “Video Popularity Prediction Through Fusing Early Viewership with Video Content,” International Conference on Virtual Storytelling, 2021.
  21. Raj Kumar Gupta and Yinping Yang, “Predicting and Understanding News Social Popularity with Emotional Salience Features,” ACM Multimedia, 2019.
  22. F. Figueiredo, J. Almeida, Marcos André Gonçalves, and Fabrício Benevenuto, “On the Dynamics of Social Media Popularity: A YouTube Case Study,” TOIT, 2014.
  23. B. M. Dalmoro and S. R. Musse, “Predicting Popularity of Facebook Videos Through Visual Features Using Support Vector Machine Classifier,” in Anais do XLVIII Seminário Integrado de Software e Hardware (SEMISH 2021).   Sociedade Brasileira de Computação - SBC, jul 18 2021.
  24. X. Wang, R. E. Mayer, M. Han, and L. Zhang, “Two Emotional Design Features Are More Effective Than One in Multimedia Learning,” Journal of Educational Computing Research, vol. 60, no. 8, pp. 1991–2014, may 29 2022.
  25. “Open source computer vision library,” https://docs.opencv.org/4.x/index.html.
  26. Text2emotion. [Online]. Available: https://pypi.org/project/text2emotion/
  27. S. Na, L. Xumin, and G. Yong, “Research on k-means clustering algorithm: An improved k-means clustering algorithm,” in 2010 Third International Symposium on intelligent information technology and security informatics.   Ieee, 2010, pp. 63–67.
  28. A. R. Weeks and G. E. Hague, “Color segmentation in the hsi color space using the k-means algorithm,” in Nonlinear image processing VIII, vol. 3026.   SPIE, 1997, pp. 143–154.
  29. A. Burambekova and P. Shamoi, “Comparative analysis of color models for human perception and visual color difference,” 2024. [Online]. Available: https://arxiv.org/abs/2406.19520
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Authors (2)
  1. Malika Ziyada (2 papers)
  2. Pakizar Shamoi (24 papers)

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