POPULARITY PREDICTION OF 9GAG POSTS BASED ON IMAGE ANALYSIS
DOI:
https://doi.org/10.24867/11BE15SimicKeywords:
data mining, data analysis, computer vision, machine learning, regression analysisAbstract
This paper presents multiple experiments with machine learning models for predicting the popularity of 9gag posts. The focus is on post popularity prediction based on image analysis. The images were analyzed by extracting three groups of features: (1) a set of objects detected on the image, (2) detection of a popular meme pattern on the image, and (3) length of the textual data contained in an image. The second approach of image analysis was a deep learning end-to-end approach. The presented image analysis models are parts of a broader system that predicts the post popularity by combining various sources of information: image, text, and metadata. The paper discusses multiple approaches to integrate this information.
References
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[2] Kempe, David, Jon Kleinberg, and Éva Tardos. "Maximizing the spread of influence through a social network." Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. 2003.
[3] https://www.crunchbase.com/organization/9gag (posećeno septembra 2020.)
[4] Meghawat, Mayank, et al. "A multimodal approach to predict social media popularity." 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2018.
[5] Mazloom, Masoud, et al. "Multimodal popularity prediction of brand-related social media posts." Proceedings of the 24th ACM international conference on Multimedia. 2016.
[6] Borth, Damian, et al. "Sentibank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content." Proceedings of the 21st ACM international conference on Multimedia. 2013.
[7] Thelwall, Mike, et al. "Sentiment strength detection in short informal text." Journal of the American society for information science and technology 61.12 (2010): 2544-2558.
[8] Hu, Jiani, Toshihiko Yamasaki, and Kiyoharu Aizawa. "Multimodal learning for image popularity prediction on social media." 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE, 2016.
[9] Thomee, Bart, et al. "YFCC100M: The new data in multimedia research." Communications of the ACM 59.2 (2016): 64-73.
[10] Khosla, Aditya, Atish Das Sarma, and Raffay Hamid. "What makes an image popular?." Proceedings of the 23rd international conference on World wide web. 2014.
[11] Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[12] Kuznetsova, Alina, et al. "The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale." arXiv preprint arXiv:1811.00982 (2018).
[13] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
[14] Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database." 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009.
[15] Lee, Kevan. "The proven ideal length of every tweet, Facebook post, and headline online." Fast Company, Apr (2014).
[16]https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-for-ensemble-models/ (posećeno septembra 2020.)
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Published
2020-12-25
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Section
Electrotechnical and Computer Engineering
