DFW-PP: dynamic feature weighting-based popularity prediction for social media content

The increasing popularity of social media platforms makes it important to study user engagement, which is a crucial aspect of any marketing strategy or business model. The oversaturation of image content on social media platforms has persuaded us to identify the important factors that affect the pop...

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Bibliographic Details
Published in:The Journal of supercomputing 2024-03, Vol.80 (4), p.5708-5730
Main Authors: Viswanatha Reddy, G., Chaitanya, B. S. N. V., Prathyush, P., Sumanth, M., Mrinalini, C., Dileep Kumar, P., Mukherjee, Snehasis
Format: Article
Language:English
Subjects:
Compilers
Computer Science
Digital media
Image quality
Interpreters
Machine learning
Processor Architectures
Programming Languages
Social networks
Statistical analysis
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Summary:The increasing popularity of social media platforms makes it important to study user engagement, which is a crucial aspect of any marketing strategy or business model. The oversaturation of image content on social media platforms has persuaded us to identify the important factors that affect the popularity of image content in social media. This comes from the fact that only an iota of the humongous image content available online receives the attention of the target audience. A good quality image having the potential content to make the image popular, can be used in online business, to attract potential buyers. Comprehensive research has been done in the area of popularity prediction using several machine learning techniques. However, we observe that there is still significant scope for improvement in analyzing the social importance of image content. We propose the DFW-PP framework, to learn the importance of different features obtained from the image content, that vary over time. The proposed method extracts features from the image content from social media, provides scores to the image content based on studies of the dynamics of the image content, and draws inferences on the impact of the image on social media. Further, the proposed method controls the skewness of the distribution of the features by applying a log–log normalization. The proposed method is experimented with a benchmark dataset, to show promising results. Our statistical analysis shows a significant influence of a combination of image and meta features on the popularity of the image in social media. The code will be made publicly available at github .
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05672-8