2021/10/16 by Viswanatha Reddy G, G, Viswanatha Reddy, Chaitanya B S N V +11
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Digital Marketing and Social Media #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2110.08510
openalex publication_date 2021/10/16 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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 over-saturation of content on social media platforms has persuaded us to identify the important factors that affect content popularity. This comes from the fact that only an iota of the humongous content available online receives the attention of the target audience. 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 media content. We propose the DFW-PP framework, to learn the importance of different features that vary over time. 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. The code will be made publicly available at https://github.com/chaitnayabasava/DFW-PP.