Abstract:
The destiny of social media images depends upon their popularity: some of the uploaded
images/videos get a lot of fame among people while others just get completely unnoticed. Why
is this so? This work addresses this question, discusses all the features related to social content
that are responsible for its popularity or negligence and also propose a system to predict the
popularity of the content for the span of 30 days before actually uploading the content on any
social media platform. There are some common features in the social content that gets fame,
in this research work we have evaluated the effect of different features on the popularity score
of the content. The proposed model predicts the popularity score in the form of number of
views for the next 30 days after uploading the content. The content popularity score can be
used by companies to improve their marketing strategies, targeting the right audience
sagaciously, managing the resources efficiently and making the strategical decisions. In
research work, the detailed methodology is discussed to design a model that can perform the
task of Image Popularity Prediction (IPP) efficiently. A critical analysis is also performed on
the results obtained from single features, combinational features and features obtained by
applying different techniques. The best results are achieved by using Linear Discriminant
Analysis (LDA) technique, which converts the higher dimensional features into six
dimensions. This provides 8.03 value for tRMSE and spearman correlation of 0.76. This
research work manifest that the features related to the image context i.e. user features and photo
features etc. outperforms other features related to the content of the image.
