Abstract
Instagram is photo and video sharing platform having a huge influence on the reach of a person or a brand worldwide. Popularity prediction is advantageous for influencers as well as organisations that make revenue through advertising or product placements. The main aim of this study is to find out the relationship between the popularity of an Instagram post with the image content posted and it’s metadata like time, day, number of hashtags, number of comments, etc. This research was conducted using data scraped from various active Instagram accounts and applying regression models to gather relevant metrics to predict the likelihood of the user’s posts being well received. Two regression techniques were tested. The Linear Regression model successfully predicted the number of likes with an MSE of 953.76, whereas the XGB Regression model had a MSE of 2876.17. Rather than viewing just the follower count for the prediction, the post’s metadata was also a major contributor.
Citations
IRE Journals:
Ravi Gusain , Saksham Pathak
"Instagram User Popularity Predictor" Iconic Research And Engineering Journals Volume 6 Issue 8 2023 Page 22-26
IEEE:
Ravi Gusain , Saksham Pathak
"Instagram User Popularity Predictor" Iconic Research And Engineering Journals, 6(8)
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