I am working on ranking different social influencers based on a set of metrics.
• categories (the niche the influencer is in)
• follower grow, follower_growth_rate
• highlightReelCount, igtvVideoCount, postsCount
• avg_likes, avg_comments
• likes_comments_ratio (comments per 100 likes, use as in authentic indicator)
• authentic_engagement (the number of likes and comments that come from real people）
• 1/post_like, 1/post_comment (total 12 latest posts)
• 1/igtv_likes, 1/igtv_comment (total 12 latest igtvs)
Here's how the data looks like:
Sample_data - https://drive.google.com/file/d/15obMah9pGI3CutOZMJNqfr3O95rLz2JS/view?usp=sharing
Objective: Rank the social influencers according to their influential power with the use of the metrics collected above.
There are a few ranking algorithms to choose from, which are:
a) Compute the score for influential power with Multi-Criteria Decision Making (MCDM) and rank it with regression
b) Create classification model and rank them through probability
c) Compute the score for influential power with Multi-Criteria Decision Making (MCDM) and rank it with machine learning model like SVM, Decision Tree and Deep Neural Network
d) Learning to rank algorithm like CatBoost
e) Trending algorithm
I would like to ask which algorithm above will be more suitable in this project and could you compare and provide the reasons for it? Any ideas will be much appreciated!
External links for algorithms:
3. [Trending algorithm](https://www.evanmiller.org/deriving-the-reddit-formula.html)