Data Profiling and Machine Learning to Identify Influencers from Social Media Platforms

Abstract

Because of the numerous applications domains in which social media networks can be used, the huge volume of data and information uploaded by them is gaining significant interest. Publishing allows consumers to express their thoughts on products and services. Some feedbacks could also influence other users on those things. Therefore, extracting and identifying influencers from social media networks, also profiling their product perceptions and preferences, is critical for marketers to use efficient viral marketing and recommendation strategies. Our major goal in this research is to find the best machine learning model for characterizing influencers on social media networks. However, to achieve this objective, our strategy revolves around applying the PageRank algorithm to profile influential nodes throughout the social media network graph. The results of our experiment showed that the correlation is always different when adding a new parameter to machine learning models, also to determine the suitable model for our needs. In any event, the experiment outcomes are critical and significant to profiling influencers from social media platforms.