Abstract
Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.
Highlights
Social media has evolved very dynamically and has become a primary source of feedback, trends, debate, and sentiments across various domains (Singh et al, 2020; Grover et al, 2019)
Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics
Such users are known as influencers. This social impact could be observed in all the business segments, online advertising and promotions have been considered as an important aspect to maintain a good brand reputation on social media platforms (Vernier et al, 2018)
Summary
Social media has evolved very dynamically and has become a primary source of feedback, trends, debate, and sentiments across various domains (Singh et al, 2020; Grover et al, 2019). They collected the tweets of particular trends and label it with an appropriate category and utilized it for model training after preprocessing of the tweets and later on these models are predicting the categories of the given tweets This approach is using the Support Vector Machine (SVM) for the predictions and classifies the topics of interest of a user on Twitter. Another research has designed a real-time system for Twitter user profiling based on a supervised machine learning approach to categorize Twitter users into various interest categories like Politics, Entertainment, Entrepreneurship, Journalism, Science & Technology, and Healthcare (Raghuram et al, 2016) based on Tweet-based, User-based and Timeseries based features They utilized numerous classifiers like Support Vector Machines, Naive-Bayes, k-Nearest Neighbours, Decision Tree, and Logistic Regression, and obtained up to 89.82% accuracy in classification. A research investigated the user behavior in an e-commerce site for predicting the buying intention of users with the help of deep belief networks and stacked denoising auto-encoders and concluded that feature extraction from high-dimensional data achieves better predictions (Vieira, A., 2016)
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