The number of observations in which the actual output is false and the output predicted by algorithm is true.
Published in Chapter:
Analytics of User Behaviors on Twitter Using Machine Learning
Noman Islam (Karachi Institute of Economics and Technology, Pakistan), Muntaha Mehboob (NED University, Pakistan), and Rimsha Javed (Mohammad Ali Jinnah University, Pakistan)
Copyright: © 2023
|Pages: 36
DOI: 10.4018/978-1-6684-6242-3.ch014
Abstract
Twitter is a leading social networking site when it comes down to topics such as politics, news, and trends around the globe. Another main reason for people to use Twitter is because they are able to share their emotions and feelings with others and form new relationships and views. With about 330 million users on Twitter (in 2020), it continues to rapidly grow, but at the same time, it is also losing users at a fast pace. In 2019, Twitter had 340 million users, but a year later, it lost 10 million of them. The goal of this chapter is to find the reasons of three questions. The first, to find the reason behind Twitter losing its users. The second, to see how a user changes behavior after usage of Twitter, and third, how a user's behavior changes when expanding his/her social circle on Twitter. For all of these questions, this chapter has designed a data set and executed experiments based on the authors' hypotheses. The results report the accuracies of each of these hypotheses.