@article{scholars19477,
          volume = {1042},
         journal = {Studies in Computational Intelligence},
           pages = {81--92},
            year = {2023},
           title = {Identifying Cyberspace Users{\^a}?? Tendency in Blog Writing Using Machine Learning{\^A} Algorithms},
            note = {cited By 1; Conference of 3rd International Conference on Engineering Mathematics and Computing, ICEMC 2020 ; Conference Date: 5 February 2020 Through 7 February 2020; Conference Code:284739},
             doi = {10.1007/978-981-19-2300-5{$_6$}},
          author = {AbuSalim, S. W. G. and Mostafa, S. A. and Mustapha, A. and Ibrahim, R. and Wahab, M. H. A.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140780716&doi=10.1007\%2f978-981-19-2300-5\%5f6&partnerID=40&md5=35b1e053f109345542bb63b7f247fcf4},
        abstract = {A blog is a form of direct interactive communication technology, which allows users to interact and communicate with each other through posting comments and sharing links as well. A blog is a platform where a writer or group of writers gives their opinion on a specific topic. Many issues and topics{\^A} that{\^A} are in a certain country being censored and{\^A} controlled by the government from being presented through the mass media. Nevertheless, blogs have the space to provide a wide platform for exchanging{\^A} ideas and opinions on various issues. There is a specific proportion between blog features and bloggers{\^a}?? tendency to social, political, and cultural patterns of different countries and nations that create trends among the bloggers in these countries. In this paper, we use an existing data{\^A} set from previous research, which has 100 records of data, and manipulate the data by applying three machine learning algorithms for implementing classification and{\^A} regression tasks. The algorithms are Decision Tree (c4.5), Linear Regression (LR), and Decision Forest (DF) with a 10-fold cross-validation method for training and testing. The results showed that C4.5 achieves the best overall results of 81 accuracy, 83 precision, and 91 recall, compared with the other two algorithms. {\^A}{\copyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.}
}