An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random Forest
Bloggers play a role in individual decision making of users in online social networking platforms. Their capability of addressing a wide audience gives them influence over their audience, which companies seek to exploit. Identification of influential bloggers can be seen as a machine learning (ML) t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8752220/ |
| _version_ | 1818855731270516736 |
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| author | Yousra Asim Basit Raza Ahmad Kamran Malik Ahmad R. Shahaid Hani Alquhayz |
| author_facet | Yousra Asim Basit Raza Ahmad Kamran Malik Ahmad R. Shahaid Hani Alquhayz |
| author_sort | Yousra Asim |
| collection | DOAJ |
| description | Bloggers play a role in individual decision making of users in online social networking platforms. Their capability of addressing a wide audience gives them influence over their audience, which companies seek to exploit. Identification of influential bloggers can be seen as a machine learning (ML) task and different ML techniques can help in classifying the professional blogger. In this paper, we propose a predictive and adaptive model named as Influential Blogger based Case-Based Reasoning (IB-CBR) model for the recognition of unseen influential bloggers. It incorporates self-prediction and self-adaptation (self-management) capabilities which are the essence of an automated system. The integration of Random Forest is found contributing to the efficiency of the IB-CBR model as compared to Nearest-Neighbor, and Artificial Neural Network. The performance of the proposed IB-CBR model is evaluated against other ML techniques by using standard performance measures on a standard blogger's dataset. It is observed that our proposed model exhibits 88-95% Accuracy and 94-97% True Positive Rate in the prediction and adaptation of professional bloggers, respectively, in three iterations of the proposed model. What's more, the IB-CBR model achieved 91-96% (increasing) F-measure, 91-98% (increasing) ROC AUC, and 36-11% (decreasing) False Positive Rate due to adaptivity. The IB-CBR model performed well when it is compared with other ML techniques using different standard datasets. |
| first_indexed | 2024-12-19T08:13:16Z |
| format | Article |
| id | doaj.art-9bb0116e39924b69b5f3053d60bcf976 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:13:16Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9bb0116e39924b69b5f3053d60bcf9762022-12-21T20:29:35ZengIEEEIEEE Access2169-35362019-01-017877328774910.1109/ACCESS.2019.29259058752220An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random ForestYousra Asim0Basit Raza1https://orcid.org/0000-0001-6711-2363Ahmad Kamran Malik2https://orcid.org/0000-0003-1521-6579Ahmad R. Shahaid3Hani Alquhayz4https://orcid.org/0000-0001-8445-7742Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad (CUI), Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad (CUI), Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad (CUI), Islamabad, PakistanDepartment of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, Saudi ArabiaBloggers play a role in individual decision making of users in online social networking platforms. Their capability of addressing a wide audience gives them influence over their audience, which companies seek to exploit. Identification of influential bloggers can be seen as a machine learning (ML) task and different ML techniques can help in classifying the professional blogger. In this paper, we propose a predictive and adaptive model named as Influential Blogger based Case-Based Reasoning (IB-CBR) model for the recognition of unseen influential bloggers. It incorporates self-prediction and self-adaptation (self-management) capabilities which are the essence of an automated system. The integration of Random Forest is found contributing to the efficiency of the IB-CBR model as compared to Nearest-Neighbor, and Artificial Neural Network. The performance of the proposed IB-CBR model is evaluated against other ML techniques by using standard performance measures on a standard blogger's dataset. It is observed that our proposed model exhibits 88-95% Accuracy and 94-97% True Positive Rate in the prediction and adaptation of professional bloggers, respectively, in three iterations of the proposed model. What's more, the IB-CBR model achieved 91-96% (increasing) F-measure, 91-98% (increasing) ROC AUC, and 36-11% (decreasing) False Positive Rate due to adaptivity. The IB-CBR model performed well when it is compared with other ML techniques using different standard datasets.https://ieeexplore.ieee.org/document/8752220/Bloggingblogger classificationcase based reasoning (CBR)machine learning |
| spellingShingle | Yousra Asim Basit Raza Ahmad Kamran Malik Ahmad R. Shahaid Hani Alquhayz An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random Forest IEEE Access Blogging blogger classification case based reasoning (CBR) machine learning |
| title | An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random Forest |
| title_full | An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random Forest |
| title_fullStr | An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random Forest |
| title_full_unstemmed | An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random Forest |
| title_short | An Adaptive Model for Identification of Influential Bloggers Based on Case-Based Reasoning Using Random Forest |
| title_sort | adaptive model for identification of influential bloggers based on case based reasoning using random forest |
| topic | Blogging blogger classification case based reasoning (CBR) machine learning |
| url | https://ieeexplore.ieee.org/document/8752220/ |
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