RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914762571382784 |
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| author | Ramezani, Majid Mohammadshahi, Hamed Daliry, Mahshid Rahmani, Soroor Asghari, Amir-Hosein |
| author_facet | Ramezani, Majid Mohammadshahi, Hamed Daliry, Mahshid Rahmani, Soroor Asghari, Amir-Hosein |
| contents | In the era of information proliferation, discerning the credibility of news content poses an ever-growing challenge. This paper introduces RELIANCE, a pioneering ensemble learning system designed for robust information and fake news credibility evaluation. Comprising five diverse base models, including Support Vector Machine (SVM), naive Bayes, logistic regression, random forest, and Bidirectional Long Short Term Memory Networks (BiLSTMs), RELIANCE employs an innovative approach to integrate their strengths, harnessing the collective intelligence of the ensemble for enhanced accuracy. Experiments demonstrate the superiority of RELIANCE over individual models, indicating its efficacy in distinguishing between credible and non-credible information sources. RELIANCE, also surpasses baseline models in information and news credibility assessment, establishing itself as an effective solution for evaluating the reliability of information sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10940 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation Ramezani, Majid Mohammadshahi, Hamed Daliry, Mahshid Rahmani, Soroor Asghari, Amir-Hosein Information Retrieval Computation and Language Machine Learning Social and Information Networks In the era of information proliferation, discerning the credibility of news content poses an ever-growing challenge. This paper introduces RELIANCE, a pioneering ensemble learning system designed for robust information and fake news credibility evaluation. Comprising five diverse base models, including Support Vector Machine (SVM), naive Bayes, logistic regression, random forest, and Bidirectional Long Short Term Memory Networks (BiLSTMs), RELIANCE employs an innovative approach to integrate their strengths, harnessing the collective intelligence of the ensemble for enhanced accuracy. Experiments demonstrate the superiority of RELIANCE over individual models, indicating its efficacy in distinguishing between credible and non-credible information sources. RELIANCE, also surpasses baseline models in information and news credibility assessment, establishing itself as an effective solution for evaluating the reliability of information sources. |
| title | RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation |
| topic | Information Retrieval Computation and Language Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2401.10940 |