Detecting Textual Propaganda Using Machine Learning Techniques
Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising...
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| Format: | Article |
| Language: | English |
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University of Baghdad, College of Science for Women
2021-03-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5251 |
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| author | Akib Mohi Ud Din Khanday Qamar Rayees Khan Syed Tanzeel Rabani |
| author_facet | Akib Mohi Ud Din Khanday Qamar Rayees Khan Syed Tanzeel Rabani |
| author_sort | Akib Mohi Ud Din Khanday |
| collection | DOAJ |
| description | Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation. Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annotating the text, feature engineering is performed using techniques like term frequency/inverse document frequency (TF/IDF) and Bag of words (BOW). The relevant features are supplied to support vector machine (SVM) and Multinomial Naïve Bayesian (MNB) classifiers. The fine tuning of SVM is being done by taking kernel Linear, Poly and RBF. SVM showed better results than MNB by having precision of 70%, recall of 76.5%, F1 Score of 69.5% and overall Accuracy of 69.2%. |
| format | Article |
| id | doaj-art-16bd43b934ad471fa5ed1a09c11e68d2 |
| institution | Matheson Library |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-16bd43b934ad471fa5ed1a09c11e68d22025-08-02T02:11:54ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862021-03-0118110.21123/bsj.2021.18.1.0199Detecting Textual Propaganda Using Machine Learning TechniquesAkib Mohi Ud Din Khanday0Qamar Rayees Khan1Syed Tanzeel Rabani2Baba Ghulam Shah Badshah University, Rajouri, J&KBaba Ghulam Shah Badshah UniversityBaba Ghulam Shah Badshah UniversitySocial Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation. Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annotating the text, feature engineering is performed using techniques like term frequency/inverse document frequency (TF/IDF) and Bag of words (BOW). The relevant features are supplied to support vector machine (SVM) and Multinomial Naïve Bayesian (MNB) classifiers. The fine tuning of SVM is being done by taking kernel Linear, Poly and RBF. SVM showed better results than MNB by having precision of 70%, recall of 76.5%, F1 Score of 69.5% and overall Accuracy of 69.2%.https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5251Social Networks, Disinformation, Propaganda, Term Frequency, Bag of Words. |
| spellingShingle | Akib Mohi Ud Din Khanday Qamar Rayees Khan Syed Tanzeel Rabani Detecting Textual Propaganda Using Machine Learning Techniques مجلة بغداد للعلوم Social Networks, Disinformation, Propaganda, Term Frequency, Bag of Words. |
| title | Detecting Textual Propaganda Using Machine Learning Techniques |
| title_full | Detecting Textual Propaganda Using Machine Learning Techniques |
| title_fullStr | Detecting Textual Propaganda Using Machine Learning Techniques |
| title_full_unstemmed | Detecting Textual Propaganda Using Machine Learning Techniques |
| title_short | Detecting Textual Propaganda Using Machine Learning Techniques |
| title_sort | detecting textual propaganda using machine learning techniques |
| topic | Social Networks, Disinformation, Propaganda, Term Frequency, Bag of Words. |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5251 |
| work_keys_str_mv | AT akibmohiuddinkhanday detectingtextualpropagandausingmachinelearningtechniques AT qamarrayeeskhan detectingtextualpropagandausingmachinelearningtechniques AT syedtanzeelrabani detectingtextualpropagandausingmachinelearningtechniques |