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Detecting Textual Propaganda Using Machine Learning Techniques
- Source :
- Baghdad Science Journal, Vol 18, Iss 1 (2021)
- Publication Year :
- 2021
- Publisher :
- College of Science for Women, University of Baghdad, 2021.
-
Abstract
- 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%.
- Subjects :
- Social Networks, Disinformation, Propaganda, Term Frequency, Bag of Words
General Computer Science
General Mathematics
General Physics and Astronomy
02 engineering and technology
General Chemistry
Agricultural and Biological Sciences (miscellaneous)
General Biochemistry, Genetics and Molecular Biology
ComputingMethodologies_PATTERNRECOGNITION
020204 information systems
Botany
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
lcsh:Q
lcsh:Science
Mathematics
Subjects
Details
- Language :
- Arabic
- ISSN :
- 24117986 and 20788665
- Volume :
- 18
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Baghdad Science Journal
- Accession number :
- edsair.doi.dedup.....a489a77889da6870edb3d61b66a10c30