Detecting Textual Propaganda Using Machine Learning Techniques

Main Article Content

Akib Mohi Ud Din Khanday
Qamar Rayees Khan
Syed Tanzeel Rabani

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%.

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Khanday AMUD, Khan QR, Rabani ST. Detecting Textual Propaganda Using Machine Learning Techniques. Baghdad Sci.J [Internet]. [cited 2021Jan.20];18(1):0199. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5251
Section
article

References

Tables in the manuscript areours. Besides, the Figures and images, which are not ours, have been given the permission for re-publication attached with the manuscript.

- Ethical Clearance: The project was approved by the local ethical committee in Baba Ghulam Shah Badshah University.

References:

Khanday AMUD, Amin A, Manzoor I, Bashir R. Face Recognition Techniques: A Critical Review. STM Journals [Internet]. 2018;5(2):24–30. Available from: https://www.researchgate.net/publication/330872439_Face_Recognition_Techniques_A_Critical_Review

O'Neil C, Schutt R. Doing data science: Straight talk from the frontline. " O'Reilly Media, Inc."; 2013 Oct 9.

J Han J, Kambel M. Data Mining: Concepts and Techniques, Mor Third. ELSEVIER; 2012. 14–15 p.

Ravindran D. Fog computing resource optimization: a review on current scenarios and resource management. Baghdad Sci J. 2019;16(2):419–27.

Kietzmann JH, Hermkens K, McCarthy IP, Silvestre BS. Social media? Get serious! Understanding the functional building blocks of social media. Bus Horiz. 2011;54(3):241–51.

AL-Jumaili AS. A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis. Baghdad Sci J. 2020;17(1(Suppl.)):0385.

Verma P, Khanday AMUD, Rabani ST, Mir MH, Jamwal S. Twitter sentiment analysis on Indian government project using R. Int J Recent Technol Eng. 2019;8(3):8338–41.

World Economic Forum. The Global Risks Report 2017 12th Edition. Glob Compet Risks Team [Internet]. 2017;103. Available from: http://www3.weforum.org/docs/GRR/WEF_GRR16.pdf

Gupta A, Lamba H, Kumaraguru P. $1.00 per rt# bostonmarathon# prayforboston: Analyzing fake content on twitter. In2013 APWG eCrime researchers summit 2013 Sep 17 (pp. 1-12). IEEE.

Arts THE, Policy C, Justice C, Security N, Safety P, Security H. Rand_Mg877 [Internet]. Available from: papers2://publication/uuid/EEFC2DA5-FA25-4EA1-A3F2-674BEC499C03

Cybenko G, Giani A, Thompson P. Cognitive hacking: A battle for the mind. Computer (Long Beach Calif). 2002;35(8):50–6.

Kumar KPK, Srivastava A, Geethakumari G. A psychometric analysis of information propagation in online social networks using latent trait theory. Computing. 2016;98(6):583–607.

Howard PN, Kollanyi B. Bots,# StrongerIn, and# Brexit: computational propaganda during the UK-EU referendum. Available at SSRN 2798311. 2016 Jun 20.

Varol O, Ferrara E, Menczer F, Flammini A. Early detection of promoted campaigns on social media. EPJ Data Sci [Internet]. 2017;6(1). Available from: http://dx.doi.org/10.1140/epjds/s13688-017-0111-y

Introduction C, Data M, Various CI, Spring A, Street OW, Presidential S, et al. First Monday , Volume 21 , Number 11 - 7 November 2016 Electronic copy available at : https://ssrn.com/abstract=2982233. 2017;21(11):1–14.

Badawy A, Ferrara E. The rise of jihadist propaganda on social networks. Journal of Computational Social Science. 2018 Sep 1;1(2):453-70.

Khanday AM, Rabani ST, Khan QR, Rouf N, Din MM. Machine learning based approaches for detecting COVID-19 using clinical text data. International Journal of Information Technology. 2020 Sep;12(3):731-9. Available from: https://doi.org/10.1007/s41870-020-00495-9

Gao H, Hu J, Zhao BY, Barbara S, Barbara UCS, Chen Y. Detecting and Characterizing Social Spam Campaigns Categories and Subject Descriptors. Proc Internet Meas Conf (IMC. 2010;

Ratkiewicz J, Conover MD, Meiss M, Gonçalves B, Flammini A, Menczer FM. Detecting and tracking political abuse in social media. InFifth international AAAI conference on weblogs and social media. 2011 Jul 5;297–304.

Halu A, Zhao K, Baronchelli A, Bianconi G. Connect and win: The role of social networks in political elections. Epl. 2013;102(1).

Ramakrishnan N, Tech V, Chen F, Arredondo J, Mares D, Summers K. Analyzing Civil Unrest through Social Media. Computer . 2013;80–4.

Liu JS, Ning KC, Chuang WC. Discovering and characterizing political elite cliques with evolutionary community detection. Soc Netw Anal Min. 2013;3(3):761–83.

Bhat SY, Abulaish M. Communities A gainst Deception in Online Social Networks 1 The Platform 2 The Mischef. IEEE. 2014;2014(2):8–16.

Wong FMF, Tan CW, Sen S, Chiang M. Quantifying political leaning from tweets, retweets, and retweeters. IEEE Trans Knowl Data Eng. 2016;28(8):2158–72.

Zarrinkalam F, Bagheri E. Event identification in social networks. Encycl with Semant Comput Robot Intell. 2017;01(01):1630002. [Internet]Available from: http://www.worldscientific.com/doi/abs/10.1142/S2425038416300020

Lightfoot S. Political Propaganda Spread Through Social Bots. Media, Culture, & Global Politics. 2018;(March):0–22.

Ashcroft M, Fisher A, Kaati L, Omer E, Prucha N. Detecting Jihadist Messages on Twitter. Proc - 2015 Eur Intell Secur Informatics Conf EISIC. 2015. 2016;161–4.

Kumar KK, Geethakumari G. Detecting misinformation in online social networks using cognitive psychology. Human-centric Comput Inf Sci. 2014;4(1):1–22.

Mazzoleni G, Bracciale R. Socially Mediated Populism: The Communicative Strategies of Political Leaders on Facebook. Ssrn [Internet]. 2018. Available from: http://dx.doi.org/10.1057/s41599-018-0104-x

Khanday AM, Khan QR, Rabani ST. Identifying propaganda from online social networks during COVID-19 using machine learning techniques. International Journal of Information Technology. 2020 Oct 29:1-8.