{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:16:28Z","timestamp":1771611388238,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>In this digital era, people rely on the internet for their news consumption. As people are free to express their opinions on social media, much information shared on the internet is loaded with propaganda. Propagandist contents are intended to influence public opinion. In the mainstream media or prominent news agencies, the authors\u2019 and news agencies\u2019 own bias may impact in the news contents. Hence, it is required to detect such propaganda spread through news articles. Detection and classification of propagandist text require standard, high-quality, annotated datasets. A few datasets are available for propaganda classification. However, these datasets are mostly in English. Hindi is the most spoken language in India, and efforts are needed to detect its propagandist contents. This research work introduces two new datasets: H-Prop and H-Prop-News, which consist of news articles in Hindi annotated as propaganda or non-propaganda. The H-Prop dataset is generated by translating 28,630 news articles from the QProp dataset. The H-Prop-News dataset contains 5500 news articles collected from 32 prominent Hindi news websites. We experiment with the proposed datasets using four supervised machine learning models combined with different feature vectors and word embeddings. Our experiments achieve 87% accuracy using Logistic Regression with TF-IDF feature vectors. The datasets provide high-quality labeled news articles in Hindi and open new avenues for researchers to explore techniques for analyzing and classifying propaganda in Hindi text.<\/jats:p>","DOI":"10.3390\/data7030029","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T20:09:57Z","timestamp":1646078997000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["H-Prop and H-Prop-News: Computational Propaganda Datasets in Hindi"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3632-3832","authenticated-orcid":false,"given":"Deptii","family":"Chaudhari","sequence":"first","affiliation":[{"name":"Department of CS & IT, Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ambika Vishal","family":"Pawar","sequence":"additional","affiliation":[{"name":"Department of CS & IT, Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Barr\u00f3n-Cede\u00f1o","sequence":"additional","affiliation":[{"name":"Department of Interpreting and Translation, Alma Mater Studiorum-Universit\u00e0 di Bologna, Corso della Repubblica 136, 47121 Forl\u00ec, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"ref_1","unstructured":"Ellul, J., Merton, R.K., Kellen, K., and Lerner, J. (1965). Propaganda: The Formation of Men\u2019s Attitudes, Vintage Books."},{"key":"ref_2","first-page":"87","article-title":"Deciphering published articles on cyberterrorism: A latent Dirichlet allocation algorithm application","volume":"11","author":"Caluza","year":"2019","journal-title":"Int. J. Data Min. Model. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alharbi, A.R., and Aljaedi, A. (2019). Predicting rogue content and arabic spammers on twitter. Futur. Internet, 11.","DOI":"10.3390\/fi11110229"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Heidarysafa, M., Kowsari, K., Odukoya, T., Potter, P., Barnes, L.E., and Brown, D.E. (2019). Women in ISIS Propaganda: A Natural Language Processing Analysis of Topics and Emotions in a Comparison with Mainstream Religious Group. Science and Information Conference, Springer.","DOI":"10.1007\/978-3-030-52246-9_45"},{"key":"ref_5","unstructured":"Nizzoli, L., Avvenuti, M., Cresci, S., and Tesconi, M. (July, January 30). Extremist propaganda tweet classification with deep learning in realistic scenarios. Proceedings of the 11th ACM Conference on Web Science, Boston, MA, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ratkiewicz, J., Conover, M., Meiss, M., Gon\u00e7alves, B., Patil, S., Flammini, A., and Menczer, F. (2011, January 28). Truthy: Mapping the spread of astroturf in microblog streams. Proceedings of the 20th International Conference Companion on World Wide Web, Hyderabad, India.","DOI":"10.1145\/1963192.1963301"},{"key":"ref_7","unstructured":"Kellner, A., Rangosch, L., Wressnegger, C., and Rieck, K. (2019). Political Elections Under (Social) Fire? Analysis and Detection of Propaganda on Twitter. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/2158244019827715","article-title":"For Whom the Bot Tolls: A Neural Networks Approach to Measuring Political Orientation of Twitter Bots in Russia","volume":"9","author":"Stukal","year":"2019","journal-title":"Sage Open"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/01292986.2019.1699938","article-title":"Digital propaganda, political bots and polarized politics in India","volume":"30","author":"Neyazi","year":"2020","journal-title":"Asian J. Commun."},{"key":"ref_10","first-page":"57","article-title":"Propaganda analysis in social media: A bibliometric review","volume":"49","author":"Chaudhari","year":"2021","journal-title":"Inf. Discov. Deliv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1016\/j.ipm.2019.03.005","article-title":"Proppy: Organizing the news based on their propagandistic content","volume":"56","author":"Jaradat","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_12","unstructured":"Barr\u00f3n-Cede\u00f1o, A., Da San Martino, G., Jaradat, I., and Nakov, P. (February, January 27). Proppy: A System to Unmask Propaganda in Online News. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_13","unstructured":"(2021, October 13). Watson Language Translator-India|IBM. Available online: https:\/\/www.ibm.com\/in-en\/cloud\/watson-language-translator."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., and Choi, Y. (2017, January 7\u201311). Truth of varying shades: Analyzing language in fake news and political fact-checking. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1317"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Popat, K., Mukherjee, S., Str\u00f6tgen, J., and Weikum, G. (2017, January 3\u20137). Where the truth lies: Explaining the credibility of emerging claims on the web and social media. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.","DOI":"10.1145\/3041021.3055133"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, Y., De Melo, G., and Weikum, G. (2018, January 28\u201331). Five shades of untruth: Finer-grained classification of fake news. Proceedings of the 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain.","DOI":"10.1109\/ASONAM.2018.8508256"},{"key":"ref_17","unstructured":"Qazvinian, V., Rosengren, E., Radev, D.R., and Mei, Q. (2011, January 27\u201331). Rumor has it Identifying Misinformation in Microblogs. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Baly, R., Karadzhov, G., Alexandrov, D., Glass, J., and Nakov, P. (November, January 31). Predicting factuality of reporting and bias of news media sources. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1389"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kwon, S., Cha, M., Jung, K., Chen, W., and Wang, Y. (2013, January 7\u201310). Prominent features of rumor propagation in online social media. Proceedings of the 2013 IEEE 13th International Conference on Data mining, Dallas, TX, USA.","DOI":"10.1109\/ICDM.2013.61"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Saleh, A., Baly, R., Barr\u00f3n-Cede\u00f1o, A., Da San Martino, G., Mohtarami, M., Nakov, P., and Glass, J. (2019, January 6\u20137). Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection. Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, MN, USA.","DOI":"10.18653\/v1\/S19-2182"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yoosuf, S., and Yang, Y. (2019, January 19). Fine-Grained Propaganda Detection with Fine-Tuned BERT. Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, Hong Kong, China.","DOI":"10.18653\/v1\/D19-5011"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Baisa, V., Herman, O., and Hor\u00e1k, A. (2019, January 2\u20134). Benchmark dataset for propaganda detection in Czech newspaper texts. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), Varna, Bulgaria.","DOI":"10.26615\/978-954-452-056-4_010"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"da San Martino, G., Yu, S., Barr\u00f3n-Cede\u00f1o, A., Petrov, R., and Nakov, P. (2019, January 3\u20137). Fine-grained analysis of propaganda in news articles. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China.","DOI":"10.18653\/v1\/D19-1565"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Da San Martino, G., Barr\u00f3n-Cede\u00f1o, A., Wachsmuth, H., and Petrov, P. (2020, January 12\u201313). SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles. Proceedings of the Fourteenth Workshop on Semantic Evaluation, Barcelona, Spain.","DOI":"10.18653\/v1\/2020.semeval-1.186"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Perry, T. (2021, January 7\u201311). LightTag: Text Annotation Platform. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing System demonstration, Santo Domingo, Dominican Republic.","DOI":"10.18653\/v1\/2021.emnlp-demo.3"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_27","unstructured":"Weston, A. (2018). A Rulebook for Arguments, Hackett Publishing. [5th ed.]."},{"key":"ref_28","first-page":"27","article-title":"The Techniques of Propaganda","volume":"10","author":"Miller","year":"1939","journal-title":"How Detect. Anal. Propag."},{"key":"ref_29","unstructured":"Torok, R. (December, January 30). Symbiotic radicalisation strategies: Propaganda tools and neuro linguistic programming. Proceedings of the 8th Australian Security and Intelligence Conference, Joondalup, Australia."},{"key":"ref_30","unstructured":"Jowett, G.S., and O\u2019Donnell, V. (2006). What Is Propaganda, and How Does It Differ From Persuasion?. Propaganda and Persuasion, Sage Publications. [4th ed.]."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"56","DOI":"10.23860\/jmle-6-2-5","article-title":"Teaching about Propaganda: An Examination of the Historical Roots of Media Literacy","volume":"6","author":"Hobbs","year":"2014","journal-title":"J. Media Lit. Educ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10503-011-9219-6","article-title":"Accounting for the force of the appeal to authority","volume":"25","author":"Goodwin","year":"2011","journal-title":"Argumentation"},{"key":"ref_33","unstructured":"Hunter, J. (2015). Brainwashing in a Large Group Awareness Training?: The Classical Conditioning Hypothesis of Brainwashing. [Ph.D. Thesis, Psychology University of Kwazu-Natal]."},{"key":"ref_34","first-page":"53","article-title":"The Kremlin\u2019s Platform for \u2018Useful Idiots\u2019 in the West: An Overview of RT\u2019s Editorial Strategy and Evidence of Impact","volume":"31","author":"Richter","year":"2017","journal-title":"Eur. 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