Truth Detection in Social Media Posts using Jaccard Algorithm with SRTD and Word Net Concept

• Autorzy: Sangwan Priyanka , Behl Rachna

Identyfikatory

DOI

10.15439/2020KM24

• Konferencja: International Conference on Research in Management & Technovation (05-06.12.2020 ; Nagpur, Indie)
• Języki publikacji: EN

Abstrakty

EN

Counterfeit news has gotten an essential subject of exploration in an assortment of solicitations including semantics and programming building. In this work, clarification of how the issue is drawn nearer from the point of view of fundamental language managing, with the objective of building a framework to subsequently see misdirection in news. The rule challenge in this line of examination is gathering quality information, i.e., occasions of phony and true reports on a sensible dispersing of subjects. In this paper, a novel truth acknowledgment system with near words thoughts is added to the versatile and overwhelming truth disclosure structure used previously. By the use of practically identical words thoughts, the controlled fake news can be recognized with much basic and snappier. The features add up same meaning words which are compared using Jaccard algorithm in the main algorithm to detect a greater number of fake news with reliability score. The reliability score is calculated by combining independent score, attitude score and uncertainty score. The implemented software is found to be having better accuracy and results compared to existing truth detection methods.

Słowa kluczowe

EN

truth; big data; SRTD; Scalable and Robust Truth Discovery; Jaccard algorithm; data mining

PL

prawda; big data; SRTD; algorytm Jaccarda; eksploracja danych

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 24
• Strony: 103--107
• Opis fizyczny: Bibliogr. 16 poz., rys., wykr.

Twórcy

autor

Sangwan Priyanka

• MRIIRS Faridabad, Haryana, India

autor

Behl Rachna

• MRIIRS Faridabad, Haryana, India

Bibliografia

• 1. Zhang, Daniel Yue & Wang, Dong & Vance, Nathan & Zhang, Yang & Mike, Steven. (2018). On Scalable and Robust Truth Discovery in Big Data Social Media Sensing Applications. IEEE Transactions on Big Data. PP. 1-1. 10.1109/TBDATA.2018.2824812.
• 2. Zhang, Daniel Yue & Han, Rungang & Wang, Dong & Huang, Chao. (2016). On robust truth discovery in sparse social media sensing. 1076-1081. 10.1109/BigData.2016.7840710.
• 3. M. Nigade, M. Raut, P. Mane, S. Phadatare, “Truth Discovery in Big Data Social Media Application” Page 40-44 © Journal of Data Mining and Knowledge Engineering 2019
• 4. Shihang Wang, Zongmin Li, Yuhong Wangand Qi Zhang, “ Machine Learning Methods to Predict Social Media Disaster Rumor Refuters”, Int. J. Environ. Res. Public Health 2019, 16, 1452; http://dx.doi.org/10.3390/ijerph16081452
• 5. Mohammed A-Sarem, Wadii Boulila, Muna Al-Harby, Junaid Qadir, and Abdullah Alsaeedi, “Deep Learning Based Rumor Detection on Microblogging Platforms: A Systematic Review”, IEEE, 2019
• 6. Cao, Juan & Guo, Junbo & Li, Xirong & Jin, Zhiwei & Guo, Han & Li, Jintao. (2018). Automatic Rumor Detection on Microblogs: A Survey.
• 7. Stefan Stieglitza,⁎, Milad Mirbabaiea, Björn Rossa, Christoph Neubergerb, “Social media analytics – Challenges in topic discovery, data collection, and data preparation”, International Journal of Information Management, 2018
• 8. Kai Shuy, Amy Slivaz, Suhang Wangy, Jiliang Tang, and Huan Liuy” Fake News Detection on Social Media: A Data Mining Perspective”, SIGKDD Explorations Volume 19, Issue 1
• 9. Carlos Argueta, Yi-Shin Chen, “Multi-Lingual Sentiment Analysis of Social Data Based on Emotion-Bearing Patterns”, Proceedings of the Second Workshop on Natural Language Processing for Social Media (Social NLP), pages 38–43,Dublin, Ireland, August 24 2014
• 10. Trisha Dowerah Baruah, “Effectiveness of Social Media as a tool of communication and its potential for technology enabled connections: A micro-level study”, International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012 1 ISSN 2250-3153
• 11. N. Baggyalakshmi, Dr. A. Kavitha, Dr. A. Marimuthu, “Microblogging in Social Networks - A Survey”, International Journal of Advanced Research in Computer and Communication Engineering, ISO 3297:2007
• 12. Certified Vol. 6, Issue 7, July 2017
• 13. Jiawei Zhang1, Bowen Dong2, Philip S. Yu, “FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network”, arxiv, 2018
• 14. Shuo Yang,yz Kai Shu,z SuhangWang,x Renjie Gu,y Fan Wu,y Huan Liuz “Unsupervised Fake News Detectionon Social Media: A Generative Approach”, The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
• 15. Conroy, Nadia & Rubin, Victoria & Chen, Yimin. (2015). Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology. 52. 1- 10.1002/pra2.2015.145052010082.
• 16. Zhou, Xinyi & Zafarani, Reza. (2018). Fake News: A Survey of Research, Detection Methods, and Opportunities.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).