In today's world, where digital information spreads swiftly, the rise of fake news poses a threat to society. This research paper presents a method to address this issue by employing data analytics. To address the issues of misinformation, public manipulation, and threats to democracy we propose a framework. Our strategy incorporates advanced methodologies, including natural language processing, machine learning, and network analysis. Using these techniques, we examine various data sources such as media posts, news articles, and user-generated content. By identifying patterns and characteristics associated with the spread of news, our goal is to develop algorithms for automatic detection and classification. Through experimentation and validation, our research showcases precision and recall rates in identifying false information. Additionally, we delve into the implications involved in deploying technology while emphasizing transparency and accountability. This paper contributes to the discussion on combating fake news by providing a practical framework that utilizes big data analytics to safeguard the integrity of digital information. Our findings hold value for policymakers, media organizations, and tech companies as they grapple with this societal challenge.
Keywords:Natural language processing, machine learning, network analysis, precision, fake news

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Pennycook G, Rand DG. Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc Natl Acad Sci USA. 2019; 116 (7): 2521–2526.
2. Kennedy H, Elgesem D, Miguel C. On fairness: User perspectives on social media data mining. Convergence. 2017; 23(3): 270-288.
3. Ruchansky N, Seo S, Liu Y. CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, November 6–10, 2017. 797–806.
4. Gupta A, Kumaraguru P, Castillo C, Meier P. Tweetcred: real-time credibility assessment of content on twitter. In: Social Informatics: 6th International Conference, SocInfo 2014, Barcelona, Spain, November 11–13, 2014. Cham, Switzerland: Springer International Publishing; 2014. 228–243.
5. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newslett. 2017; 19 (1): 22–36.
6. Conroy NK, Rubin VL, Chen Y. Automatic deception detection: methods for finding fake news. Proc Assoc Inform Sci Technol. 2015; 52 (1): 1–4.
7. Horne BD, Adali S. This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: Proceedings of the International AAAI Conference on Web and Social Media, May 3, 2017. Volume 11. 759–766.
8. Mendoza M, Poblete B, Castillo C. Twitter under crisis: Can we trust what we RT? In: Proceedings of the First Workshop on Social Media Analytics, Washington, DC, USA, July 25, 2010. 71–79.
9. Zannettou S, Caulfield T, Setzer W, Sirivianos M, Stringhini G, Blackburn J. Who let the trolls out? Towards understanding state-sponsored trolls. In: Proceedings of the 10th ACM Conference on Web Science, Boston, MA, USA, June 30–July 3, 2019. pp. 353–362.
10. Friggeri A, Adamic L, Eckles D, Cheng J. Rumor cascades. In: Proceedings of the International AAAI Conference on Web and Social Media, May 16, 2014. Volume 8. 101–110.
11. Marberg E, Pawlowski B. On some properties of symplectic Grothendieck polynomials. J Pure Appl 2021; 225 (1): 106463.
12. Ma J, Gao W, Wong KF. Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, British Columbia, Canada, 2017. Association for Computational Linguistics. 708–717.
13. Waladi W, Partek B, Manoosh J. Regulating ammonia concentration in swine housing: Part II. Application examples. Trans 1999; 43 (4): 540–547.
14. Griffin Jr A Cotton Ginners' Handbook. Agricultural Handbook No. 503. Washington, DC, USA: US Department of Agriculture; 1997.
15. Wegmuller M, Von Der Weid JP, Oberson P, Gisin N. High resolution fiber distributed measurements with coherent OFDR. In: Proceedings of the ECOC’00, September 2000. Volume 11, No. 4, p. 109.
16. Clancey WJ. Transfer of Rule-Based Expertise Through a Tutorial Dialogue. PhD Dissertation. Stanford, CA, USA: Stanford University; 1979.

In today's world, where digital information spreads swiftly, the rise of fake news poses a threat to society. This research paper presents a method to address this issue by employing data analytics. To address the issues of misinformation, public manipulation, and threats to democracy we propose a framework. Our strategy incorporates advanced methodologies, including natural language processing, machine learning, and network analysis. Using these techniques, we examine various data sources such as media posts, news articles, and user-generated content. By identifying patterns and characteristics associated with the spread of news, our goal is to develop algorithms for automatic detection and classification. Through experimentation and validation, our research showcases precision and recall rates in identifying false information. Additionally, we delve into the implications involved in deploying technology while emphasizing transparency and accountability. This paper contributes to the discussion on combating fake news by providing a practical framework that utilizes big data analytics to safeguard the integrity of digital information. Our findings hold value for policymakers, media organizations, and tech companies as they grapple with this societal challenge.
Keywords:Natural language processing, machine learning, network analysis, precision, fake news

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Pennycook G, Rand DG. Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc Natl Acad Sci USA. 2019; 116 (7): 2521–2526.
2. Kennedy H, Elgesem D, Miguel C. On fairness: User perspectives on social media data mining. Convergence. 2017; 23(3): 270-288.
3. Ruchansky N, Seo S, Liu Y. CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, November 6–10, 2017. 797–806.
4. Gupta A, Kumaraguru P, Castillo C, Meier P. Tweetcred: real-time credibility assessment of content on twitter. In: Social Informatics: 6th International Conference, SocInfo 2014, Barcelona, Spain, November 11–13, 2014. Cham, Switzerland: Springer International Publishing; 2014. 228–243.
5. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newslett. 2017; 19 (1): 22–36.
6. Conroy NK, Rubin VL, Chen Y. Automatic deception detection: methods for finding fake news. Proc Assoc Inform Sci Technol. 2015; 52 (1): 1–4.
7. Horne BD, Adali S. This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: Proceedings of the International AAAI Conference on Web and Social Media, May 3, 2017. Volume 11. 759–766.
8. Mendoza M, Poblete B, Castillo C. Twitter under crisis: Can we trust what we RT? In: Proceedings of the First Workshop on Social Media Analytics, Washington, DC, USA, July 25, 2010. 71–79.
9. Zannettou S, Caulfield T, Setzer W, Sirivianos M, Stringhini G, Blackburn J. Who let the trolls out? Towards understanding state-sponsored trolls. In: Proceedings of the 10th ACM Conference on Web Science, Boston, MA, USA, June 30–July 3, 2019. pp. 353–362.
10. Friggeri A, Adamic L, Eckles D, Cheng J. Rumor cascades. In: Proceedings of the International AAAI Conference on Web and Social Media, May 16, 2014. Volume 8. 101–110.
11. Marberg E, Pawlowski B. On some properties of symplectic Grothendieck polynomials. J Pure Appl 2021; 225 (1): 106463.
12. Ma J, Gao W, Wong KF. Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, British Columbia, Canada, 2017. Association for Computational Linguistics. 708–717.
13. Waladi W, Partek B, Manoosh J. Regulating ammonia concentration in swine housing: Part II. Application examples. Trans 1999; 43 (4): 540–547.
14. Griffin Jr A Cotton Ginners' Handbook. Agricultural Handbook No. 503. Washington, DC, USA: US Department of Agriculture; 1997.
15. Wegmuller M, Von Der Weid JP, Oberson P, Gisin N. High resolution fiber distributed measurements with coherent OFDR. In: Proceedings of the ECOC’00, September 2000. Volume 11, No. 4, p. 109.
16. Clancey WJ. Transfer of Rule-Based Expertise Through a Tutorial Dialogue. PhD Dissertation. Stanford, CA, USA: Stanford University; 1979.