The PeopleReporter: Smart social media tool to detect breaking news and it's credibility

Main Article Content

Srivathshan KS
Parvej Reja Saleh
Vishesh Kumar Jha
Joyfred Jesuraj A
Desu Sesha Sai Suhash


With a total of 4,156,932,140 internet users by 2017, the number of internet users has increased drastically, reaching 54% of the total population and counting. An increase in the total number of users means more user-generated content across several online platforms, which is predominantly real-time. The user-generated content is being leveraged by applications to derive insights into customer behavior, opinion mining, marketing and for providing niche services like banking in real-time. In recent years, we have also seen a rise in citizen journalism and public posting real-time events on social media channels. Social media has emerged as a supporting player for traditional media as well as powerful standalone expression tool for the public, and hence changing the reliance on traditional media for reports and news. Further, the increase in smartphones and better coverage of data networks has shown increased credible news sourced by mainstream media to be from Social Media. Not only media agencies but the real-time event identification can be used by security departments, disaster management, and others for quick action. The most prominent source of information is the micro-blogging site, Twitter providing geolocation and other features like time, author id, author name, source, link, people’s reaction towards that data, etc. and can be easily extracted, stored and analyzed using Big Data Tools. Entities extraction in Natural language Processing (NLP) is used for identifying the type of event and proceed further. The fundamental goal of our work is to limit the spread of falsehood by halting the proliferation of fake news in the system. This helps us in taking lead in collecting information on certain events ahead of local media platforms. For example, when an earthquake occurs, people make many posts related to the earthquake, which enables detection of earthquake occurrence promptly. Our model delivers such notifications of such events much faster than the announcements of other media sources. In this paper, we have utilized the information from the social platforms in real-time based upon some keywords and geolocation and visualized it with powerful BI tools. Continuous monitoring helps us analyzing the events occurring in the respective geolocation and defining its credibility. The credibility of such an event is detected with the help of the credit score factors developed considering multiple factors including temporal and spatial features of the reported content.

Article Details

How to Cite
KSS., SalehP. R., JhaV. K., AJ. J., & Sai SuhashD. S. (2020). The PeopleReporter: Smart social media tool to detect breaking news and it’s credibility. Probyto AI Journal, 1(01). Retrieved from


[1] A. Kaplan and M. Haenlain, "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, vol. 53, no. 1, pp. 59-68, January-February 2010.
[2] M. Zuckerberg, "Facebook," 04 October 2012. [Online]. Available: [Accessed 6 December 2012].
[3] S. Petrovic and e. al., "Streaming first story detection with application to Twitter," in Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010.
[4] A. Agarwal and e. al., "Sentiment Analysis of Twitter Data," in LSM '11 Proceedings of the Workshop on Languages in Social Media, 2011.
[5] E. Kouloumpis and e. al., "Twitter Sentiment Analysis: The Good the Bad and the OMG!," in In Fifth International AAAI Conference on Weblogs and Social Media, 2011.
[6] C. Taylor, March 2011. [Online]. Available:
[7] W. Dou and e. al., "Event Detection in Social Media Data," in IEEE VisWeek Workshop on Interactive Visual Text Analytics – Task Driven Analytics of Social Media Content, 2012.
[8] C. Li, A. Sun and A. Datta, "Twevent: Segment-based Event Detection from Tweets," in The 21st ACM International Conference on Information and Knowledge Management, New York, 2012.
[9] M. Osborne, S. Petrovic, R. McCreadie, C. Macdonald and I. Ounis, "Bieber no more: First Story Detection using Twitter and Wikipedia," in In Proceedings SIGIR 2012 Workshop on Timeaware Information Access, Portland, 2012.
[10] A. Pak and P. Paroubek, "Twitter as a Corpus for Sentiment Analysis and Opinion Mining," in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta, 2010.
[11] S. Petrovic, M. Osborne and V. Lavrenko, "The Edinburgh Twitter Corpus," in Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media, Stroudsburg, 2010.
[12] N. Arif, W. Edi, “Event Detection in Social Media: a Survey”
[13] T. Baldwin, P. Cook, B. Han, A. Harwood, S. Karunasekera and M. Moshtaghi, "A Support Platform for Event Detection Using Social Intelligence," in Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, 2012.
[14] T. Sakaki, M. Okazaki and Y. Matsuo, "Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors," in Proceedings of the 19th International Conference on World Wide Web, 2010.
[15] M. Osborne and e. al., "Bieber no more: First Story Detection using Twitter and Wikipedia," in Proceedings of the SIGIR Workshop in Time-aware Information Access, 2012.
[17] World Health Organization (WHO), Emergencies preparedness, response, Disease Outbreak News (DONs); ‘Nipah virus – India’
[18] Joy Tirkey, NDTV, Sports Home, IPL 2018 – News, ‘Du Plessos Shines as CSK Beat SRH to enter Tournament Final’
[19] Business Desk, ‘OnePlus 6 launched in India at Rs 34,999’

Most read articles by the same author(s)

1 2 > >>