Advertisement

Smart Crowdsourcing Based Content Review System (SCCRS): An Approach to Improve Trustworthiness of Online Contents

  • Kishor Datta GuptaEmail author
  • Dipankar Dasgupta
  • Sajib Sen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11280)

Abstract

Online media is now a significant carrier for quicker and ubiquitous diffusion of information. Any user in social media can post contents, provide news blogs, and engage in debate or opinion nowadays. Most of the posted pieces of information on social media are useful while some are fallacious and insulting to others. Keeping the promise of freedom of speech and simultaneously no tolerance against hate speech often becomes a challenge for the hosting services. Some automated tools were developed for content filtering in industries. Also, companies are hiring specialized reviewers for accurate and unbiased reporting. However, these approaches are not achieving the goal as expected, on the other hand, new strategies are being adopted to tweak the automated systems. To face the situation, we proposed a smart crowdsourcing based content review technique to provide trustworthy and unbiased reviews for online shared contents. In this techniques, we designed an intelligent self-learned crowdsourcing strategy to select an appropriate set of reviewers efficiently which ensures reviewers’ diversity, availability, quality, and familiarity with the news topic. To evaluate our proposed method, we developed a mobile app similar to popular social media (e.g., Facebook).

Keywords

Social network security and privacy Big data analysis Fake news Crowd source Social review system 

References

  1. 1.
    Leonhardt, D., Thompson, S.A.: Trump’s lies. New York Times, June 2017. https://www.nytimes.com/interactive/2017/06/23/opinion/trumps-lies.html. Accessed 30 Sept 2018
  2. 2.
    Carlos, M.: Millonario negocio fake news. Univision Noticias (2017). https://www.univision.com/noticias/america-latina/el-millonario-negocio-detras-de-los-sitios-de-fake-news-en-mexico. Accessed 30 Sept 2018
  3. 3.
    Hunt, E.: What is fake news? How to spot it and what you can do to stop it. The Guardian, January 2017. https://www.theguardian.com/media/2016/dec/18/what-is-fake-news-pizzagate. Accessed 30 Sept 2018
  4. 4.
    Woolf, N.: How to solve Facebook’s fake news problem: experts pitch their ideas. The Guardian, January 2017. https://www.theguardian.com/technology/2016/nov/29/facebook-fake-news-problem-experts-pitch-ideas-algorithms. Accessed 30 Sept 2018
  5. 5.
    Shane, S.: From headline to photograph, a fake news masterpiece. New York Times, May 2016. https://www.nytimes.com/2017/01/18/us/fake-news-hillary-clinton-cameron-harris.html. Accessed 30 Sept 2018
  6. 6.
    Gottfried, J., Shearer, E: News use across social media platforms. Pew Research Center Journalism & Media, January 2016. http://www.journalism.org/2016/05/26/news-use-across-social-media-platforms-2016/. Accessed 30 Sept 2018
  7. 7.
    Silverman, C., Strapagiel, L., Shaban, H., Hall, E.: Hyperpartisan Facebook pages are publishing false and misleading information at an alarming rate. Buzzfeed (2016). https://www.buzzfeednews.com/article/craigsilverman/partisan-fb-pages-analysis. Accessed 30 Sept 2018
  8. 8.
    Craig, S.: Viral fake election news outperformed real news on Facebook? Buzzfeed (2016). https://www.buzzfeednews.com/article/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook. Accessed 30 Sept 2018
  9. 9.
    Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. National Bureau of Economic Research (2017). https://www.nber.org/papers/w23089. Accessed 30 Sept 2018
  10. 10.
    Goldman, R.: Reading fake news, Pakistani minister directs nuclear threat at Israel. New York Times (2016). https://www.nytimes.com/2016/12/24/world/asia/pakistan-israel-khawaja-asif-fake-news-nuclear.html. Accessed 30 Sept 2018
  11. 11.
    Melissa, K.: Fake-photos-hurricane-sandy. Yahoo News (2012). https://www.yahoo.com/news/blogs/trending-now/fake-photos-hurricane-sandy-flood-social-media-190021894.html. Accessed 30 Sept 2018
  12. 12.
    Guardian: Fake-photos-hurricane-sandy. Guardian (2012). https://www.theguardian.com/world/us-news-blog/2012/oct/30/hurricane-sandy-storm-new-york. Accessed 30 Sept 2018
  13. 13.
    Gomes, L.H., Cazita, C., Almeida, J.M., Almeida, V., Meira, W.: Workload models of spam and legitimate e-mails. Perform. Eval. 64, 690–714 (2007)CrossRefGoogle Scholar
  14. 14.
    Fetterly, D., Manasse, M., Najork, M.: Spam, damn spam, and statistics: using statistical analysis to locate spam web pages. In: 7th International Workshop on the Web and Databases: Colocated with ACM SIGMOD/PODS 2004, pp. 1–6 (2004)Google Scholar
  15. 15.
    Thomason, A.: Blog spam: a review. In: Conference on Email and Anti-Spam (CEAS), pp. 1–4 (2007)Google Scholar
  16. 16.
    Wu, C.T., Cheng, K.T., Zhu, Q., Wu, Y.L: Using visual features for anti-spam filtering. In: IEEE International Conference on Image Processing 2005, vol. 3, pp. III–509 (2005). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.4031&rep=rep1&type=pdf
  17. 17.
    Gupta, A., Lamba, H., Kumaraguru, P., Joshi, A.: Faking sandy: characterizing and identifying fake images on Twitter during hurricane sandy. In: 22nd International Conference on World Wide Web, pp. 729–736 (2013)Google Scholar
  18. 18.
    Corvey, W.J., Verma, S., Vieweg, S., Palmer, M., Martin, J.H.: Foundations of a multilayer annotation framework for Twitter communications during crisis events. In: 8th International Conference on Language Resources and Evaluation, pp. 1–5 (2012)Google Scholar
  19. 19.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: 20th International Conference on World Wide Web, pp. 675–684 (2011)Google Scholar
  20. 20.
    Canini, K.R., Suh, B., Pirolli, P.L.: Finding credible information sources in social networks based on content and social structure. In: IEEE Third International Conference on Privacy, Security, Risk and Trust, pp. 1–8 (2011)Google Scholar
  21. 21.
    Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: 1st Workshop on Privacy and Security in Online Social Media, pp. 2:2–2:8 (2012)Google Scholar
  22. 22.
    O’Donovan, J., Kang, B., Meyer, G., Hllerer, T., Adalii, S.: Credibility in context: an analysis of feature distributions in Twitter. In: 2012 International Conference on Privacy, Security, Risk and Trust, pp. 293–301 (2012)Google Scholar
  23. 23.
    Eaton-Robb, P.: College students come with plug combat fake news (2017). https://www.businessinsider.com/ap-college-students-come-up-with-plug-in-to-combat-fake-news-2017-12. Accessed 30 Sept 2018

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kishor Datta Gupta
    • 1
    Email author
  • Dipankar Dasgupta
    • 1
  • Sajib Sen
    • 1
  1. 1.Department of Computer ScienceUniversity of MemphisMemphisUSA

Personalised recommendations