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Fact Checking Misinformation Using Recommendations from Emotional Pedagogical Agents

  • Ricky J. SethiEmail author
  • Raghuram Rangaraju
  • Bryce Shurts
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11528)

Abstract

Dealing with complex and controversial topics like the spread of misinformation is a salient aspect of our lives. In this paper, we present initial work towards developing a recommendation system that uses crowd-sourced social argumentation with pedagogical agents to help combat misinformation. We model users’ emotional associations on such topics and inform the pedagogical agents using a recommendation system based on both the users’ emotional profiles and the semantic content from the argumentation graph. This approach can be utilized in either formal or informal learning settings, using threaded discussions or social networking virtual communities.

Notes

Acknowledgments

We would like to gratefully acknowledge support from the Amazon AWS Research Grant program. We would also like to thank Roger Azevedo for the valuable discussions and support.

References

  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Acm Sigmod Record, vol. 22, pp. 207–216. ACM (1993)Google Scholar
  2. 2.
    Baram-Tsabari, A., Sethi, R.J., Bry, L., Yarden, A.: Asking scientists: a decade of questions analyzed by age, gender, and country. Sci. Educ. 93(1), 131–160 (2008).  https://doi.org/10.1002/sce.20284CrossRefGoogle Scholar
  3. 3.
    Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User-Adapt. Interact. 25(2), 99–154 (2015)CrossRefGoogle Scholar
  4. 4.
    Chen, L., Pu, P.: Experiments on the preference-based organization interface in recommender systems. ACM Trans. Comput.-Hum. Interact. 17(1), 1–33 (2010).  https://doi.org/10.1145/1721831.1721836MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chen, L., Wang, F.: Sentiment-enhanced explanation of product recommendations. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 239–240. ACM (2014)Google Scholar
  6. 6.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)Google Scholar
  7. 7.
    Keeney, R.L., Raiffa, H., Rajala, D.W.: Decisions with multiple objectives: preferences and value trade-offs. IEEE Trans. Syst. Man Cybern. 9(7), 403–403 (1979)CrossRefGoogle Scholar
  8. 8.
    Sethi, R., Rangaraju, R.: Extinguishing the backfire effect: using emotions in online social collaborative argumentation for fact checking. In: 2018 IEEE International Conference on Web Services, ICWS 2018, San Francisco, CA, USA, 2–7 July 2018, pp. 363–366 (2018).  https://doi.org/10.1109/ICWS.2018.00062
  9. 9.
    Sethi, R.J.: Crowdsourcing the verification of fake news and alternative facts. In: ACM Conference on Hypertext and Social Media (ACM HT) (2017).  https://doi.org/10.1145/3078714.3078746
  10. 10.
    Sethi, R.J.: Spotting fake news: a social argumentation framework for scrutinizing alternative facts. In: IEEE International Conference on Web Services (IEEE ICWS) (2017)Google Scholar
  11. 11.
    Sethi, R.J., Bry, L.: The Madsci network: direct communication of science from scientist to layperson. In: International Conference on Computers in Education (ICCE) (2013)Google Scholar
  12. 12.
    Sethi, R.J., Gil, Y.: A social collaboration argumentation system for generating multi-faceted answers in question & answer communities. In: CMNA at AAAI Conference on Artificial Intelligence (AAAI) (2011)Google Scholar
  13. 13.
    Sethi, R.J., Rossi, L.A., Gil, Y.: Measures of threaded discussion properties. In: Intelligent Support for Learning in Groups at International Conference on Intelligent Tutoring Systems (ITS) (2012)Google Scholar
  14. 14.
    Tintarev, N., Masthoff, J.: Explaining recommendations: design and evaluation. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 353–382. Springer, Boston, MA (2015).  https://doi.org/10.1007/978-1-4899-7637-6_10CrossRefGoogle Scholar
  15. 15.
    Wineburg, S., McGrew, S.: Lateral reading: reading less and learning more when evaluating digital information (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Fitchburg State UniversityFitchburgUSA

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