Abstract
Instructional technology that supports the development of media literacy skills has garnered increased attention in the wake of recent misinformation campaigns. While critical, this work often ignores the role of myside bias in the acceptance and propagation of misinformation. Here we present results from an alternative approach that uses natural language processing to model the dynamic relationship between the user and the content they are consuming. This model powers a debiasing intervention in the context of a “fake news detection” task. Information about the user- and content-values was used to predict when the user may be prone to myside bias. The intervention resulted in significantly better performance on the misinformation classification task. These results support the development of content-general and embedded debiasing systems that could encourage informal learning and bias reduction in real-world contexts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Clifford, S., Iyengar, V., Cabeza, R., Sinnott-Armstrong, W.: Moral foundations vignettes: a standardized stimulus database of scenarios based on moral foundations theory. Behav. Res. Methods 47(4), 1178–1198 (2015)
Diana, N., Stamper, J., Koedinger, K.: Towards value-adaptive instruction: a data-driven method for addressing bias in argument evaluation tasks. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–11 (2020)
Diana, N., Stamper, J.C., Koedinger, K.: Predicting bias in the evaluation of unlabeled political arguments. In: CogSci, pp. 1640–1646 (2019)
Franks, A.S., Scherr, K.C.: Using moral foundations to predict voting behavior: regression models from the 2012 us presidential election. Anal. Soc. Issues Public Policy 15(1), 213–232 (2015)
Gao, T., Yao, X., Chen, D.: SimCSE: simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821 (2021)
Garten, J., Hoover, J., Johnson, K.M., Boghrati, R., Iskiwitch, C., Dehghani, M.: Dictionaries and distributions: combining expert knowledge and large scale textual data content analysis. Behav. Res. Methods 50(1), 344–361 (2017). https://doi.org/10.3758/s13428-017-0875-9
Graham, J., et al.: Moral foundations theory: the pragmatic validity of moral pluralism. In: Advances in Experimental Social Psychology, vol. 47, pp. 55–130. Elsevier (2013)
Graham, J., Nosek, B.A., Haidt, J., Iyer, R., Spassena, K., Ditto, P.H.: Moral foundations questionnaire. J. Pers. Soc. Psychol. (2008)
Hone, B., Rice, J., Brown, C., Farley, M.: Factitious (2018). factitious.augamestudio.com
Koleva, S.P., Graham, J., Iyer, R., Ditto, P.H., Haidt, J.: Tracing the threads: how five moral concerns (especially purity) help explain culture war attitudes. J. Res. Pers. 46(2), 184–194 (2012)
Literat, I., Chang, Y.K., Eisman, J., Gardner, J.: LAMBOOZLED!: the design and development of a game-based approach to news literacy education. J. Media Literacy Educ. 13(1), 56–66 (2021)
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakeNewsNet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8(3), 171–188 (2020)
Silverman, C.: This analysis shows how viral fake election news stories outperformed real news on Facebook, November 2016. https://www.buzzfeednews.com/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook
Sniezek, J.A., Buckley, T.: Cueing and cognitive conflict in judge-advisor decision making. Organ. Behav. Hum. Decis. Process. 62(2), 159–174 (1995)
Stanovich, K.E., West, R.F., Toplak, M.E.: Myside bias, rational thinking, and intelligence. Curr. Dir. Psychol. Sci. 22(4), 259–264 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Diana, N., Stamper, J. (2022). Reducing Bias in a Misinformation Classification Task with Value-Adaptive Instruction. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-11644-5_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11643-8
Online ISBN: 978-3-031-11644-5
eBook Packages: Computer ScienceComputer Science (R0)