Misinformation Prevention

We work on multiple aspects of misinformation prevention, including:

1. Detecting fake news automatically: Using text and visual features together with media theories to build machine learning based classifiers for fake news detection [1, 2].

2. Understanding COVID related conspiracy theories: Using Google autocomplete as a lens to understand how conspiracy related terms spread across the globe [3].

3. Understanding the differences in COVID related autocompletes in English and Spanish: Systematically understanding the differences between Google search autocompletes in English and Spanish [4].

4. Spam detection on Twitter: Understanding different modes of spam transmission over networks and identifying ways to detect them [5,6].

Related Publications

  1. Park, J., Ellezhuthil, R., Arunachalam, R., Feldman, L., & Singh, V. (2022). Fairness in Misinformation Detection Algorithms. In Workshop Proceedings of the 16th International AAAI Conference on Web and Social Media. Retrieved from https://doi. org/10.36190.
  2. Singh, V. K., Ghosh, I., & Sonagara, D. (2021). Detecting fake news stories via multimodal analysis. Journal of the Association for Information Science and Technology, 72(1), 3-17.
  3. Houli, D. A., Radford, M. L., & Singh, V. K. (2021). “COVID19 is_”: The Perpetuation of Coronavirus Conspiracy Theories via Google Autocomplete. Proceedings of the Association for Information Science and Technology, 58(1), 218-229.
  4. Singh, V. K., Singh, I., & Valera, P. (2021). Search Auto-Completes Related to COVID-19 Yield Different Results in English and Spanish, Rutgers (Preliminary) Technical Report. Available at: http://sites.comminfo.rutgers.edu/vsingh/wp-content/uploads/sites/35/2020/06/Language_Bias_in_COVID_Search_2020.06.01.pdf
  5. Almaatouq, A., Shmueli, E., Nouh, M., Alabdulkareem, A., Singh, V. K., Alsaleh, M., Alarifi, A., Alfaris, A. & Pentland, A.S. (2016). If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. International Journal of Information Security, 15(5), 475-491
  6. Almaatouq, A., Alabdulkareem, A., Nouh, M., Shmueli, E., Alsaleh, M., Singh, V. K., Alarifi, A., Alfaris, A., & Pentland, A. S. (2014). Twitter: who gets caught? Observed trends in social micro-blogging spam. In Proceedings of the 2014 ACM conference on Web science (pp. 33-41).

    Funding and Support

We gratefully acknowledge the support from the National Science Foundation for this work.

1. EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Fair and Accurate Information Quality Assessment Algorithm

2. RAPID: Countering Language Biases in COVID-19 Search Auto-Completes

 
Coverage

Rutgers Today: Online Autocompletes Are More Likely to Yield COVID-19 Misinformation in Spanish than in English