Often, members of our group will collaborate on research projects together. These are often driven by a desire to improve computational algorithms, answer social science questions, or both. A sample of our work is presented below.

Explorations in Computational Modeling

Duan, Z., Yang, S., Shao, A., Chen, K., Hu, Y., Suh, Y., Kim, J., Lee, H., and  Liao, X. (2023). VecOpt: Development of a Word Embedding-based Optimization Approach to Extracting Moral Appeals from Text. Paper accepted by the 2023 International Communication Association Conference, Toronto (This work was selected to receive funding from the WARF Accelerator Big Data Challenge Grant 2023!)

SMAD Assistance (Continual)

As this group is affiliated with the Journalism School’s Social Media and Democracy research group, many of our members assist with computational tasks for a variety of SMAD’s research projects. As of the 2021-2022 year, ongoing SMAD projects that we are working on include:

  • A multi-layer analysis of gun discourse on social media and news during mass shootings, and its subsequent impact on legislation and gun sales.
  • An in-depth analysis of the 2016 and 2020 U.S. Presidential debates, including computational analysis of audio features, video features, linguistic features, and reaction from social media (i.e., Twitter).
  • A cross-platform analysis of the #MeToo movement from 2017 to 2020. Paper:
    • Li, M., Suk, J., Sun, Y., Lian, R., Zhang, Y., Kwon, H., Wang, R., Dong, X., Pevehouse, J., and Shah, D. V. (2023). Discursive Opportunities in Social Media Activism: A Cross-platform Analysis of #MeToo on Twitter, Facebook, and Reddit, 2017-2020 (2023). Paper accepted by the 2023 International Communication Association Conference, Toronto

“Sockhunt” Russian Disinformation Project (2017-Ongoing)

This project focuses on Russian disinformation efforts in digital communication from 2013 to present day. Ongoing work includes analyzing the popularity of IRA-linked accounts on Twitter during and after the 2016 U.S. Presidential Election, comparing tweets written by IRA accounts in English and Russian, and analyzing tweets written by IRA accounts during Euromaidan.

One of their paper won the Political Communication Interest Group’s Top Paper Award at the 2018  Association for Education in Journalism and Mass Communication (AEJMC) annual conference.


Doroshenko, L. & Lukito, J. (2019). Trollfare: Russia’s disinformation campaign during military conflict in Ukraine. Paper submitted to the 2019 Association for Education in Journalism and Mass Communication (AEJMC) Conference.

Zhang, Y., Lukito, J., Su, M.-S., Suk, J., Xia, Y., Kim, S. J., & Wells, C. (2019). Beyond Disinformation: How Polarized Media and Publics Amplified Russian IRA Operatives’ Influence in the 2016 US Election Cycle. Paper submitted to the 2019 AEJMC Conference.

Suk. J., Lukito, J., Su, M-H., Kim, S. J., Sun, Z., Sarma, P., & Tong, C. (2019). Do I sound American? Predicting disinformation sharing of Russian IRA tweets from a linguistic perspective. Paper accepted to the 2019 Annual Conference of the International Communication Association (ICA).

Xia, Y., Lukito, J., Zhang, Y., Kim, S. J., Tong, C. (2018). “This world lacks personalities. You have one.” Self-presentation of an IRA account. Paper presented at the 2018 Information Communication and Society symposium.

Lukito, J., Suk, J., Zhang, Y., Doroshenko, L., Su, M.-H., Kim, S. J., Xia, Y. & Wells, C. (2018). Hacking the message amplification cycle: How Russia’s Internet Research Agency infiltrated American political journalism. Paper presented at the 2018 AEJMC Conference. [Top Faculty Paper, Political Communication Interest Group] [Third Paper Professional Relevance Award,  AEJMC]

Obamacare and Polarization Project (2015-2016)

This project applied supervised machine learning strategies to study political polarization in tweets about the Affordable Care Act (during the year 2012). This analysis also considered the role of elites on Twitter, including celebrities, politicians, news organization, and other “opinion leaders.”


Yang, J., Sangari, A., Duncan, M., Zhang, Cao, D., Lukito, J, Bialik, K., Kim, S., Kornfield, R., Wu, Y., & Zhang, W. (2017). Obamacare and Political Polarization on Twitter: An Application of Machine Learning and Social Network Analysis. Paper presented at the 2017 International Communication Association Conference.

Yang, J., Sangari, A., Zhang, W., & Shah, D. V. (2016). Applying Supervised Machine Learning to Compute Political Ideology Among Twitter Users. Paper submitted to the 2016 International Conference on Computational Social Science. Evanston, IL, USA.