This is a list of all of my publications so far (last updated December 2023):
- Rob Churchill and Lisa Singh. Using topic-noise models to generate domain-specific topics across data sources. In Knowledge and Information Systems (KAIS), 2023. (pdf)
- Rob Churchill and Lisa Singh. Temporal Topic-Noise Models for Social Media Data Sets. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2022. (pdf)
- Rob Churchill, Lisa Singh, Rebecca Ryan, and Pamela Davis-Kean. A Guided Topic Model for Social Media Data. In The Web Conference (WWW), 2022. (pdf)
- (Doctoral Thesis) Rob Churchill. Modernizing Topic Models: Accounting for Noise, Time, and Domain Knowledge. Diss. Georgetown University, 2021. (pdf)
- Jaren Haber, Lisa Singh, Ceren Budak, Josh Pasek, Meena Balan, Ryan Callahan, Rob Churchill, Brandon Herren, Kornraphop Kawintiranon. Research Note: Lies and presidential debates: How political misinformation spread across media streams during the 2020 election. Harvard Kennedy School Misinformation Review, 2021. (html)
- Rob Churchill and Lisa Singh. Topic-Noise Models: Modeling Topic and Noise Distributions in Social Media Post Collections. In International Conference on Data Mining (ICDM), 2021. (pdf)
- Rob Churchill and Lisa Singh. The evolution of topic modeling. In ACM Computing Surveys (CSUR), 2021. (pdf)
- Rob Churchill and Lisa Singh. textPrep: A text preprocessing toolkit for topic modeling on social media data. In The DATA Conference, 2021. (pdf)
- Rob Churchill and Lisa Singh. Percolation-based topic modeling for tweets. In KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM), 2020. (pdf)
- Rob Churchill, Lisa Singh, and Josh Pasek. The impact of pre-processing classes on meaningful topics from online text data. In MIDAS Symposium, 2018.
- Rob Churchill, Lisa Singh, and Christo Kirov. A temporal topic model for noisy mediums. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018. (pdf)