I am a third-year Computer Science PhD candidate in Columbia University's Natural Language Text Processing Lab. My research interests broadly lie at the intersection of Natural Language Processing, linguistics, and the social sciences working toward more nuanced and improved understanding of attitudes in LLMs including the relationship between attitude expression and identity. I am advised by Professor Kathleen McKeown, and I am generously supported by the NSF Graduate Research Fellowship, the Columbia Presidential Fellowship, and the Provost Diversity Fellowship.

Email: ndeas AT cs DOT columbia DOT edu
Office: Schapiro CEPSR 724
[ CV ][ Github ][ Google Scholar ] [ LinkedIn ]

Research Interests

Linguistic Biases in LLMs

Much of the text found online used for pre-training language models reflects White Mainstream English, leading to models that are less capable of understanding other dialects and language varieties of English such as African American Language. This research direction seeks to evaluate and mitigate linguistic biases to help create models capable of understanding a wider diversity of language.

Modeling Social Psychology

As language models continue to be applied in psychological research and mental health settings, they must be capable of interpreting linguistic expressions of emotion or mental health. This research direction draws on theories and findings in social psychology and psycholinguistics to embue models with better understanding of human interpersonal behavior.

Political Perspectives and Polarization

With increased interaction on social media and a diverse array of media sources, it is important to ensure that language models fairly learn from and aid productive public discourse. This research direction aims to evaluate the political alignment of language models, ensure fair representation of perspectives on issues, and build models capable of understanding nuances of political speech and rhetoric.

Selected Publications

[NLP: NLP and Computational Linguistics , P: Psychology, PS: Political Science]

[NLP,PS] Data Caricatures: On the Representation of African American Language in Pretraining Corpora

Nicholas Deas*, Blake Vente*, Amith Ananthram, Jessi Grieser, Desmond Patton, Shana Kleiner, Jessi Grieser, James Shepard, Kathleen McKeown (Preprint)

[NLP,PS] Summarization of Opinionated Political Documents with Varied Perpsectives

Nicholas Deas, Kathleen McKeown (COLING 2025)

[NLP,P] MASIVE: Open-Ended Affective State Identification in English and Spanish

Nicholas Deas, Elsbeth Turcan, Iván Pérez Mejía, Kathleen McKeown (EMNLP 2024)

[NLP] PhonATe: Impact of Type-written Phonological Features of African American Language on Generative Language Modeling Tasks

Nicholas Deas, Jessi Grieser, Xinmeng Hou, Shana Kleiner, Tajh Martin, Sreya Nandanampati, Desmond U Patton, Kathleen McKeown (COLM 2024)

[NLP] How Negativity and Policy Content Drive the Spread of Political Messages

Jeffrey A. Fine, D. Hudson Smith, Cierra Oliveira, Nicholas Deas, Spencer Shellnutt, Riley Stotzky, Rachel Clyburn (Journal of Information Technology & Politics)

[NLP,PS] Evaluation of African American Language Bias in Natural Language Generation

Nicholas Deas, Jessi Grieser, Shana Kleiner, Desmond Patton, Elsbeth Turcan, Kathleen McKeown (EMNLP 2023)

[NLP,P] I just want to matter: Examining the role of anti-mattering in online suicide support communities using natural language processing

Nicholas Deas, Robin Kowalski, Sophie Finnell, Emily Radovic, Hailey Carroll, Chelsea Robbins, Andrew Cook, Kenzie Hurley, Natalie Cote, Kelly Evans, Isabella Lorenzo, Kelly Kiser, Gabriela Mochizuki, Meredith Mock, and Lyndsey Brewer (Computers in Human Behavior 2023)

[NLP, PS] Using Natural Language Processing to Automate Detection of Targeted Attacks in Political Tweets.

Nicholas Deas, Jacob Sargent, and Spencer Shellnut (MPSA 2019)