Maite Taboada

Distinguished SFU Professor, Department of Linguistics
Director, Discourse Processing Lab

Robert C. Brown (RCB) Building 8109
8888 University Drive
Simon Fraser University
Burnaby, BC V5A 1S6, Canada

My name is pronounced like this and transcribed like this:
['ma̯ite taβo'aða] /'maite tabo'ada/

I am Distinguished SFU Professor in the Department of Linguistics at Simon Fraser University and a Fellow of the Royal Society of Canada. I direct the Discourse Processing Lab.
At SFU, I am also an Associate Member of the Cognitive Science Program and the School of Computing Science. I am a member of the Digital Democracies Institute and the Visual and Interactive Computing Institute.

I am a linguist working at the intersection of discourse analysis and computational linguistics. In discourse analysis, I study the mechanisms for coherence in discourse. In computational linguistics, I develop methods and algorithms to process and exploit discourse structure in different applications, such as discourse parsing or sentiment analysis. My research is interdisciplinary, at the boundary between theoretical and applied fields, between the social sciences and engineering.

Current and recent projects:

A project examining the nature of online news comments, those posted in response to a news article. This is a corpus and computational project.
- Corpus: SOCC, the SFU Opinion and Comments Corpus. Ca. 10,000 editorials and 630,000 comments posted on the site of The Globe and Mail. Publicly available.
- Computational work: a system to moderate comments, identifying constructive comments -- those that contribute to the conversation, creating civil dialogue.
- Kolhatkar, V., H. Wu, L. Cavasso, E. Francis, K. Shukla and M. Taboada (2020) The SFU Opinion and Comments Corpus: A corpus for the analysis of online news comments. Corpus Pragmatics 4: 155–190.
- Kolhatkar, V., N. Thain, J. Sorensen, L. Dixon and M. Taboada (2023) Classifying constructive comments. First Monday 28(4). https://doi.org/10.5210/fm.v28i4.13163
A project on detecting fake news and misinformation. Outputs include:
- MisInfoText, a dataset of fact-checked news articles.
- A system to detect fake news online.
- Asr, F. T. and M. Taboada (2019) Big data and quality data for fake news and misinformation detection. Big Data & Society. January-June 2019: 1-14.
The Gender Gap Tracker, developed within our lab and in collaboration with Informed Opinions, monitors the proportion of women and men quoted in news stories in mainstream Canadian media in English. Radar de Parité analyzes French-language media in Canada. The research behind these systems is described in:

Soumah, V-G., P. Rao, P. Eibl and M. Taboada (2023) Radar de Parité: An NLP system to measure gender representation in French news stories. Proceedings of the 36th Canadian Conference on Artificial Intelligence. Montréal. Jun 2023.
Rao, P. and M. Taboada (2021) Gender bias in the news: A scalable topic modelling and visualization framework. Frontiers in Artificial Intelligence – Language and Computation 4(82). doi: 10.3389/frai.2021.664737.
Asr, F.T., M. Mazraeh, A. Lopes, V. Gautam, J. Gonzales, P. Rao and M. Taboada (2021) The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media. PLoS ONE 16(1): e0245533.

More information on these projects is available from the site of the Discourse Processing Lab. You can also check a full curriculum vitae (in pdf), or consult my research, teaching and publications pages. The lab also has a GitHub page.