no code implementations • NAACL (CLPsych) 2021 • Eli Sherman, Keith Harrigian, Carlos Aguirre, Mark Dredze
Spurred by advances in machine learning and natural language processing, developing social media-based mental health surveillance models has received substantial recent attention.
no code implementations • 3 Mar 2024 • Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapta, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason Fries, Parisa Rashidi, Brett Beaulieu-Jones, Xuhai Orson Xu, Matthew McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gursoy, Marzyeh Ghassemi, Emma Pierson, George Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
1 code implementation • 15 Nov 2023 • Keith Harrigian, Tina Tang, Anthony Gonzales, Cindy X. Cai, Mark Dredze
Diabetic eye disease is a major cause of blindness worldwide.
no code implementations • NAACL (CLPsych) 2022 • Keith Harrigian, Mark Dredze
Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental health language from the last decade.
1 code implementation • 22 Jun 2022 • Keith Harrigian, Mark Dredze
Social media allows researchers to track societal and cultural changes over time based on language analysis tools.
no code implementations • EACL 2021 • Carlos Aguirre, Keith Harrigian, Mark Dredze
While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, such as gender and racial/ethnic groups.
1 code implementation • NAACL (CLPsych) 2021 • Keith Harrigian, Carlos Aguirre, Mark Dredze
Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Keith Harrigian, Carlos Aguirre, Mark Dredze
Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples.
1 code implementation • WS 2018 • Keith Harrigian
In this paper, we introduce the first geolocation inference approach for reddit, a social media platform where user pseudonymity has thus far made supervised demographic inference difficult to implement and validate.
no code implementations • 24 Sep 2018 • Ahmet Salih Gundogdu, Arjun Sanghvi, Keith Harrigian
In this paper, we propose a Named Entity Recognition (NER) system to identify film titles in podcast audio.