Nadia Conroy 2017

Nadia was a part of the LiT.RL team from 2010 till 2017. After her graduation, Dr. Conroy was a Postdoctoral Fellow at the Social Media Lab at Ryerson. Under the supervision of Anatoliy Gruzd, she worked on “Learning Analytics Dashboard for the Social Media Age’s e-Learners and Educators,” a project funded by eCampusOntario to study the use of social media by instructors who are teaching online courses as part of eCampusOntario.

Nadia is a Library and Information Science researcher and developer whose specialties are CSCW, NLP, HCI, information behaviour and information retrieval. Her research goal was to study people and behaviour in order to match system design to real-world practice, and thus improve the measurable, human value of information. Her dissertation entitled, Understanding Collaborative Sensemaking for System Design — An Investigation of Musicians’ Practice, utilized information behavior studies to design social, collaborative tools supporting group creativity. Dr. Rubin was her thesis supervisor.

Nadia’s background prior to her academic career lies in application development and instruction in private enterprise. She received BSc in Computer Science and an MLIS, and has a background in software engineering and information management.

Nadia was engaged in related research for collaborative applications for news consumption through social media, deception detection, notions of blame in news, and content sharing. Her work in the lab centers around methodological issues in deception detection and news verification. She has co-authored multiple papers and conference presentations, specifically on the newly developed deception detection techniques. The methodologies have been applied to computer-mediated interactions and radio broadcast transcripts. Her most recent effort is in computational satire identification as satirical news feed are often mistaken for legitimate (“serious”) news, especially when disassociated from their original sources, as it might happen when an article from the Onion is recommended via Facebook newsfeed.