학술논문

Inquire : Large-scale Early Insight Discovery for Qualitative Research
Document Type
Conference
Source
Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. :1562-1575
Subject
big data
exploratory
hypothesis formation
insights
keyword search
large-scale data
qualitative research
semantic
text data
Language
English
Abstract
We introduce Inquire, a tool designed to enable qualitative exploration of utterances in social media and large-scale texts. As opposed to keyword search, Inquire allows the effective use of sentences as queries to quickly explore millions of documents to retrieve semantically-similar sentences. We apply Inquire to LiveJournal.com (LJ) database, which contains millions of personal diaries, and we use semantic embeddings trained in LJ or Google News (GN) datasets. We present the system design through iterative evaluations with qualitative researchers. We show how queries become a part of the inductive process, enabling researchers to try multiple ideas while gaining intuition and discovering less-obvious insights. We discuss the choice of LJ as a rich source of public posts, the preference for GN embeddings which link formal language (e.g. "reminiscence triggers") with colloquial expressions (e.g. "music brings back memories"), the interplay between tool and user, and potential qualitative and social research opportunities.

Online Access