Alcoholics Anonymous (AA) is the largest grassroots peer support group for any health condition. While AA meeting attendance is particularly important for people who are newly sober, newcomers often have trouble finding meetings because of a lack of global up-to-date meeting list due to preference for regional autonomy in AA's organizational structure. Detection of regional webpages containing meetings and extraction of day, time, and address of meetings from those pages are essential steps in making the information available and up-to-date in a global meeting list. However, varied structure of the webpages and the meetings pose challenges in achieving the goal with traditional information retrieval methods. In this paper we propose HAIR: a semi-automated human-aided information retrieval technique and explore its potential to solve this problem. We describe future directions in developing this critical tool and discuss major implications of our work in pointing to the importance of context-specific rather than context-agnostic semi-automated information retrieval techniques by conceptualizing the proposed methods and results in a broader context.
|Original language||English (US)|
|Title of host publication||CHIIR 2019 - Proceedings of the 2019 Conference on Human Information Interaction and Retrieval|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|State||Published - Mar 8 2019|
|Event||4th ACM SIGIR Conference on Information Interaction and Retrieval, CHIIR 2019 - Glasgow, United Kingdom|
Duration: Mar 10 2019 → Mar 14 2019
|Name||CHIIR 2019 - Proceedings of the 2019 Conference on Human Information Interaction and Retrieval|
|Conference||4th ACM SIGIR Conference on Information Interaction and Retrieval, CHIIR 2019|
|Period||3/10/19 → 3/14/19|
Bibliographical noteFunding Information:
We would like to thank Thomas Crumrine for helping us develop the machine learning model. We also thank Zachary Levonian and Loren Terveen for their useful feedback on the paper. This work was funded by the NSF grant (1464376 and 1651575).
© 2019 Association for Computing Machinery.
- Alcoholics anonymous
- Human computation
- Information retrieval
- Peer support