Abstract
Given the important role of search engines in our everyday lives, a better understanding of the information needs that guide our information seeking behavior is essential. Known-item needs form a particular type of information need and occur when a user has a limited but concrete description of an existing object and would like to (re-)find it. Most studies of know-item needs have focused on the short query representations of these needs as they occur in search engine logs. In this article, we focus on richer, more complex known-item need representations posted to six dedicated Reddit discussion forums in the casual leisure domain. An analysis of 462 known-item requests from these subreddits revealed 33 different relevance aspects of items in a variety of different domains. Some of these aspects are highly domain-specific, while others are broadly applicable across domains. The domain %of the item sought also has a strong influence on the length of the known-item requests. Our findings can be used to prioritize efforts to help existing search engines better support known-item needs, both by highlighting which aspects are easier to classify automatically and by determining which information sources should be added to a search engine's index.
Original language | English |
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Title of host publication | Proceedings of the 32nd ACM Conference on Hypertext and Social Media (HT '21) |
Editors | Owen Conlan |
Publisher | Association for Computing Machinery |
Publication date | 2021 |
Pages | 143-154 |
ISBN (Electronic) | 978-1-4503-8551-0 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Event | 32nd ACM Conference on Hypertext and Social Media - Virtual, United States Duration: 30 Aug 2021 → 2 Sept 2021 https://dl.acm.org/doi/proceedings/10.1145/3465336 |
Conference
Conference | 32nd ACM Conference on Hypertext and Social Media |
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Location | Virtual |
Country/Territory | United States |
Period | 30/08/2021 → 02/09/2021 |
Internet address |
Keywords
- Search engines
- Information needs
- Known-item needs
- Search behavior
- Relevance aspects