Answer Garden: a tool for growing organizational memory
COCS '90 Proceedings of the ACM SIGOIS and IEEE CS TC-OA conference on Office information systems
Answer Garden 2: merging organizational memory with collaborative help
CSCW '96 Proceedings of the 1996 ACM conference on Computer supported cooperative work
Visual information foraging in a focus + context visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Relational View of Information Seeking and Learning in Social Networks
Management Science
What are you looking for?: an eye-tracking study of information usage in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Can social bookmarking enhance search in the web?
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
SearchTogether: an interface for collaborative web search
Proceedings of the 20th annual ACM symposium on User interface software and technology
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
CoSearch: a system for co-located collaborative web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A survey of collaborative web search practices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Algorithmic mediation for collaborative exploratory search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Signpost from the masses: learning effects in an exploratory social tag search browser
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
mimir: a market-based real-time question and answer service
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
With a little help from my friends: examining the impact of social annotations in sensemaking tasks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social Navigation Support for Information Seeking: If You Build It, Will They Come?
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Understanding together: sensemaking in collaborative information seeking
Proceedings of the 2010 ACM conference on Computer supported cooperative work
WeSearch: supporting collaborative search and sensemaking on a tabletop display
Proceedings of the 2010 ACM conference on Computer supported cooperative work
What do people ask their social networks, and why?: a survey study of status message q&a behavior
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
An elaborated model of social search
Information Processing and Management: an International Journal
Do your friends make you smarter?: An analysis of social strategies in online information seeking
Information Processing and Management: an International Journal
Co-located collaborative sensemaking on a large high-resolution display with multiple input devices
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part II
Who knows?: searching for expertise on the social web: technical perspective
Communications of the ACM
Asking questions of targeted strangers on social networks
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Direct answers for search queries in the long tail
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social annotations in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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As web search increasingly becomes reliant on social signals, it is imperative for us to understand the effect of these signals on users' behavior. There are multiple ways in which social signals can be used in search: (a) to surface and rank important social content; (b) to signal to users which results are more trustworthy and important by placing annotations on search results. We focus on the latter problem of understanding how social annotations affect user behavior. In previous work, through eyetracking research we learned that users do not generally seem to fixate on social annotations when they are placed at the bottom of the search result block, with 11% probability of fixation [22]. A second eyetracking study showed that placing the annotation on top of the snippet block might mitigate this issue [22], but this study was conducted using mock-ups and with expert searchers. In this paper, we describe a study conducted with a new eyetracking mix-method using a live traffic search engine with the suggested design changes on real users using the same experimental procedures. The study comprised of 11 subjects with an average of 18 tasks per subject using an eyetrace-assisted retrospective think-aloud protocol. Using a funnel analysis, we found that users are indeed more likely to notice the annotations with a 60% probability of fixation (if the annotation was in view). Moreover, we found no learning effects across search sessions but found significant differences in query types, with subjects having a lower chance of fixating on annotations for queries in the news category. In the interview portion of the study, users reported interesting "wow" moments as well as usefulness in recalling or re-finding content previously shared by oneself or friends. The results not only shed light on how social annotations should be designed in search engines, but also how users make use of social annotations to make decisions about which pages are useful and potentially trustworthy.