CSCW2008 Paper on “Towards a Model of Understanding Social Search”
Search engine researchers typically depict search as the solitary activity of an individual searcher. They hardly ever talk about the social interactions that occurs around search. I think this is just plain wrong.
Brynn Evans and I recently conducted research asking web users their experiences of using search engines on the web. We conducted a type of survey called Critical Incident Survey, where we asked them to recall the last time they did a search on the web, and what that experience was like. Results from our critical-incident survey of 150 users on Amazon’s Mechanical Turk suggest that social interactions play an important role throughout the search process.
We surveyed users about their most recent searching experience. We used Amazon’s Mechanical Turk, a type of micro-task market, which can engage a large number of users to perform evaluation tasks both at low cost and relatively quickly (see our previous published paper in CHI2008 about this approach of doing user studies).
We recruited users with a specific statement of our purpose: “We are interested in how you search for digital information on your computer. Please answer the following questions about your most recent search experience.”
We then analyzed the results from the survey and looked to see where social interactions occurred. Note that we didn’t specifically ask them to recall incidents in which they had social interactions—just the “most recent” search they did. This style of survey forces users to recall the last significant event that they essentially can still remember. Consequently, about 2/3 of search acts occurred on the same day that users filled out our survey (48.7% occurred “recently” and 14.7% occurred “earlier in the day”). 19.3% of searches occurred the day before, and 17.3% occurred more than 2 days ago.
Here is an example of an interesting report we received. A barista (let’s call her Janet) works in a cafe, and couldn’t remember a really good recipe for a special drink. But she can remember just several ingredients in the recipe. She asks her colleagues if they know the drink, and of course she didn’t know the name of the drink. She had partial knowledge of what she needs to know, but only had more specific information to find the recipe. She goes to Google and types in the ingredients and finally finds recipe after some browsing and searching. After she finds the recipe, she prints out the information and shares it with her co-workers in the cafe the next day.
Interestingly, Janet’s extended search process not only extended over a few days, but she also interacted socially around her search process both before as well as after the search. The problem is that Google only sees her interaction with the search engine for a brief period of time, not knowing the entire social process that occurred behind the scene. Perhaps the search engine only saw keywords like “coffee cinnamon honey”, but not how she had obtained some of these ingredients’ name from other co-workers nor how she printed out the result to share with someone.
Janet never had a chance to interact with other baristas (who might be online at that moment) to see if they had a better idea about how to look for the recipe. Her new found knowledge was also not shared with other like-minded community interested in coffee drinks. Delicious and other social tagging sites can be used by groups of people to share what they have found, but the knowledge does not travel easily from the person who found it to the person that needs it efficiently. It seems tool support for social search is still relatively poor.
Now, our definition of “social search” is intended to be broad, to include a range of possible social interactions that may facilitate information seeking and sensemaking tasks:
“Social search” is an umbrella term used to describe
search acts that make use of social interactions with
others. These interactions may be explicit or implicit,
co-located or remote, synchronous or asynchronous.
In terms of results from our research, this example insight is just the tip of the iceberg. Stay tuned for more results from this research about to be published in CSCW2008.
Brynn Evans, Ed H. Chi. Towards a Model of Understanding Social Search. In Proc. of Computer-Supported Cooperative Work (CSCW), (to appear). ACM Press, 2008. San Diego, CA.
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