Brynn Evans has been doing some interesting research on looking at how social networks support collaboration over information seeking. She uses two dimensions—search goals and search locations—and looks at how social interactions affect search activities, and discusses the implications that this work has for the design of tools to support information seeking. Slides are available through SlideShare, and a video of the talk is also online.
I wasn’t able to attend or contribute to the Collaborative Information Behavior (CIB) workshop held recently in conjunction with the GROUP 2009 conference, and was looking forward to reading about the discussion, which was posted today by Sharoda Paul. The discussion touched on several interesting points, and perhaps we can get some of the participants to comment more on them (here or elsewhere). The notes highlight discussion around models, behavior vs. technical design, appropriate research methods, explicit vs. implicit collaboration (yeah!), and evaluation.
Just announced is an interesting new platform from Google, around shared collaboration environments. Explicitly-shared environments.
A “wave” is equal parts conversation and document, where people can communicate and work together with richly formatted text, photos, videos, maps, and more…Here’s how it works: In Google Wave you create a wave and add people to it. Everyone on your wave can use richly formatted text, photos, gadgets, and even feeds from other sources on the web. They can insert a reply or edit the wave directly. It’s concurrent rich-text editing, where you see on your screen nearly instantly what your fellow collaborators are typing in your wave. That means Google Wave is just as well suited for quick messages as for persistent content — it allows for both collaboration and communication. You can also use “playback” to rewind the wave and see how it evolved.
Now, add a search layer into this rich, shared space, and you’ll have something quite akin to Merrie Morris’ SearchTogether system, which combines real-time awareness with a collaboratively authored results and note set. Put some algorithmic mediation under that, and you’ll have some of the projects that we’ve been working over the past few years here at FXPAL, which uses real-time actions and behaviors of multiple, explicitly collaborating team members to alter and inform the information that each individual sees. We think that the ability to put jointly-relevant information on the same real-time page, but also let users explicitly work together in the finding and discovery of that information, is and will continue to be an extremely useful application.
Looks like this space is heating up.
It’s my pleasure to announce that the final report to the NSF on the workshop on Information Seeking Support Systems (ISSS) so ably organized by Gary Marchionini and Ryen White has been published. The report covers many aspects that define this research area and distinguish it from both Information Retrieval and Human-Computer Interaction fields.
Three kinds of challenges are defined and preliminary steps toward meeting the challenges are presented in this report: robust models of human‐information interaction; new tools, techniques, and services to support the full range of information seeking activities; and techniques and methods to evaluate information seeking across communities, platforms, sources, and time. Special attention is given to collaborative information seeking and the need for industry‐academic collaboration.
It was a wonderful experience to have two days of discussion of these and other topics with so many smart people, and I am happy to have contributed to the workshop organization and to the writing of the report. Finally, I am delighted that collaborative information seeking is featured as an important aspect of the field. We hope that this report will inspire others to take on the outstanding challenges and will encourage the NSF to understand the significance of this work for our society.
A paper presented at CHI 2009 described strategies and processes used by intelligence analysts. Among other aspects, the paper discusses collaboration among analysts, quoting one of their participants:
What I will not trust and put into my analysis is somebody else’s analysis. I need to know the source of the information and build on that so that I can put my level of trust in it and then it’s my name at stake when I provide an answer… I won’t trust their analysis until I look at the source of the information, and it will be, “Do I agree with the conclusions that they came to based on the facts and the evidence?”
The area of collaborative search has experienced significant growth over the past couple of years in the number of research groups interested in the topic, and in the number of research papers being published in proceedings of respected conferences.
Interestingly, there is also a rash of “best paper” awards for this work. In chronological order of publication, the following papers related to collaborative search have received “best paper” designations.
Congratulations to all the authors, and please let me know if I forgot to list your paper!
In their recent ECIR paper, Joho et al. explored the effectiveness of several mediation techniques around relevance feedback in supporting collaborative search. They ran simulations based on TREC HARD topics queries elicited in an earlier study, and found that certain techniques were effective at increasing recall during (simulated) search sessions consisting of 20 queries.
Giles Crouch wrote recently about social search vs. general search, pointing out that people often search for information in their social network vs. in a general index such a Google or Yahoo! While we need to distinguish the cases when people search their social network for information about the network per se vs. information that the network refers to, there are circumstances when people make explicit decisions about where to search. It seems to me that there are several reasons why people may prefer not to use generic search in certain cases. Continue Reading
Late last year Hideo Joho, David Hannah and Joemon M. Jose published a paper that described an experiment in collaborative exploratory search. They compared teams of pairs of searchers in three conditions — independent (not collaborative), collaborative without communication, and collaborative with communication. This paper is interesting for several reasons, not least of which is that it made an attempt to quantify the effects of collaboration on search performance, an important subject that has not yet received adequate attention.
At CHI 2009 this year, Sharoda Paul presented a paper she co-wrote with Merrie Morris that explores how sensemaking can be managed in a collaborative search environment. They created CoSense, an interface that augments SearchTogether with several tools that facilitate awareness and information sharing among collaborators. Tools include interactive query timelines, statistics on individual queries and term use, chat history, and a workspace for annotating search results.