There are a number of new sites proclaiming to do social search, and yet each implementation takes somewhat a different approach. It’s fine if social search does not have one clear, precise definition. The goal of this project was, simply, to learn about the role of social interactions during search, such as:

1. Where are social interactions useful in the search process?
2. Why are social interactions useful when they occur?

I did this work with Ed Chi at PARC, and it has been written up in more detail on my blog, on PARC’s blog, and in the full paper.

In brief summary, we ran a critical-incident survey on Mechanical Turk asking 150 users to recount their most recent search experience. We didn’t provide grand incentives for completing our survey (merely 20-35 cents), but we structured the survey in a narrative format and figured that most people completed it because it was fun or interesting. (This is a major reason for Turker participation.)

From this we constructed a model of social search, identifying two classes of searchers and three types of searches (or information needs, based on Broder’s taxonomy). Not every search is “created equal” so to speak:

Self-motivated users—performing self-initiated searches—were the most interesting because of their search habits, propensity to seek help from others, and the reasons behind their social exchanges. For this class of users, a majority performed informational, exploratory searches where the search query was ambiguous, unclear, or poorly specified, leading to a need for guidance from others. Their social interactions, therefore, were primarily used to brainstorm, get more information, and further develop their search schema before embarking on their search. Finally, the search process didn’t end after these users identified preliminary search results—they often shared their findings out of interest to others, but also to get feedback, validate their results, and contemplate refining and repeating their search.

It is noteworthy that we did not ask users to report social search experiences in the survey. Instead, we asked for their most recent search act, regardless of what it was, expecting that across all 150 examples we would be able to begin finding generalizable patterns. Indeed, a large majority performed social search acts, but nearly all of the social exchanges were done through real-world interactions—not through online tools. It is no surprise that online tools need to better support social search experiences (our study is only further proof of this); but our study does contribute to a better understanding of user needs during “social” search, which may lead to tools that can best identify and support the class of users and search types best suited for explicit and implicit social support during search.

Finally, in response to the questions I posed at the very beginning:

Where are social interactions useful in the search process?
Before, during, and after a “search act”! Over 2/3 of our sample interacted with others at some point during the course of searching. However, social interactions may not benefit everyone equally—they appear to provide the best support for self-motivated users and users performing informational searches.

Why are social interactions useful when they occur?
It depends! The reasons for engaging with others ranged from a need to establish search guidelines to a need for brainstorming, collecting search tips, seeking advice, getting feedback, and validating search results. Social support during search may be best appreciated and adopted if it directly addresses these types of user needs.

James D. Hollan, Edwin L. Hutchins, and Javier Movellan

What conditions can facilitate rapid advances and breakthroughs in behavioral science to rival those seen in the biological and physical sciences in the past century? The emergence of cognitive science and the converging view across multiple disciplines that human behavior is a complex dynamic interaction among biological, cognitive, linguistic, social and cultural processes are important first steps. While empirical and theoretical work is rapidly advancing at the biological end of this continuum, understanding such a complex system also necessitates data that capture the richness of real-world human activity and analytic frameworks that can exploit that richness.

In the history of science, changes in technologies for capturing data, as well as those for creating and manipulating representations, have often led to significant advances. The human genome project, for example, would have been impossibly complex without automatic DNA sequencing. Recent advances in digital technology present unprecedented opportunities for the capture, storage, analysis, and sharing of human activity data.

Researchers from many disciplines are taking advantage of increasingly inexpensive digital video and storage facilities to assemble extensive data collections of human activity captured in real-world settings. The ability to record and share such data has created a critical moment in the practice and scopeof behavioral research. The main obstacles to fully capitalizing on this opportunity are the huge time investment required for analysis using current methods and understanding how to coordinate analyses focused at different scales so as to profit fully from the theoretical perspectives of multiple disciplines.

We propose to integrate video and multiscale visualization facilities with computer vision techniques to create a flexible open framework to radically advance analysis of time-based records of human activity. We will combine automatic annotation with multiscale visual representations to allow events from multipledata streams to be juxtaposed on the same timeline so that co-occurrence, precedence, and other previously invisible patterns can be observed as analysts explore data relationships at multiple temporal and spatial scales. Dynamic lenses and annotation tools hwill provide interactive visualizations and flexible organizations of data.

Our goals are to (1) accelerate analysis by employing vision-based pattern recognition capabilities to pre-segment and tag data records, (2) increase analysis power by visualizing multimodal activity and macro-micro relationships, and coordinating analysis and annotation across multiple scales, and (3) facilitate shared use of our developing framework with collaborators.

The work we propose builds on our long term commitment to understanding cognition “in the wild”, developing multiscale visualizations, and recent experience automatically annotating video of freeway driving. We propose to extend the theory and methods developed in our earlier work and integrate them with new web-based analysis tools to enable more effective analysis of human activity. As initial test domains we will focus on understanding activity in high-fidelity flight simulators and the activity histories of workstation usage and the process of writing. We will also evaluate a novel technique to assist in reinstating the context of earlier activities. Our long range objective is to better understand the dynamics of human activity as a scientific foundation for design.