An elaborated model of social search

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Abstract

Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon’s Mechanical Turk service suggest that social interactions play an important role throughout the search process. A second survey of also 150 users, focused instead on difficulties encountered during searches, suggests similar conclusions. These social interactions range from highly coordinated collaborations with shared goals to loosely coordinated collaborations in which only advice is sought. Our main contribution is that we have integrated models from previous work in sensemaking and information-seeking behavior to present a canonical social model of user activities before, during, and after a search episode, suggesting where in the search process both explicitly and implicitly shared information may be valuable to individual searchers.

We seek to situate collaboration in these search episodes in the context of our developed model for social search. We discuss factors that influence social interactions and content sharing during search activities. We also explore the relationship between social interactions, motivations, and query needs. Finally, we introduce preliminary findings from the second survey on difficult and failed search efforts, discussing how query needs and social interactions may differ in cases of search failures.

Introduction

Web search has changed dramatically how we interact with the knowledge of the world. Its success in impacting our everyday lives in the last two decades is perhaps unparalleled. Surprisingly, however, researchers have mostly thought about navigating and browsing for information as a solitary user activity, focusing on eliciting a user’s information needs and improving the relevance of search results.

This view is somewhat in conflict with prior research by library scientists looking at users’ information-seeking habits (Fox et al., 1993, Kuhlthau, 1991, Shepherd, 1983, Twidale et al., 1997, Wilson, 1981). These studies were done by scientists before the wide availability of web search engines, but demonstrated that other individuals may be valuable information resources during searches.

More recently, researchers have observed direct user cooperation during web-based information seeking. Morris (2008) conducted a survey of 209 enterprise users, revealing that nearly half engaged in explicit collaboration on joint search tasks on the web. Collaboration is defined to be the “act of working jointly” (WordNet. Definition of “collaboration”, 2009). So by extension, collaborative search is the act of working jointly on a search problem. Certainly, active collaboration by multiple parties does occur under some circumstance (e.g., enterprise settings); at other times, and perhaps for a greater majority of searches, users may interact with others remotely, asynchronously (Rodden, 1991), and even involuntarily and implicitly.

As we shall see, it is clear that a wide range of collaboration happens in search episodes, including individual searchers who momentarily make use of peers, colleagues, and social resources as part of their otherwise personal search goals. Such episodes represent momentary collaborations in the scope of a larger search task. We are interested in this entire range of collaboration, from loosely coordinated searches, where perhaps only the search result is distributed to others, to highly coordinated searches, where people interact before, during, and after the search episode.

We refer to any search that contains social interactions to be “social search.” The general term “social search” has been applied widely in the field of Web 2.0 to describe searches that:

  • utilize social and expertise networks;

  • are done in shared social workspaces;

  • or involve social data mining or collective intelligence processes to improve the search process.

Our definition of “social search” is intended to be broad, to include a range of possible social interactions and collaborations 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.

Our focus, therefore, is to bring some clarity to how social search occurs in the real world. We analyzed the self-reported search experiences of 150 users from Amazon’s Mechanical Turk, and mapped their complex social activities onto a single, canonical model of the extended search process. As we present this integrated model of social search, we note specifically where social interactions occurred before, during, and after a search event. We support the model with observations from our data by describing: (1) users’ search motivations; (2) their pre-search preparation process (seeking guidance, advice, and clarifications on the information need); (3) how they conducted searches according to those information needs (transactional, navigational, informational); (4) how they shared end results after the search.

We also discuss factors that influence social interactions and content sharing during search activities, and explore the relationship between social interactions, motivations, and query needs. Finally, we introduce preliminary findings from the second survey on difficult and failed search efforts, discussing how query needs and social interactions may differ in cases of search failures.

We present our results in four parts:

  • Part 1: characterization of social search.

  • Part 2: factors of social interaction and content sharing.

  • Part 3: patterns between pre-search and post-search social interactions.

  • Part 4: preliminary results of failed and difficult search data.

In the rest of this paper, we briefly review past research works in this area, describe our survey and data collection procedure, and present the canonical model, both as a diagram and with quantitative support and anecdotal case studies of actual behavior. We conclude with design implications, limitations, and some general remarks.

Section snippets

Related work

Until quite recently, researchers have mostly thought about navigating and browsing for information as a single user activity (Catledge and Pitkow, 1995, Cockburn and Jones, 1996), even among those who developed behavioral models of information seeking (Bates, 2002, Choo et al., 1999, Ellis, 1989, Marchionini, 1995). Ellis’ early work in understanding the behavioral patterns of users as they search for information led to a basic model of information-seeking characterized by six general

Procedure

In this paper, we report on two critical-incident style surveys that we posted on Amazon’s Mechanical Turk. Here, we first describe the methods for the primary survey—a collection of everyday searches. In Part 4, we describe the methods for the secondary survey—a collection of failed and difficult searches.

Mechanical Turk is 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 (Kittur, Chi, & Suh, 2008).

Results

Our main contribution is that we have integrated our findings with models of sensemaking and information seeking from the literature, and we present a canonical model of user activities throughout the search process (Fig. 2, below). We present our results in several parts:

  • 1.

    First, our characterization of social search discusses our model in three phases: before, during, and after a search act. We illustrate each phase with quantitative data and anecdotal case studies of actual, reported user

Discussion

In support of recent findings (Morris, 2008, Twidale et al., 1997), our results demonstrate that users have a strong social inclination throughout the search process, interacting with others for reasons ranging from obligation to curiosity. Self-motivated searchers, users conducting informational searches, and failed or difficult queries provided the most compelling cases for social support during search.

Despite the fact that direct cooperation with others during search was uncommon in our

Conclusion

This study was intended to document the ways in which social interactions play a role in search tasks online. We believe that it complements related work in online collaborative information seeking (e.g., Morris, 2008) that has examined active cooperation during co-located, synchronous searches. Although most of our users were not explicitly collaborating with others in joint search tasks, we have shown how people can be momentarily recruited to collaborate during certain phases of search to

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