Towards a Model of Understanding Social Search (0908.0595v1)

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. 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 search, suggesting where in the search process even implicitly shared information may be valuable to individual searchers.

• The paper presents a canonical model that reveals how social interactions fundamentally influence search behaviors across all phases of the search process.
• It employs a critical-incident survey with 150 Mechanical Turk participants to gather qualitative data on social contexts in information seeking.
• Key findings demonstrate that social engagement boosts search effectiveness, with up to 59.3% of exploratory search acts benefiting from interactive collaboration.

Understanding Social Search: An Expert Analysis

The paper "Towards a Model of Understanding Social Search" by Brynn M. Evans and Ed H. Chi presents a nuanced exploration of how social interactions are interwoven with the process of information seeking. It challenges the prevailing perception that search activities are inherently solitary and introduces a canonical model that integrates the social dimensions of searching.

Context and Methodology

While traditional models of web searching emphasize individual activity, this paper employs a critical-incident survey with 150 participants from Amazon's Mechanical Turk to examine how social interactions influence search behaviors. The researchers gathered qualitative data on the social context and motivations behind users' search activities. This method is pivotal in moving beyond the solitary search paradigm, encompassing both explicit collaborative efforts and implicit social influences during information seeking.

Survey Results and Model Phases

The paper categorizes the search process into three main phases: before, during, and after the search act. Each phase is delineated with respect to how social interactions occur and influence outcomes:

1. Before Search: Information needs are framed and refined with social interactions playing a significant role in establishing search requirements. Notably, 31.3% of participants searched based on external requests, while 42% engaged in social exchanges to refine search inquiries.
2. During Search: The research identifies three types of search—transactional, navigational, and informational—with social interactions contributing primarily in informational searches. Here, 59.3% of the acts were exploratory in nature, benefiting significantly from social inputs. The paper highlights that these interactions during the search process aid in schema development and enhancing result accuracy.
3. After Search: Post-search activities often involve organization and distribution of information, where social interactions facilitate validation and sharing of results. It was observed that 58.7% of the users shared their findings, with significant engagement occurring in self-initiated searches (47.5%).

Implications and Future Directions

The canonical model proposed by Evans and Chi underscores the critical role of social interactions in enhancing both the efficiency and effectiveness of search tasks. It suggests system designs that could incorporate features enabling social engagement, such as real-time feedback loops, integrated messaging systems, and community-driven insight mechanisms. This research not only prompts reevaluation of current search engine designs but also contributes to the broader discourse on collaborative information seeking, with implications for fields like Human-Computer Interaction and social network analysis.

Conclusions

This investigation into social search processes represents a significant step towards developing a comprehensive understanding of how human interactions inform and improve information seeking behaviors. The paper's findings advance theoretical models of sensemaking and offer practical insights for enhancing search technologies by leveraging social dynamics. Future research could further elucidate how different social contexts and technologies mediate these interactions, potentially creating more robust, socially-aware search tools.