Social Search: retrieving information in Online Social Platforms -- A Survey

TL;DR

This survey reviews the current state of Social Search in online social platforms, proposing a new taxonomy, analyzing key approaches, and identifying future research directions to better leverage social information for improved information retrieval.

Contribution

It introduces a new taxonomy for Social Search, analyzes existing approaches, and highlights limitations and open research directions in the field.

Findings

Social features are commonly used to model user preferences.

Current models often lack structured social profile information.

Future research should focus on exploiting detailed social data.

Abstract

Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviors (as they can be inferred from data available on social platforms) to optimize their information needs further.