Spatio-Temporal Small Worlds for Decentralized Information Retrieval in Social Networking

TL;DR

This paper explores decentralized information retrieval in social networks using spatio-temporal contexts, proposing the concept of Spatio-Temporal Small Worlds to enhance social and semantic cohesion.

Contribution

It introduces the novel concept of Spatio-Temporal Small Worlds for agent-based IR, integrating social, semantic, and spatio-temporal contexts in decentralized social networking.

Findings

Spatio-temporal contexts can unify social and semantic cohesion.

Empirical analysis using Twitter data supports the effectiveness of the approach.

Spatio-Temporal Small Worlds facilitate improved decentralized IR.

Abstract

We discuss foundations and options for alternative, agent-based information retrieval (IR) approaches in Social Networking, especially Decentralized and Mobile Social Networking scenarios. In addition to usual semantic contexts, these approaches make use of long-term social and spatio-temporal contexts in order to satisfy conscious as well as unconscious information needs according to Human IR heuristics. Using a large Twitter dataset, we investigate these approaches and especially investigate the question in how far spatio-temporal contexts can act as a conceptual bracket implicating social and semantic cohesion, giving rise to the concept of Spatio-Temporal Small Worlds.