Spatio-Temporal Small Worlds for Decentralized Information Retrieval in Social Networking
This addresses information retrieval challenges in decentralized and mobile social networking scenarios, but appears incremental as it builds on existing agent-based and contextual methods.
The paper tackled the problem of decentralized information retrieval in social networking by proposing agent-based approaches that incorporate long-term social and spatio-temporal contexts, using a large Twitter dataset to investigate how these contexts create Spatio-Temporal Small Worlds for improved retrieval.
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.