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The Social Internet of Things (SIoT) paradigm incorporates social networking concepts with the Internet of Things (IoT) solutions to support novel services. The massive amount of data (big data) produced by SIoT necessitates efficient information processing frameworks to exploit social relationships and comprehend actionable information from real-world observations. Data from AI-enabled sensors (AIS) is typically geo-tagged, thus demanding geo-textual processing for information retrieval and analysis. Social media applications are the main source of geo-textual data as mobile users connect with millions of posts daily. The processing of big geo-textual data requires resource-efficient algorithms and frameworks. Clustering algorithms are often applied to geo-textual data to examine spatial, textual, and temporal information for event detection, sentiment analysis, and search query response. Clustering algorithms …