Microblog Intelligence: A Framework for Credibility-Oriented Microblog Search, Summarisation and Analytics

Microblogs provide a continuously evolving source of information, but their short, noisy, contextual, and rapidly changing nature makes effective search, exploration, summarisation, and analysis challenging. This paper presents a unified framework for microblog intelligence that integrates credibility-oriented processing, semantic retrieval, faceted exploration, continuous extractive summarisation, topic-evolution analysis, media aggregation, sentiment analysis, and multidimensional analytics within a common architecture. The framework is organised into three functional layers: Credibility-Oriented Processing, Intelligence and Retrieval, and Analytical Intelligence. These layers are supported by a shared processing and representation flow and guided by five design principles: Unified Representation, Credibility-Oriented Processing, Progressive Intelligence, Temporal Intelligence, and Non-Generative Traceability. Facet-Value Pairs (FVPs) provide a structured representation that can be reused across retrieval, faceted exploration, summarisation, and analytical functions. The framework uses bot or suspicious-account detection and provenance information as early credibility-oriented signals; these signals provide filtering and source context but do not establish the factual truth of individual microblog posts. The framework also adopts extractive rather than generative summarisation so that summary content remains directly traceable to the underlying microblog posts. An interface-based case study demonstrates how the integrated architecture supports topic search, faceted refinement, temporal and geographical exploration, sentiment analysis, entity analysis, media exploration, and continuous summarisation. The contribution of the work is primarily architectural and integrative: it coordinates complementary microblog-processing capabilities through shared representations and a defined processing flow. A systematic quantitative evaluation of retrieval effectiveness, credibility filtering, summarisation quality, computational performance, and component-level contribution is identified as future work.