Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process. It has various applications in information retrieval (IR) and natural language processing (NLP), including text summarisation, topic analysis and document indexing. This paper proposes a simple but effective post-processing-based universal approach to improving the performance of any AKE methods, via an enhanced level of semantic-awareness supported by PoS tagging. To demonstrate the performance of the proposed approach, we considered word types retrieved from a PoS tagging step and two representative sources of semantic information—specialised terms defined in one or more context-dependent thesauri, and named entities in Wikipedia. The above three steps can be simply added to the end of any AKE methods as part of a post-processor, which simply re-evaluates all candidate keywords following some context-specific and semantic-aware criteria. For five state-of-the-art (SOTA) AKE methods, our experimental results with 17 selected datasets showed that the proposed approach improved their performances both consistently (up to 100% in terms of improved cases) and significantly (between 10.2% and 53.8%, with an average of 25.8%, in terms of F1-score and across all five methods), especially when all the three enhancement steps are used. Our results have profound implications considering the fact that our proposed approach can be easily applied to any AKE method with the standard output (candidate keywords and scores) and the ease to further extend it.

Purpose. This paper proposes a unifying conceptual framework for analysing problematic information in order to address persistent terminological fragmentation across disciplines and improve conceptual clarity in research, policy, and practice.Design/methodology/approach. The study synthesises interdisciplinary literature from information science, communication, philosophy, computer science, and policy studies to identify common conceptual gaps. It develops a multidimensional framework structured around three core dimensions, veracity, misleadingness, and harmfulness, complemented by optional contextual attributes such as intent, timeliness, contextuality, provenance, and ambiguity.Findings. The framework demonstrates that many existing terms, including misinformation, disinformation, malinformation, rumours, satire, conspiracy theories, and deepfakes, can be more precisely characterised through explicit dimensional combinations rather than fixed categorical labels. This approach clarifies conceptual boundaries, explains overlaps and disagreements in prior definitions, and provides a systematic method for generating consistent terminology across analytical contexts. The framework also highlights how different dimensions become salient in tasks such as detection, moderation, fact-checking, and regulation.Originality. Unlike prior typologies that emphasise single criteria such as falsity or intent, this framework introduces a structured, extensible dimensional model that separates core properties of information from context-dependent attributes. It contributes a generalisable conceptual architecture designed to support interdisciplinary research and improve definitional precision in the study of problematic information.