Alia I. Abdelmoty

Live social audio platforms such as X Spaces enable conversations to evolve beyond their creator-defined thematic intent, challenging the reliability of static metadata labels for representing real-time discourse. This study investigates conversational drift and thematic stability through a duration-weighted framework combining semantic segmentation, topic classification, and thematic aggregation. Analysing 335 X Spaces sessions comprising approximately 566 hours of conversational audio, we quantify the alignment between intended topics and emergent discussion content based on aggregate thematic duration. The findings suggest that conversational drift follows structured, topic-dependent patterns rather than random disruption. Although off-topic discussion occupied a substantial proportion of conversational time, drift frequently remained semantically adjacent to the intended scope, indicating gradual thematic expansion rather than complete topical deviation. Several recurrent thematic transition patterns were observed between niche technical discussions and broader interpersonal categories. These findings highlight the limitations of static creator-assigned metadata for representing evolving live discourse within synchronous social audio environments. More broadly, they demonstrate the importance of duration-aware approaches for content discovery, recommendation, and moderation in environments where dominant conversational themes may diverge from their originally intended scope.

Humans typically describe spatial location using names, hierarchies, and relative positions (e.g., east of, inside), yet mainstream GIS represents places primarily through geometric coordinates, rendering qualitative spatial queries computationally challenging. We introduce the Qualitative Place Model (QPM), a GIS-native framework that transforms standard boundary datasets and place layers into structured knowledge bases of Qualitative Place Description (QPD). QPM provides a homogeneous representation whereby administrative units and physical places are treated uniformly as Place entities. The model materializes a compact set of local relations, hierarchical containment, directional neighbourhood, and optional proximity, that support rich inferences through sound logical operations (inverse relationships and per-predicate transitive closure). We implement QPM as an ArcGIS Pro toolbox that computes and persists QPDs within a geodatabase, with optional RDF export for SPARQL querying. This implementation enables natural-language-style spatial queries such as “Where is x?” or “Which places are north of x?” within standard GIS workflows. Evaluation on Wales (UK) administrative, postal, and electoral hierarchies plus a comprehensive place layer demonstrates robust performance: QPM generated 95.8% of expected basic-place statements (52,821 places) and achieved 89.7–96.5% coverage across administrative hierarchies. All QPDs proved unique under our deterministic signature. Despite compact storage requirements, directional relations expand by more than an order of magnitude (10.6× overall expansion) under logical closure, demonstrating substantial inferential power from a minimal stored representation. QPM complements geometric GIS with an explainable qualitative layer that aligns with human spatial cognition while remaining fully operational within standard GIS environments.

The rise of audio-based platforms such as X Spaces introduces synchronous, ephemeral modes of interaction that differ from traditional text-based social media. This study investigates user connectivity and thematic engagement by analysing a substantial dataset of X Spaces events. We model participation as a bipartite user-Space network, projected into user-user connections to assess community structure and interaction patterns. Our network analysis confirms broad-scale network characteristics and provides novel insights into local structures and user influence. Beyond structural analysis, we examine how users engage thematically across modalities by comparing creator-assigned topics, conversation-derived topics extracted from transcribed audio summaries, and textual posts. Our results reveal that user interests are only weakly aligned across modalities, indicating distinct communicative roles for audio and text. We also introduce a hybrid method combining BERT embeddings, spaCy similarity, and expert validation to assess the alignment between creator-assigned topics and actual conversation content. While most Spaces exhibit high topic coverage, 44% introduce additional themes, suggesting that live audio conversations often diverge from their predefined scope. These findings contribute to understanding interest expression, structural cohesion, and topical drift in emerging audio-based social platforms.

Administrative geography is concerned with the hierarchy of areas related to national and local government in a country. They form an important dataset in the country’s open data provision and act as the geo-referencing backdrop for many types of geospatial data. Proprietary ontologies are built to model and represent these data with little focus on spatial semantics. Studying the quality of these ontologies and developing methods for their evaluation are needed. This paper addresses these problems by studying the spatial semantics of administrative geography data and proposes a uniform set of qualitative semantics that encapsulates the inherent spatial structure of the administrative divisions and allows for the application of spatial reasoning. Topological and proximity semantics are defined and combined into a single measure of spatial completeness and used for defining a set of competency questions to be used in the evaluation process. The significance of the novel measure of completeness and competency questions is demonstrated on four prominent real world administrative geography ontologies. It is shown how these can provide an objective measure of quality of the geospatial ontologies and gaps in their definition. The proposed approach to defining spatial completeness complements the established methods in the literature, that primarily focus on the syntactical and structural dimensions of the ontologies, and offers a novel approach to ontology evaluation in the geospatial domain.

Users' awareness of the extent of information implicit in their geo-profiles on social networks is limited. This questions the validity of their consent to the collection, storage and use of their data. Tools for location privacy awareness are needed that provide users with accessible means for understanding the implicit content in their location information as well as a view of the level of risk to their privacy as a consequence of disclosing this information. Towards this goal, an abstract model of location privacy threat levels is first derived from a user study involving 186 users. This is then used to inform the design of a prototype privacy feedback tool for a location-based social network. Another user study involving 338 users of this network is carried out to test the effectiveness of the proposed design. Findings confirm the strong need of users for more transparent access to and control over their location profiles and guide the proposal of recommendations to the design of more privacy-sensitive geo-social networks.