The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind. Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL, and yet a complex task, even for experts, since the selection criteria of OUV are not mutually exclusive. Furthermore, manual annotation of heritage values and attributes from multi-source textual data, which is currently dominant in heritage studies, is knowledge-demanding and timeconsuming, impeding systematic analysis of such authoritative documents in terms of their implications on heritage management. This study applies state-of-the-art NLP models to build a classifier on a new dataset containing Statements of OUV, seeking an explainable and scalable automation tool to facilitate the nomination, evaluation, research, and monitoring processes of World Heritage sites. Label smoothing is innovatively adapted to improve the model performance by adding prior interclass relationship knowledge to generate soft labels. The study shows that the best models fine-tuned from BERT and ULMFiT can reach 94.3% top-3 accuracy. A human study with expert evaluation on the model prediction shows that the models are sufficiently generalizable. The study is promising to be further developed and applied in heritage research and practice. 1

Abstract. Social inclusion has grown as an important goal for heritage planning over the past decades. Whilst the document Recommendation on the Historic Urban Landscape called a decade ago for novel tools for civic engagement and knowledge documentation, social media already functions as a platform for online communities to actively get involved in heritage-related activities by sharing their ideas. Especially when radical events occur around heritage properties, either positive or negative, emotions and opinions would spread rapidly across the globe via the internet to reach online communities of interested or concerned citizens. This paper presents a theoretical framework defined to classify social inclusion of online communities in heritage planning processes through differentiating the everyday baseline scenarios from the event-triggered activated ones. A preliminary systematic literature review shows that research integrating and comparing both scenarios is still scarce, and that specific tools and algorithms to handle large datasets are needed to identify the structure of communication networks underpinning the spread of information on social media. This framework is the first step on future research to investigate the different focal attention points, mechanisms, and patterns of social inclusion of online communities in heritage planning, towards transforming it to a more socially inclusive practice.

Values (why to conserve) and Attributes (what to conserve) are essential concepts of cultural heritage. Recent studies have been using social media to map values and attributes conveyed by the public to cultural heritage. However, it is rare to connect heterogeneous modalities of images, texts, geo-locations, timestamps, and social network structures to mine the semantic and structural characteristics therein. This study presents a methodological [d=AR]frameworkworkflow for constructing such multi-modal datasets using posts and images on Flickr for graph-based machine learning (ML) tasks concerning heritage values and attributes. After data pre-processing using [d=AR]pre-trainedstate-of-the-art ML models, the multi-modal information of visual contents and textual semantics are modelled as node features and labels, while their social relationships and spatio-temporal contexts are modelled as links in Multi-Graphs. The [d=AR]frameworkworkflow is tested in three cities containing UNESCO World Heritage properties—Amsterdam, Suzhou, and Venice— which yielded datasets with high consistency for semi-supervised learning tasks. The entire process is formally described with mathematical notations, ready to be applied in provisional tasks both as ML problems with technical relevance and as urban/heritage study questions with societal interests. This study could also benefit the understanding and mapping of heritage values and attributes for future research in global cases, aiming at inclusive heritage management practices. Moreover, the proposed framework could be summarized as creating attributed graphs from unstructured social media data sources, ready to be applied in a wide range of use cases.

Abstract. The Statements of Outstanding Universal Value (OUV) concerns the core justification for nominating and inscribing cultural and natural heritage properties on the UNESCO World Heritage List, ever since 2007. Ten criteria are specified and measured independently for the selection process. The 2008 ICOMOS Report “What is OUV” has been a successful example to interpret OUV as an integral concept by inspecting the associations of the selection criteria in all inscribed properties. This paper presents a novel methodology for interpreting OUV using computational techniques of Natural Language Processing, Machine Learning, and Graph Visualization. Firstly, frequent phrases appearing in Statements of OUV are used to construct a lexicon for each selection criterion; Secondly, three similarity matrices are constructed as graphs to represent the pair-wise associations of the criteria; Lastly, the lexicon and graphs are visualized in 2D. The study shows that the lexicon derived from computational techniques can capture the essential concepts of OUV, and that the selection criteria are consistently associated with each other in different similarity metrics. This study provides a quantitative and qualitative interpretation of the Statements of OUV and the associations of selection criteria, which can be seen as an elaborated computational extension of the 2008 Report, useful for future inscription and evaluation process of World Heritage nominations.

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