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Values (why to conserve) and Attributes (what to conserve) are essentialconcepts of cultural heritage. Recent studies have been using social media tomap values and attributes conveyed by public to cultural heritage. However, itis rare to connect heterogeneous modalities of images, texts, geo-locations,timestamps, and social network structures to mine the semantic and structuralcharacteristics therein. This study presents a methodological workflow forconstructing such multi-modal datasets using posts and images on Flickr forgraph-based machine learning (ML) tasks concerning heritage values andattributes. After data pre-processing using state-of-the-art ML models, themulti-modal information of visual contents and textual semantics are modelledas node features and labels, while their social relationships andspatiotemporal contexts are modelled as links in Multi-Graphs. The workflow istested in three cities containing UNESCO World Heritage properties - Amsterdam,Suzhou, and Venice, which yielded datasets with high consistency forsemi-supervised learning tasks. The entire process is formally described withmathematical notations, ready to be applied in provisional tasks both as MLproblems with technical relevance and as urban/heritage study questions withsocietal interests. This study could also benefit the understanding and mappingof heritage values and attributes for future research in global cases, aimingat inclusive heritage management practices.

Heri-Graphs: A Workflow of Creating Datasets for Multi-modal Machine Learning on Graphs of Heritage Values and Attributes with Social Media