The Market for Heritage: Evidence From eBay Using Natural Language Processing

Abstract

The trade in antiquities and cultural objects has proven difficult to understand and yet is highly dynamic. Currently, there are few computational tools that allow researchers to systematically understand the nature of the legal market, which can also potentially provide insights into the illegal market such as types of objects traded and countries trading antiquities. Online sales in antiquities and cultural objects are often unstructured data; relevant cultural affiliations, types, and materials for objects are important for distinguishing what might sell, but these data are rarely organized in a format that makes the quantification of sales a simple process. Additionally, sale locations and the total value of sales are relevant to understanding the focus and size of the market. These data all provide potentially useful insights into how the market in antiquities and cultural objects is developing. Based on this, this work presents the results of a machine learning approach using natural language processing and dictionary-based searches that investigate relatively low-end but high sales volume objects sold on eBay’s U.S. site, where sales are often international, between October 2018 and May 2019. The use of named entity recognition, using a conditional random field approach, classifies objects based on the cultures in which they come from, what type of objects they are, and what the objects are made of. The results indicate that objects from the United Kingdom, affiliated with the Roman period, mostly constituting jewelry, and made of metals sell the most. Metal and jewelry objects, in fact, sold more than other object types. Other important countries for selling ancient and cultural objects include the United States, Thailand, Germany, and Cyprus. Some countries appear to more greatly sellspecific types of objects, such as Egypt being a leader in selling Islamic, terracotta, stone, and wood artifacts and Germany selling Viking/early Medieval weapons. Overall, the approach and tool used demonstrate that it is possible to monitor the online antiquities and cultural objects market while potentially gaining useful insights into the market. The tool developed is provided as part of this work so that it can be applied for other cases and online sites, where it can be applied in real time or using historical data.

The sale of heritage on eBay: Market trends and cultural value

“…To address this, some researchers have applied semi-supervised techniques as well as weighing techniques that can balance category selection for terms (Zafarian et al, 2015). Other methods also include term dictionary searches that match and designate given terms to a category using a domain-specific search (Altaweel, 2019;McCallum and Li, 2003;Wang et al, 2017; e.g., axe designated as a 'weapon', which is a more general categorisation). Overall, NER and CRF have not been extensively applied in studying the antiquities market.…”

“…Scraped data recovered from eBay sales information includes: the date when an object is sold, the US dollar value the item was sold for, description of the object, location of the seller and seller username (collected from 8 July 2019). The collection of seller-related data is new in this work relative to the earlier work; the data given here is anonymised (Altaweel, 2019). All of the data, with the exception of the object descriptions, is structured.…”

“…The spellings used in the category designations given in the results are UK spellings, although both US and UK spelling standards are utilised in training and categorising terms. In a deviation from Altaweel (2019), this work assesses deliberately misspelled words and corrects misspellings given by the seller in the NER, considering the outputs of both as possible for categorisation. In other words, misspellings are incorporated as part of the analysis, while words that can be corrected to proper spelling are also corrected before they are then analysed in NER.…”

“…Part of this presentation includes discussion on the use of computational and NLP methods in understanding heritage along with a general background on NER. The method applied in this work is then introduced, building on earlier work from Altaweel (2019). The results are then presented with the subsequent discussion and conclusion focused on insights obtained on the antiquities market and how that may reflect wider cultural interests and values that shape the heritage market.…”

Towards a Digital Sensorial Archaeology as an Experiment in Distant Viewing of the Trade in Human Remains on Instagram

“…In recent years, work led by researchers affiliated with the Trafficking Culture project and others [2,3] have applied criminological and ethnographic lenses to the broader trade in antiquities. Research by Tsirogiannis and Tsirogiannis [4] have developed and applied novel techniques from network analysis to 'fill in the gaps' in our understanding of how antiquities move from source to consumption points; work by Hardy, Al-Azm and Paul, and Altaweel [5][6][7] have shone needed light on various social networks as platforms for buying and selling antiquities. Our own prior research [8][9][10] has looked at the patterns of discourse in tens of thousands of Instagram posts, and what people involved in the trade in human remains say they are doing, in their posts.…”