A Site-Level Market Model of the Antiquities Trade

Greenland, Fiona; Marrone, James V.; Topçuoğlu, Oya; Vorderstrasse, Tasha

doi:10.1017/s0940739119000018

Cited by 18 publications

(6 citation statements)

References 31 publications

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“…The results showed that a community of small-scale traders are increasingly making higher profits using social media platforms as they are able to sell human remains. Greenland, Marrone, Topçuoglu, and Vorderstrasse (2019) very recently applied a machine learning technique to estimate the value of antiquities sold so that one can better estimate how much in value looted archaeological sites might be yielding. In the realm of studying the past, social media data have been analyzed using NLP in the formation of identity politics and how archaeological and historical references are integrated in the debate revolving around Brexit, which is the process of the UK leaving the European Union (Bonacchi, Altaweel, & Krzyzanska, 2018).…”

Section: Online Cultural Heritagementioning

confidence: 99%

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

Altaweel

2019

Social Science Computer Review

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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.

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Section: Online Cultural Heritagementioning

confidence: 99%

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

Altaweel

2019

Social Science Computer Review

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“…The results showed a relatively large portion of human remains are sold by a relatively small circle of sellers. Machine learning methods have also been applied, in particular recently by Greenland et al (2019), who used looted areas to estimate the value of antiquities sold on the illegal heritage market. In fact, this might be the only study to use machine learning-based approaches to study the illegal antiquities market from source sites where objects are looted.…”

Section: Antiquities Heritage and Online Sourcesmentioning

confidence: 99%

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

Altaweel

Hadjitofi²

2020

Big Data & Society

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The marketisation of heritage has been a major topic of interest among heritage specialists studying how the online marketplace shapes sales. Missing from that debate is a large-scale analysis seeking to understand market trends on popular selling platforms such as eBay. Sites such as eBay can inform what heritage items are of interest to the wider public, and thus what is potentially of greater cultural value, while also demonstrating monetary value trends. To better understand the sale of heritage on eBay’s international site, this work applies named entity recognition using conditional random fields, a method within natural language processing, and word dictionaries that inform on market trends. The methods demonstrate how Western markets, particularly the US and UK, have dominated sales for different cultures. Roman, Egyptian, Viking (Norse/Dane) and Near East objects are sold the most. Surprisingly, Cyprus and Egypt, two countries with relatively strict prohibition against the sale of heritage items, make the top 10 selling countries on eBay. Objects such as jewellery, statues and figurines, and religious items sell in relatively greater numbers, while masks and vessels (e.g. vases) sell at generally higher prices. Metal, stone and terracotta are commonly sold materials. More rare materials, such as those made of ivory, papyrus or wood, have relatively higher prices. Few sellers dominate the market, where in some months 40% of sales are controlled by the top 10 sellers. The tool used for the study is freely provided, demonstrating benefits in an automated approach to understanding sale trends.

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“…Scholars from different fields attempted to demystify the invisible impacts involved along with the evolution of cultural heritage looting. From the tourism management perspective, it tends to be an essential part of a global organized crime that created an active illegal market (Bowman, 2008;Loureiro et al 10.3389/fpsyg.2022.944250 Campbell, 2013;Greenland et al, 2019). This global issue has established a barrier for many governments, local authorities, and international organizations to restrain its rapid growth due to its striking trades of looted antiquities.…”

Section: Introductionmentioning

confidence: 99%

Culture, heritage looting, and tourism: A text mining review approach

et al. 2022

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Tourism scholars have been devoted to exploring the significance of cultural heritage in generating economic, environmental, and social values. However, limited efforts were found to verify potential threats that demolish these values such as looting issue in the global heritage tourism industry. Therefore, this study has reviewed extant publications to demonstrate the potential emerged textual clusters discussed by previous studies. It also summarized the network distribution of articles journals and authors’ affiliations to capture the mobility and diversity with a focus on the business and tourism management field. Hence, the core clusters discovered were related to heritage destruction, public access, world heritage, human rights, cultural heritage preservation, and protection of cultural heritage in the event. The results have established theoretical insights and research agendas for future tourism studies, while it determined critical drawbacks in employing technology tools including virtual reality, augmented reality, and artificial intelligence for cultural heritage preservation/protection.

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A Site-Level Market Model of the Antiquities Trade

Cited by 18 publications

References 31 publications

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

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

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

Culture, heritage looting, and tourism: A text mining review approach

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