Seminar: Diversity and Inclusion in the Sharing Economy: An Airbnb Case Study • 27 March 2024

Event organised by the Computational Humanities research group.

To register to the seminar, please fill in this form by Friday 22 March 2024.

27 March 2024 – 5pm GMT

In person – King’s College London (Macadam Building MB-2.1)

Remote – Via Microsoft Teams

Giovanni Quattrone (Middlesex University London), Diversity and Inclusion in the Sharing Economy: An Airbnb Case Study


The sharing economy model is a contested concept: on one hand, its proponents have praised it to be enabler of fair marketplaces, with all participants receiving equal opportunities; on the other hand, its detractors have criticised it for actually exacerbating preexisting societal inequalities. In this paper, we propose a scalable quantitative method to measure participants’ diversity and inclusion in such marketplaces, with the aim to offer evidence to ground this debate. We apply the method to the case of the Airbnb hospitality service for the city of London, UK. Our findings reveal that diversity is high for gender, but not so for age and ethnicity. As for inclusion, we find strong signals of homophily both in terms of gender, age and ethnicity, thus suggesting that under-represented groups have significantly fewer opportunities to gain from this market model. Interestingly, the sentiment associated to same-group (homophilic) interactions is just as positive as that associated to heterophilic ones, even after controlling for Airbnb property’s type, price and location. This suggests that increased diversity and inclusion are desirable not only for moral but also for economic and market reasons.


Dr Giovanni Quattrone is a prominent researcher and expert in the fields of Social Data Science and Urban Science. With a strong background in data-driven analysis and interdisciplinary research, Giovanni has made significant contributions to advancing our understanding of complex social phenomena and urban dynamics.

Giovanni’s expertise lies in leveraging computational methods, such as machine learning and network analysis to gain insights into social interactions and human behaviour across both online and offline domains. Giovanni’s research also encompasses the analysis of urban data, driving the development of data-driven solutions to enhance urban planning, sustainability, and livability. To date, Giovanni has published more than 80 peer-reviewed publications, with over 1900 citations collectively (Source: Google Scholar).

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