Offline biases in online platforms: a study of diversity and homophily in Airbnb
Abstract How diverse are sharing economy platforms? Are they fair marketplaces, where all participants operate on a level playing field, or are they large-scale online aggregators of offline human biases? Often portrayed as easy-to-access digital spaces whose participants receive equal opportunities...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-03-01
|
| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1140/epjds/s13688-019-0189-5 |
| _version_ | 1819230031868592128 |
|---|---|
| author | Victoria Koh Weihua Li Giacomo Livan Licia Capra |
| author_facet | Victoria Koh Weihua Li Giacomo Livan Licia Capra |
| author_sort | Victoria Koh |
| collection | DOAJ |
| description | Abstract How diverse are sharing economy platforms? Are they fair marketplaces, where all participants operate on a level playing field, or are they large-scale online aggregators of offline human biases? Often portrayed as easy-to-access digital spaces whose participants receive equal opportunities, such platforms have recently come under fire due to reports of discriminatory behaviours among their users, and have been associated with gentrification phenomena that exacerbate preexisting inequalities along racial lines. In this paper, we focus on the Airbnb sharing economy platform, and analyse the diversity of its user base across five large cities. We find it to be predominantly young, female, and white. Notably, we find this to be true even in cities with a diverse racial composition. We then introduce a method based on the statistical analysis of networks to quantify behaviours of homophily, heterophily and avoidance between Airbnb hosts and guests. Depending on cities and property types, we do find signals of such behaviours relating both to race and gender. We use these findings to provide platform design recommendations, aimed at exposing and possibly reducing the biases we detect, in support of a more inclusive growth of sharing economy platforms. |
| first_indexed | 2024-12-23T11:22:37Z |
| format | Article |
| id | doaj.art-43d0fdb02a6141dba9b0afaf4823682f |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-23T11:22:37Z |
| publishDate | 2019-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-43d0fdb02a6141dba9b0afaf4823682f2022-12-21T17:49:01ZengSpringerOpenEPJ Data Science2193-11272019-03-018111710.1140/epjds/s13688-019-0189-5Offline biases in online platforms: a study of diversity and homophily in AirbnbVictoria Koh0Weihua Li1Giacomo Livan2Licia Capra3Department of Computer Science, University College LondonDepartment of Computer Science, University College LondonDepartment of Computer Science, University College LondonDepartment of Computer Science, University College LondonAbstract How diverse are sharing economy platforms? Are they fair marketplaces, where all participants operate on a level playing field, or are they large-scale online aggregators of offline human biases? Often portrayed as easy-to-access digital spaces whose participants receive equal opportunities, such platforms have recently come under fire due to reports of discriminatory behaviours among their users, and have been associated with gentrification phenomena that exacerbate preexisting inequalities along racial lines. In this paper, we focus on the Airbnb sharing economy platform, and analyse the diversity of its user base across five large cities. We find it to be predominantly young, female, and white. Notably, we find this to be true even in cities with a diverse racial composition. We then introduce a method based on the statistical analysis of networks to quantify behaviours of homophily, heterophily and avoidance between Airbnb hosts and guests. Depending on cities and property types, we do find signals of such behaviours relating both to race and gender. We use these findings to provide platform design recommendations, aimed at exposing and possibly reducing the biases we detect, in support of a more inclusive growth of sharing economy platforms.http://link.springer.com/article/10.1140/epjds/s13688-019-0189-5Sharing EconomySocial NetworksHomophilyOnline User BehaviorStatistical Validation |
| spellingShingle | Victoria Koh Weihua Li Giacomo Livan Licia Capra Offline biases in online platforms: a study of diversity and homophily in Airbnb EPJ Data Science Sharing Economy Social Networks Homophily Online User Behavior Statistical Validation |
| title | Offline biases in online platforms: a study of diversity and homophily in Airbnb |
| title_full | Offline biases in online platforms: a study of diversity and homophily in Airbnb |
| title_fullStr | Offline biases in online platforms: a study of diversity and homophily in Airbnb |
| title_full_unstemmed | Offline biases in online platforms: a study of diversity and homophily in Airbnb |
| title_short | Offline biases in online platforms: a study of diversity and homophily in Airbnb |
| title_sort | offline biases in online platforms a study of diversity and homophily in airbnb |
| topic | Sharing Economy Social Networks Homophily Online User Behavior Statistical Validation |
| url | http://link.springer.com/article/10.1140/epjds/s13688-019-0189-5 |
| work_keys_str_mv | AT victoriakoh offlinebiasesinonlineplatformsastudyofdiversityandhomophilyinairbnb AT weihuali offlinebiasesinonlineplatformsastudyofdiversityandhomophilyinairbnb AT giacomolivan offlinebiasesinonlineplatformsastudyofdiversityandhomophilyinairbnb AT liciacapra offlinebiasesinonlineplatformsastudyofdiversityandhomophilyinairbnb |