Predicting Reputation in the Sharing Economy with Twitter Social Data

In recent years, the sharing economy has become popular, with outstanding examples such as Airbnb, Uber, or BlaBlaCar, to name a few. In the sharing economy, users provide goods and services in a peer-to-peer scheme and expose themselves to material and personal risks. Thus, an essential component o...

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Bibliographic Details
Published inApplied sciences Vol. 10; no. 8; p. 2881
Main Authors Prada, Antonio, Iglesias, Carlos A.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2020
Subjects
Architecture
Computer science
Economics
Footprints
natural language processing
Prediction models
Product reviews
reputation
Reputations
Semantic web
Sharing economy
Social behavior
Social interactions
Social networks
Social organization
Society
Trust
Twitter
User behavior
User profiles
Wallapop
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Summary:In recent years, the sharing economy has become popular, with outstanding examples such as Airbnb, Uber, or BlaBlaCar, to name a few. In the sharing economy, users provide goods and services in a peer-to-peer scheme and expose themselves to material and personal risks. Thus, an essential component of its success is its capability to build trust among strangers. This goal is achieved usually by creating reputation systems where users rate each other after each transaction. Nevertheless, these systems present challenges such as the lack of information about new users or the reliability of peer ratings. However, users leave their digital footprints on many social networks. These social footprints are used for inferring personal information (e.g., personality and consumer habits) and social behaviors (e.g., flu propagation). This article proposes to advance the state of the art on reputation systems by researching how digital footprints coming from social networks can be used to predict future behaviors on sharing economy platforms. In particular, we have focused on predicting the reputation of users in the second-hand market Wallapop based solely on their users’ Twitter profiles. The main contributions of this research are twofold: (a) a reputation prediction model based on social data; and (b) an anonymized dataset of paired users in the sharing economy site Wallapop and Twitter, which has been collected using the user self-mentioning strategy.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10082881