Predicting reputation in the sharing economy with twitter social data

Iglesias Fernandez, Carlos Ángel and Prada, Antonio (2020). Predicting reputation in the sharing economy with twitter social data. "Applied Sciences-Basel", v. 10 (n. 2881); pp. 1-18. ISSN 2076-3417. https://doi.org/10.3390/app10082881.

Description

Title: Predicting reputation in the sharing economy with twitter social data
Author/s:
  • Iglesias Fernandez, Carlos Ángel
  • Prada, Antonio
Item Type: Article
Título de Revista/Publicación: Applied Sciences-Basel
Date: 21 April 2020
ISSN: 2076-3417
Volume: 10
Subjects:
Freetext Keywords: sharing economy; reputation; natural language processing; Wallapop; Twitter
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería de Sistemas Telemáticos [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 63864
DC Identifier: http://oa.upm.es/63864/
OAI Identifier: oai:oa.upm.es:63864
DOI: 10.3390/app10082881
Official URL: https://www.mdpi.com/2076-3417/10/8/2881
Deposited by: Memoria Investigacion
Deposited on: 29 Sep 2020 15:49
Last Modified: 29 Sep 2020 15:49
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