Predicting Reputation in the Sharing Economy with Twitter Social Data

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.

Young Consumers’ Intention to Participate in the Sharing Economy: An Integrated Model

“…Trust is also a fundamental variable in the online and insecure context of commerce and tourism carried out by young consumers through shared economy platforms [111,112]. Trust is a subjective feeling about how the supplier will deliver as promised [113,114]. More specifically, trust refers to the positive expectations of the consumer about the supplier's competency, (effective professional), benevolent (good behavior) and honest (integrity) conduct through an online platform [95,115].…”

The Stranger Factor: How Familiarity Influences Sharing Behaviour across Generations

“…The research mentions the so-called digital identity, i.e., the information one provides about oneself in the online environment. This information can be important for so-called reputation systems, which platforms can use to evaluate participants in the sharing economy and predict their future behaviour (Prada & Iglesias, 2020). Interestingly, research by Mittendorf (2016) suggests that it is not only the activity of platforms to address this issue that is important for building trust but also the disposition to trust that sharing economy participants have.…”

Bibliometrics-based visualization analysis of knowledge-based economy and implications to environmental, social and governance (ESG)

“…As the microfoundations of society, people participate in the sharing economy (Lee et al, 2018). A shared economy is inseparable from the sharing of personal information to some extent on social media like Twitter (Prada and Iglesias, 2020). Such rich information may become a supplement to ESG information disclosure, including the working hours of employees, the safety of the working environment, the standardization and equality of employment, the employment opportunities provided to the community, the medical (Lokuwaduge and Heenetigala, 2017) care policies of employees, etc.…”