# Predicting Reputation in the Sharing Economy with Twitter Social Data
## 1 Introduction  
The increasing adoption of social media is transforming the way we live and do business [1]. As of October 2018, there are more than 2.2 billion monthly active users and 1.5 billion daily active users on Facebook [2]. Other social networks such as Youtube and Twitter have 1900 and 300 million monthly active users, respectively [2]. Social Media interactions, from commenting on a post to liking a photo, leave behind digital traces--voluntary or not--that can be used for mining patterns of individual, group, and societal behaviors [3]. These social data created by users when they interact with social media channels are usually known as _digital footprints_[4]. The full potential of digital footprints for generating insights about users comes from their combination with other sources, such as bank transactions [5], sensor information [6], or other social networks [3]. Several works have used many techniques for user profile matching across social networks, such as user profile similarity [7, 8] as well as based on spatial, temporal, and content similarity [9]. In this work, we address how social media footprints can improve the social markets of the so-called sharing economy. The sharing economy can be defined as _\"Collaborative consumption that takes place in organized systems or networks, in which participants conduct sharing activities in the form of renting, lending, trading, bartering, and swapping of goods, services, transportation solutions, space, or money\"_[10]. The success of the sharing economy lies in connecting individuals who have underutilized assetswith other individuals interested in their use in the short term [11]. The shared assets range from vehicles and spare time to rooms, second-hand objects or spare time for performing everyday tasks. Some of examples of these services are BlaBlaCar ([[http://www.blablacar.com](http://www.blablacar.com)]([http://www.blablacar.com](http://www.blablacar.com))) for long-distance car- sharing, Turo ([[http://turo.com](http://turo.com)]([http://turo.com](http://turo.com))) for short-term car renting, Uber ([[http://www.uber.com](http://www.uber.com)]([http://www.uber.com](http://www.uber.com))) and Cabify ([[http://www.cabify.com](http://www.cabify.com)]([http://www.cabify.com](http://www.cabify.com))) that offer an alternative to taxis, Airbnb ([[http://www.airbnb.com](http://www.airbnb.com)]([http://www.airbnb.com](http://www.airbnb.com)))) that enable sharing rooms and apartmetns, Wallapp ([[http://www.wallapp.com](http://www.wallapp.com)]([http://www.wallapp.com](http://www.wallapp.com))) for second-hand objects, and Fiverr ([[http://www.fiverr.com](http://www.fiverr.com)]([http://www.fiverr.com](http://www.fiverr.com))) for performing everyday tasks [11]. One of the potential barriers for the development of the sharing economy is the high level of trust needed to do potentially dangerous activities with strangers as driving for them or staying at their houses [12]. Several solutions attempt to overcome this problem. Some examples are online reputation systems where users can publicly rate each other after a transaction outcome or insurance-like protections that cover material and monetary risks [13]. Online reputation systems enable buyers and sellers to rate each other after each transaction (e.g., ride or apartment sharing), in the same way as eBay transactions. In this way, they help to build a reputation profile that makes transactions safer and less uncertain [12]. Nonetheless, some authors [14; 15] have pointed out some drawbacks of these systems, such as their manipulation with opinion spam [16] or extremely high averaged ratings [15]. Another drawback is the need for historical data, and therefore the total uncertainty of new users' future behavior, which translates to mistrust from other users of the platform. Such drawbacks could be mitigated by predicting the probability of a user being negatively rated by others. That information could enable preemptive measures to be taken.