Mining Social Media for Open Innovation in Transportation Systems

TL;DR

This paper introduces a framework that leverages social media content analysis to support open innovation in transportation, demonstrated through a case study involving Uber and a major event.

Contribution

It presents a novel framework combining social media data extraction, text processing, and sentiment analysis for open innovation in transportation systems.

Findings

Increased tweet volume during a controversial event without affecting Uber's image.

Social media analysis revealed significant diffusion of Uber's product during the event.

The framework effectively captures user perception and product reception from social media data.

Abstract

This work proposes a novel framework for the development of new products and services in transportation through an open innovation approach based on automatic content analysis of social media data. The framework is able to extract users comments from Online Social Networks (OSN), to process and analyze text through information extraction and sentiment analysis techniques to obtain relevant information about product reception on the market. A use case was developed using the mobile application Uber, which is today one of the fastest growing technology companies in the world. We measured how a controversial, highly diffused event influences the volume of tweets about Uber and the perception of its users. While there is no change in the image of Uber, a large increase in the number of tweets mentioning the company is observed, which meant a free and important diffusion of its product.