Mining social media for open innovation in transportation systems

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.

[1]  S. Cook.  THE CONTRIBUTION REVOLUTION : Letting Volunteers Build Your Business , 2008 .

[2]  Mário J. Silva,et al.  POPmine: Tracking Political Opinion on the Web , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[3]  Gary L. Lilien,et al.  Location, Location, Location: How Network Embeddedness Affects Project Success in Open Source Systems , 2006, Manag. Sci..

[4]  C. Prahalad,et al.  Co-creation experiences: The next practice in value creation , 2004 .

[5]  Djamel Khadraoui,et al.  A transport based clearing system for dynamic carpooling business services , 2011, 2011 11th International Conference on ITS Telecommunications.

[6]  Hajo Hippner,et al.  Crowdsourcing , 2012, Business & Information Systems Engineering.

[7]  C. Prahalad,et al.  Co‐creating unique value with customers , 2004 .

[8]  Fábio Pinto,et al.  POPSTAR at RepLab 2013: Name Ambiguity Resolution on Twitter , 2013, CLEF.

[9]  Pasquale Lops,et al.  Developing Smart Cities Services through Semantic Analysis of Social Streams , 2015, WWW.

[10]  Ponnurangam Kumaraguru,et al.  TweetCred: Real-Time Credibility Assessment of Content on Twitter , 2014, SocInfo.

[11]  Jeff Howe,et al.  Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business , 2008, Human Resource Management International Digest.

[12]  John Cullen,et al.  Democratizing Innovation , 2020, Encyclopedia of Creativity, Invention, Innovation and Entrepreneurship.

[13]  Rosaldo J. F. Rossetti,et al.  Mobility Network Evaluation in the User Perspective: Real-Time Sensing of Traffic Information in Twitter Messages , 2010 .

[14]  Eric Fang.  Customer Participation and the Trade-Off between New Product Innovativeness and Speed to Market , 2008 .

[15]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[16]  A. Payne,et al.  Managing the co-creation of value , 2008 .

[17]  P. Evans,et al.  Collaboration rules , 2005, IEEE Engineering Management Review.

[18]  Zeynab Abbasi Khalifelu,et al.  Analysis and evaluation of unstructured data: text mining versus natural language processing , 2011, 2011 5th International Conference on Application of Information and Communication Technologies (AICT).

[19]  N. Franke,et al.  Testing the Value of Customization: When Do Customers Really Prefer Products Tailored to Their Preferences? , 2009 .

[20]  Eric von Hippel,et al.  Finding Commercially Attractive User Innovations: A Test of Lead User Theory , 2005 .

[21]  Pedro Saleiro,et al.  Sentiment Aggregate Functions for Political Opinion Polling using Microblog Streams , 2016, C3S2E.

[22]  Karl Andersson,et al.  Web-based adaptive application mobility , 2012, 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET).

[23]  Lerzan Aksoy,et al.  Undervalued or Overvalued Customers: Capturing Total Customer Engagement Value , 2010 .

[24]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[25]  V. Karkaletsis,et al.  Passive crowdsourcing in government using social media , 2014 .

[26]  Mary Jo Bitner,et al.  Moving Forward and Making a Difference: Research Priorities for the Science of Service , 2010 .

[27]  C. Adams,et al.  Differentiation through customer involvement in production or delivery , 1993 .

[28]  Ronghui Liu,et al.  Advances in Artificial Transportation Systems and Simulation , 2014 .

[29]  E. Hippel,et al.  Lead users: a source of novel product concepts , 1986 .

[30]  Rosaldo J. F. Rossetti,et al.  TwitterJam: Identification of mobility patterns in urban centers based on tweets , 2015, 2015 IEEE First International Smart Cities Conference (ISC2).

[31]  Rosaldo J. F. Rossetti,et al.  A Cooperative Simulation Framework for Traffic and Transportation Engineering , 2008, CDVE.

[32]  Hassan Artail,et al.  The shared-taxi problem: Formulation and solution methods , 2014 .