On the Role of Political Affiliation in Human Perception The Case of Delhi OddEven Experiment

  • Tahar ZanoudaEmail author
  • Sofiane Abbar
  • Laure Berti-Equille
  • Kushal Shah
  • Abdelkader Baggag
  • Sanjay Chawla
  • Jaideep Srivastava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


In an effort to curb air pollution, the city of Delhi (India), known to be one of the most populated, polluted, and congested cities in the world has run a trial experiment in two phases of 15 days intervals. During the experiment, most of four-wheeled vehicles were constrained to move on alternate days based on whether their plate numbers ended with odd or even digits. While the local government of Delhi represented by A. Kejriwal (leader of AAP party) advocated for the benefits of the experiment, the prime minister of India, N. Modi (former leader of BJP) defended the inefficiency of the initiative. This later has led to a strong polarization of public opinion towards OddEven experiment. This real-world urban experiment provided the scientific community with a unique opportunity to study the impact of political leaning on humans perception at a large-scale. We collect data about pollution and traffic congestion to measure the real effectiveness of the experiment. We use Twitter to capture the public discourse about the experiment in order to study people’s opinion within different dimensions: time, location, and topics. Our results reveal a strong influence of political affiliation on how people perceived the outcomes of the experiment. For instance, AAP supporters were significantly more enthusiastic about the success of OddEven compared to BJP supporters. However, taking into account location of people revealed that personal experience is able to overcome political bias.


Urban big data analytics Urban policy making Computational social science Political science 


  1. 1.
    Delhi government’s notification (2015).
  2. 2.
  3. 3.
    Oddeven official notification (2016).
  4. 4.
    Barber, P.: Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Polit. Anal. 23(1), 76–91 (2015)CrossRefGoogle Scholar
  5. 5.
    Barberá, P., Jost, J.T., Nagler, J., Tucker, J.A., Bonneau, R.: Tweeting from left to right: Is online political communication more than an echo chamber? Psychol. Sci. 26, 1531–1542 (2015)CrossRefGoogle Scholar
  6. 6.
    Barberá, P., Rivero, G.: Understanding the political representativeness of Twitter users. Soc. Sci. Comput. Rev. 33, 712–729 (2014). 0894439314558836CrossRefGoogle Scholar
  7. 7.
    Cohen, R., Ruths, D.: Classifying political orientation on Twitter: it’s not easy!. In: Proceedings of ICWSM 2013 (2013)Google Scholar
  8. 8.
    Colleoni, E., Rozza, A., Arvidsson, A.: Echo chamber or public sphere? predicting political orientation and measuring political homophily in Twitter using big data. J. Commun. 64(2), 317–332 (2014)CrossRefGoogle Scholar
  9. 9.
    Conover, M.D., Gonçalves, B., Ratkiewicz, J., Flammini, A., Menczer, F.: Predicting the political alignment of Twitter users. In: Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), pp. 192–199 (2011)Google Scholar
  10. 10.
    Dodds, P.S., Clark, E.M., Desu, S., Frank, M.R., Reagan, A.J., Williams, J.R., Mitchell, L., Harris, K.D., Kloumann, I.M., Bagrow, J.P., et al.: Human language reveals a universal positivity bias. Proc. Natl. Acad. Sci. 112(8), 2389–2394 (2015)CrossRefGoogle Scholar
  11. 11.
    Fowler, J.H., Heaney, M.T., Nickerson, D.W., Padgett, J.F., Sinclair, B.: Causality in political networks. Am. Polit. Res. 39(2), 437–480 (2011)CrossRefGoogle Scholar
  12. 12.
    Gehl, J.: Cities for People. Island press, Washington, D.C. (2013)Google Scholar
  13. 13.
    Golbeck, J., Hansen, D.: A method for computing political preference among Twitter followers. Soc. Netw. 36, 177–184 (2014)CrossRefGoogle Scholar
  14. 14.
    Himelboim, I., McCreery, S., Smith, M.: Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on Twitter. J. Comput.-Med. Commun. 18(2), 40–60 (2013)CrossRefGoogle Scholar
  15. 15.
    Kumar, P., Gulia, S., Harrison, R.M., Khare, M.: The influence of odd-even car trial on fine and coarse particles in delhi. Environ. Pollut. 225, 20–30 (2017)CrossRefGoogle Scholar
  16. 16.
    Makazhanov, A., Rafiei, D., Waqar, M.: Predicting political preference of Twitter users. Soc. Netw. Anal. Min. 4(1), 1–15 (2014)CrossRefGoogle Scholar
  17. 17.
    Pang, B., Lee, L., et al.: Opinion mining and sentiment analysis. Found. Trends\({\textregistered }\) Inf. Retr. 2(1–2), 1–135 (2008)Google Scholar
  18. 18.
    Sehgal, M., Gautam, S.K.: Odd even story of delhi traffic and air pollution. Int. J. Environ. Stud. 73(2), 170–172 (2016)CrossRefGoogle Scholar
  19. 19.
    Sharma, S., Malik, J., Suresh, R., Ghosh, P.: Analysis of odd-even scheme-full report. TERIIN Institute (2016).
  20. 20.
    Singh, S.K.: Scenario of Urban transport in Indian cities: challenges and the way forward. In: Dev, S.M., Yedla, S. (eds.) Cities and Sustainability. SPBE, pp. 81–111. Springer, New Delhi (2015). doi: 10.1007/978-81-322-2310-8_5 CrossRefGoogle Scholar
  21. 21.
    Singhania, K., Girish, G., Vincent, E.N.: Impact of odd-even rationing of vehicular movement in Delhi on air pollution levels. Low Carbon Econ. 7(04), 151 (2016)CrossRefGoogle Scholar
  22. 22.
    Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1397–1405. ACM (2011)Google Scholar
  23. 23.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inform. Sci. Technol. 61(12), 2544–2558 (2010)CrossRefGoogle Scholar
  24. 24.
    WHO: WHO Global Urban Ambient Air Pollution Database (2014).
  25. 25.
    Zheng, Y., Liu, F., Hsieh, H.P.: U-air: when urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1436–1444. ACM (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Tahar Zanouda
    • 1
    Email author
  • Sofiane Abbar
    • 1
  • Laure Berti-Equille
    • 1
  • Kushal Shah
    • 1
  • Abdelkader Baggag
    • 1
  • Sanjay Chawla
    • 1
  • Jaideep Srivastava
    • 1
  1. 1.Qatar Computing Research Institute, HBKUDohaQatar

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