Skip to main content
SpringerLink
Log in

Non-fear-Based Road Safety Campaign as a Community Service: Contexts from Social Media

  • Conference paper
  • First Online:
Innovations for Community Services (I4CS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1139))

Included in the following conference series:

Abstract

Traffic crash is a critical health hazard throughout the world. Traffic safety campaigns are important in increasing behavioral safety. Social media makes safety campaigns convenient due to its greater accessibility compared to mass media. Most road safety campaigns are fear-based. There is a need to use these campaigns carefully to reach a wider audience. Non-fear-based safety campaigns are limited in number, and their impact is significant in changing public attitudes towards safety. This study collected YouTube comment data from two non-fear-based safety campaigns and compared their impacts by using natural language processing tools. The findings of this study can help policymakers in understanding public perception and determining appropriate measures to improve road user behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. World Health Organization: International Classification of Diseases – 10th Revision. Geneva, World Health Organization (1990)

    Google Scholar 

  2. World Health Organization: Global Status Report on Road Safety 2018. Geneva, World Health Organization (2018)

    Google Scholar 

  3. YouTube: YouTube for press. https://www.youtube.com/yt/press/statistics.html. Accessed 29 Aug 2019

  4. Wundersitz, L., Hutchinson, T.: Road safety advertising and social marketing. J. Australas. Coll. Road Saf. 22(4), 34–40 (2011)

    Google Scholar 

  5. Stafford, P.: Top social media campaigns and what you can learn from them. http://www.smartcompany.com.au/advertising-and-marketing/20110113-top-social-mediacampaigns-what-you-can-learn-from-them.html. Accessed 29 Aug 2019

  6. Homer, P., Yoon, S.: Message framing and the interrelationships among ad-based feelings, affect and cognition. J. Advertising 21(1), 19–33 (1992)

    Article  Google Scholar 

  7. Watsford, R., Roads, N.S.W.: The success of the Pinkie campaign ‘Speeding. No one thinks big of you’: a new approach to road safety marketing. In: Annual Conference of the Australasian College of Road Safety, High Risk Road Users-Motivating Behavior Change: What Works and What Doesn’t Work, pp. 18–19 (2008)

    Google Scholar 

  8. Smart, W., et al.: The Newell Highway road safety review. In: Australasian Road Safety Research, Policing and Education conference, New South Wales (2009)

    Google Scholar 

  9. Elliott, B.: Road Safety Mass Media Campaigns: A Meta-Analysis. Federal Office of Road Safety, Canberra (1993)

    Google Scholar 

  10. Delhomme, P., De Dobbeleer, W., Forward, S., Simões, A.: Manual for designing, implementing, and evaluating road safety communication campaigns: Part I. In: Campaigns and Awareness Raising Strategies in Traffic Safety, Institut Belge pour la Sécurité Routière (IBSR) (2009)

    Google Scholar 

  11. Rogers, R.W.: Cognitive and physiological processes in fear appeals and attitude change: a revised theory of protection motivation. In: Cacioppo, J.T., Petty, R.E. (eds.) Social Psychophysiology, Guilford, New York, NY, pp. 153–176 (1983)

    Google Scholar 

  12. Cismaru, M., Lavack, A.M., Markewich, E.: Social marketing campaigns aimed at preventing drunk driving: a review and recommendations. Int. Market. Rev. 26(3), 292–311 (2009)

    Article  Google Scholar 

  13. Wundersitz, L.N., Hutchinson, T.P., Woolley, J.E.: Best practice in road safety mass media campaigns: A literature review. Centre for Automotive Safety Research. Adelaide, Australia (2010)

    Google Scholar 

  14. SWOV: SWOV Fact sheet: Fear-based information campaigns. Leidschendam, the Netherlands, Institute for Road Safety Research (2009)

    Google Scholar 

  15. Brennan, L., Binney, W.: Fear, guilt, and shame appeals I social marketing. J. Bus. Res. 63, 140–146 (2010)

    Article  Google Scholar 

  16. Hastings, G., Stead, M., Webb, J.: Fear appeals in social marketing: strategic and ethical reasons for concerns. Psychol. Market. 21, 961–986 (2004)

    Article  Google Scholar 

  17. Laroche, M., Toffoli, R., Zhang, Q., Pons, F.: A cross-cultural study of the persuasive effect of fear appeal messages in cigarette advertising: China and Canada. Int. J. Advertising 20, 297–317 (2001)

    Article  Google Scholar 

  18. Meneses, G.D.: Refuting fear in heuristics and in recycling promotion. J. Bus. Res. 63, 104–109 (2010)

    Article  Google Scholar 

  19. Mowen, J.C., Harris, E.G., Bone, S.A.: Personality traits and fear response to print advertisements: theory and an empirical study. Psychol. Market. 21, 927–943 (2004)

    Article  Google Scholar 

  20. Rossiter, J.R., Thornton, J.: Fear-pattern analysis supports the fear-drive model for anti-speeding and road safety TV ads. Psychol. Market. 21, 45–60 (2004)

    Google Scholar 

  21. Arnett, J.J.: Optimistic bias in adolescent and adult smokers and nonsmokers. Addict. Behav. 25, 625–632 (2000)

    Article  Google Scholar 

  22. Kempf, D.S., Harmon, S.K.: Examining the effectiveness of proposed cigarette package warning labels with graphic images among U.S. college students. Acad. Market. Stud. J. 10, 77–93 (2006)

    Google Scholar 

  23. Tay, R., Watson, B.: Changing drivers’ intentions and behaviors using fear-based driver fatigue advertisements. Health Market. Q. 19(4), 55–68 (2002)

    Article  Google Scholar 

  24. Das, S., Sun, X., Dutta, A.: Text mining and topic modeling of compendiums of papers from transportation research board annual meetings. Transp. Res. Rec.: J. Transp. Res. Board 2552, 48–56 (2016)

    Article  Google Scholar 

  25. Das, S., Dixon, K., Sun, X., Dutta, A., Zupancich, M.: Trends in transportation research. Transp. Res. Rec.: J. Transp. Res. Board 2614, 27–38 (2017)

    Article  Google Scholar 

  26. Casas, I., Delmelle, E.C.: Tweeting about public transit — gleaning public perceptions from a social media microblog. Case Stud. Transp. Policy 5(4), 634–642 (2017)

    Article  Google Scholar 

  27. Das, S., Mudgal, A., Dutta, A., Geedipally, S.R.: Vehicle consumer complaint reports involving severe incidents: mining large contingency tables. Transp. Res. Rec.: J. Transp. Res. Board 2672, 72–82 (2018)

    Article  Google Scholar 

  28. Trueblood, A., et al.: A semi-automated tool for identifying agricultural roadway crashes in crash narratives. Traffic Injury Prev. 20(4), 413–418 (2019)

    Article  Google Scholar 

  29. Das, S.: #TRBAM: Exploring Knowledge Management, Research Trends, and Networks by Social Media Mining. TR News, November-December Issue (2019)

    Google Scholar 

  30. Cottrill, C., Gault, P., Yeboah, G., Nelson, J.D., Anable, J., Budd, T.: Tweeting transit: an examination of social media strategies for transport information management during a large event. Transp. Res. Part C: Emerg. Technol. 77, 421–432 (2017)

    Article  Google Scholar 

  31. Das, S., Dutta, A., Medina, G., Minjares-Kyle, L., Elgart, Z.: Extracting patterns from Twitter to promote biking. IATSS Res. 43(1), 51–59 (2019)

    Article  Google Scholar 

  32. Das, S., Griffin, G.: Big data and transportation safety: connecting the dots. In: The Proceedings of Transportation Research Board Annual Meeting, Washington DC, 12–16 January 2020

    Google Scholar 

  33. Haghighi, N.N., Liu, C., Wei, R., Li, W., Shao, H.: Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service. Public Transp. 10(2), 363–377 (2018)

    Article  Google Scholar 

  34. Das, S., Jha, K., Dutta, A.: Vision zero hashtags in social media: understanding end-user needs from natural language processing. In: the Proceedings of Transportation Research Board Annual Meeting, Washington DC, 12–16 January 2020

    Google Scholar 

  35. Pender, B., Currie, G., Delbosc, A., Shiwakoti, N.: Social media use during unplanned transit network disruptions: a review of literature. Transp. Rev. 34(4), 501–521 (2014)

    Article  Google Scholar 

  36. Das, S., Dutta, A., Brewer, M.: Transportation research record articles: a case study of trend mining. In: The Proceedings of Transportation Research Board Annual Meeting, Washington DC, 12–16 January 2020

    Google Scholar 

  37. Sood, G.: tuber: Access YouTube from R. http://soodoku.github.io/tuber/authors.html Accessed 29 Aug 2019

  38. Klostermann, P.: YouTube Comment Scrapper. https://github.com/philbot9/youtube-comment-scraper. Accessed 29 Aug 2019

  39. Monroe, B., Colaresi, M., Quinn, K.: Fightin’ words: lexical feature selection and evaluation for identifying the content of political conflict. Polit. Anal. 16, 372–403 (2008)

    Article  Google Scholar 

  40. Silge, J., Robinson, D.: Text Mining with R: A Tidy Approach. O’Reilly Media, Newton (2018)

    Google Scholar 

  41. Nielsen, F.: A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of the ESWC2011 Workshop on ‘Making Sense of Microposts’: Big Things Come in Small Packages 718 in CEUR Workshop Proceedings, pp. 93–98 (2011)

    Google Scholar 

  42. Dodds, P., Danforth, C.: Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J. Happiness Stud. 11(4), 441–456 (2010)

    Article  Google Scholar 

  43. Doss, P., Harris, K., Kloumann, I., Bliss, C., Danforth, C.: Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter. Plos One 6(12), e26752 (2011)

    Google Scholar 

  44. Lastufka, A., Dean, M.: YouTube: An Insider’s Guide to Climbing the Charts, 1st edn. O’Reilly Media, Newton (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Texas A&M Transportation Institute, Bryan, TX, 77807, USA

    Subasish Das & Songjukta Datta

  2. Ohio State University, Columbus, OH, 43210, USA

    Anandi Dutta

  3. Indian Institute of Technology (BHU), Varanasi, 221005, UP, India

    Abhisek Mudgal

Authors
  1. Subasish Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anandi Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abhisek Mudgal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Songjukta Datta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subasish Das .

Editor information

Editors and Affiliations

  1. Kalinga Institute of Industrial Technology, Bhubaneswar, India

    Siddharth Swarup Rautaray

  2. Deutsche Telekom Technology & Innovation, Darmstadt, Germany

    Gerald Eichler

  3. University of Applied Sciences Jena, Jena, Germany

    Christian Erfurth

  4. University of Hagen, Hagen, Germany

    Günter Fahrnberger

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Das, S., Dutta, A., Mudgal, A., Datta, S. (2020). Non-fear-Based Road Safety Campaign as a Community Service: Contexts from Social Media. In: Rautaray, S., Eichler, G., Erfurth, C., Fahrnberger, G. (eds) Innovations for Community Services. I4CS 2020. Communications in Computer and Information Science, vol 1139. Springer, Cham. https://doi.org/10.1007/978-3-030-37484-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37484-6_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37483-9

  • Online ISBN: 978-3-030-37484-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation