Harnessing social media data for analyzing public inconvenience in construction of Indian metro rail projects
Tóm tắt
The metro rail links many parts of a large city and offers one of the best modes of transit across it. However, the construction phase of the metro causes inconveniences to the people living in cities by significantly escalating the noise and dust pollution. During the construction of metro rails, citizens face problems in their everyday lives, especially as the elevated corridors go through thick-populated areas and high-vehicle traffic areas, creating traffic snarls and impediments. The literature review revealed additional concerns, such as public green cover depletion, potholes, building waste disposal, and vibrational issues. With the advent of social media, urban citizens use platforms like Twitter to express their opinions on inconveniences. The interactions over these social media platforms constitute big data. This data has immense potential for public engagement, accountability, and timely resolution of public inconvenience. The examination and analysis of social media posts on these issues are important as such analysis would inform the metro rail agency about the factors that affect the public and their emotional reaction to a specific activity or series of activities performed by the construction team. In this work, Twitter posts about the inconveniences associated with metro rail projects in four cities across India were analyzed. Social network analysis (SNA), text analytics, topic modeling, and sentiment analysis were conducted to analyze the data. SNA helped find the Twitter accounts with above-average betweenness centralities, while text analytics and topic modeling helped find latent discussion topics among the stakeholders. Sentiment analysis gave an idea of the public sentiments towards the projects.
Tài liệu tham khảo
Sharma N, Dhyani R, Gangopadhyay S (2013) Critical issues related to metro rail projects in India. J Infrastruct Dev 5(1):67–86. https://doi.org/10.1177/0974930613488296
Manetti G, Bellucci M, Bagnoli L (2017) Stakeholder engagement and public information through social media: a study of canadian and american public transportation agencies. Am Rev Public Adm 47(8):991–1009. https://doi.org/10.1177/0275074016649260
Ninan J, Clegg S, Mahalingam A (2019) Branding and governmentality for infrastructure megaprojects: the role of social media. Int J Project Manage 37(1):59–72. https://doi.org/10.1016/j.ijproman.2018.10.005
Pasayat A (n.d.) Kachrulal Bhagirath Agrawal & Ors vs State Of Maharashtra & Ors on 22 September, 2004. Retrieved from https://indiankanoon.org/doc/293583/
Hadi M (2001) DTI construction industry directorate and forestry commission project report: prepared for: CD Framework: Best value UK timber in construction Approved on behalf of BRE
Glass J, Simmonds M (2007) “considerate construction”: case studies of current practice. Eng Constr Archit Manag 14(2):131–149. https://doi.org/10.1108/09699980710731263
Schexnayder CJ (n.d.) Mitigation of night-time construction noise, vibrations and other nuisances. Synthesis Practice 218, National Cooperative Highway Research Program, Transportation Research Board, Washington, DC.
Duminda JMS (2010) Strategy to minimize user inconvenience during road rehabilitation. University of Moratuwa
Griffith A, Lynde M (2002) Assessing public inconvenience in highway work zones (No. FHWA-OR-RD-02-20). Oregon Dept of Transportation Research Unit.
Shane JS, Amr Kandil CJS (2011) Nighttime construction impacts on safety, quality, and productivity (Issue 10). https://onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP10-78_FR.pdf
Ferguson A (2012) Qualitative evaluation of transportation construction related social costs and their impacts on the local community (Issue May) [The University of Texas at Arlington]. https://rc.library.uta.edu/uta-ir/handle/10106/11165
Kukadia V, Upton S, Grimwood C (2003) Contorlling particles, vapour and noise pollution from construction sites; parts 1–5: site preparation, demolition, earthworks and landscaping. BRE Pollution Guide, pp 1–8
Xue X, Zhang R, Zhang X, Yang RJ, Li H (2015) Environmental and social challenges for urban subway construction: an empirical study in China. Int J Project Manag 33(3):576–588. https://doi.org/10.1016/j.ijproman.2014.09.003
Ray R (2017) Open for business? Effects of los angeles metro rail construction on adjacent businesses. J Trans Land Use 10(1):725–742. https://doi.org/10.5198/jtlu.2017.932
Chakraborty, D (2010) Mumbai Residents Oppose Elevated Metro Corridors. Avail online at http://www.projectsmonitor.com corridors, accessed in January 2011.
United States Environmental Protection Agency (n.d.) Public Participation Guide: Social Media. Retrieved from https://www.epa.gov/international-cooperation/public-participation-guide-social-media#:~:text=Social media allow stakeholders to,in a variety of ways.
Nik-Bakht M, El-Diraby TE (2020) Beyond chatter: profiling community discussion networks in urban infrastructure projects. J Infrastruct Syst 26(3):05020006.
Perera S, Victoria M, Brand S (2015) Use of social media in construction industry: a case study. Going North for Sustainability: Leveraging Knowledge and Innovation for Sustainable Construction and Development: Proceedings of the International Council for Research and Innovation in Building and Construction (CIB2015), 23-25 November 2015, South Bank University, London, UK, 462-473.
Qi B, Costin A, Jia M (2020) A framework with efficient extraction and analysis of Twitter data for evaluating public opinions on transportation services. Travel Behav Soc 21:10–23. https://doi.org/10.1016/j.tbs.2020.05.005
Kocatepe A, Ulak MB, Lores J, Ozguven EE, Yazici A (2018) Exploring the reach of departments of transportation tweets: What drives public engagement? Case Stud Trans Policy 6(4):683–694
Wojtowicz J, Wallace WA (2016) Use of social media by transportation agencies for traffic management. Transp Res Rec 2551(1):82–89. https://doi.org/10.3141/2551-10
Basu R, Khatua A, Jana A, Ghosh S (2017) Harnessing twitter data for analyzing public reactions to transportation policies: evidence from the odd-even policy in Delhi, India. (November). Retrieved from https://www.researchgate.net/publication/321997978_Harnessing_Twitter_Data_for_Analyzing_Public_Reactions_to_Transportation_Policies_Evidences_from_the_Odd-Even_Policy_in_Delhi_India
Kaur N, Pushe V, Kaur R (2014) Natural language processing interface for synonym. Int J Comput Sci Mob Comput 3(7):638–642
Hu X, Liu H (2012) Text analytics in social media. In: Mining text data (pp 385–414). Springer, Boston, MA.
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Sujon M, Dai F (2021) Social media mining for understanding traffic safety culture in washington state using twitter data. J Comput Civ Eng 35(1):04020059. https://doi.org/10.1061/(asce)cp.1943-5487.0000943
Hutto C, Gilbert E (2014) Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the international AAAI conference on web and social media, vol 8, No. 1, pp 216–225
Borg A, Boldt M (2020) Using VADER sentiment and SVM for predicting customer response sentiment. Expert Syst Appl 162:113746. https://doi.org/10.1016/j.eswa.2020.113746
Scott J (1988) Social network analysis. Sociology 22(1):109–127
Williams NL, Ferdinand N, Pasian B (2015) Online stakeholder interactions in the early stage of a megaproject. Proj Manag J 46(6):92–110
Carrasco JA, Hogan B, Wellman B, Miller EJ (2008) Collecting social network data to study social activity-travel behavior: an egocentric approach. Environ Plann B Plann Des 35(6):961–980
Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323(5916):892–895
Johari A (2018) From parsis to adivasis, Mumbai’s metro project faces heat from citizens. Retrieved from https://scroll.in/article/881470/from-parsis-to-adivasis-mumbais-underground-metro-project-faces-heat-from-citizens
Chattyopadhay S (2019) In Kolkata, houses collapse during Metro tunnelling work. Retrieved from https://frontline.thehindu.com/dispatches/article29332315.ece
Palmer S, Udawatta N (2019) Characterising “Green Building” as a topic in Twitter. Constr Innov 19(4):513–530. https://doi.org/10.1108/CI-02-2018-0007
Harel D, Koren Y (2002) A fast multi-scale method for drawing large graphs. J Graph Algorithm Appl 6(3):177–202. https://doi.org/10.7155/jgaa.00051