Exploring the public sentiment of local community on major infrastructure development: Evidence from media news and Twitter data

References

• Aaen, S. B., Lyhne, I., & Nielsen, H. (2018). The use of social media in impact assessment: Experiences among national infrastructure developers in Denmark. Impact Assessment and Project Appraisal, 36(6), 456–466. https://doi.org/10.1080/14615517.2018.1500091
   Web of Science ®Google Scholar
• Agostino, D., & Arnaboldi, M. (2016). A measurement framework for assessing the contribution of social media to public engagement: An empirical analysis on Facebook. Public Management Review, 18(9), 1289–1307. https://doi.org/10.1080/14719037.2015.1100320
   Web of Science ®Google Scholar
• Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214(1), 481–518. https://doi.org/10.1140/epjst/e2012-01703-3
   Web of Science ®Google Scholar
• Chen, Y., Silva, E. A., & Reis, J. P. (2020). Measuring policy debate in a regrowing city by sentiment analysis using online media data: A case study of Leipzig 2030. Regional Science Policy & Practice, 13(3), 675–692. https://doi.org/10.1111/rsp3.12292
   Web of Science ®Google Scholar
• Clark, B., Lyons, G., & Chatterjee, K. (2016). Understanding the process that gives rise to household car ownership level changes. Journal of Transport Geography, 55, 110–120. https://doi.org/10.1016/j.jtrangeo.2016.07.009
   Web of Science ®Google Scholar
• Collin, P., Rahilly, K., Richardson, I., & Third, A. (2011). The benefits of social networking services: A literature review. Cooperative Research Centre for Young People, Technology and Wellbeing.
   Google Scholar
• Cook, D. M., Waugh, B., Abdipanah, M., Hashemi, O., & Rahman, S. A. (2014). Twitter deception and influence: Issues of identity, slacktivism, and puppetry. Journal of Information Warfare, 13(1), 58–71. https://www.jstor.org/stable/26487011
   Google Scholar
• Creighton, J. L. (2005). The public participation handbook: Making better decisions through citizen involvement. John Wiley & Sons.
   Google Scholar
• Fredericks, J., & Foth, M. (2013). Augmenting public participation: Enhancing planning outcomes through the use of social media and web 2.0. Australian Planner, 50(3), 244–256. https://doi.org/10.1080/07293682.2012.748083
   Google Scholar
• Furia, L. D., & Jones, J. W. (2000). The effectiveness of provisions and quality of practices concerning public participation in EIA in Italy. Environmental Impact Assessment Review, 20(4), 457–479. https://doi.org/10.1016/S0195-9255(00)00035-4
   Web of Science ®Google Scholar
• Gibbons, J., Malouf, R., Spitzberg, B., Martinez, L., Appleyard, B., Thompson, C., Tsou, M. H., & Tsou, M.-H. (2019). Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLOS ONE, 14(7), e0219550. https://doi.org/10.1371/journal.pone.0219550.t003
   PubMed Web of Science ®Google Scholar
• Global Web Index. (2019) . Indonesia market snapshot.
   Google Scholar
• Gokulakrishnan, B., Priyanthan, P., Ragavan, T., Prasath, N., & Perera, A. (2012). Opinion mining and sentiment analysis on a twitter data stream. In International conference on advances in ICT for emerging regions (ICTer2012) (pp. 182–188). IEEE. https://doi.org/10.1109/ICTer.2012.6423033
   Google Scholar
• Gorodnichenko, Y., Pham, T., & Talavera, O. (2021). Social media, sentiment and public sentiments: Evidence from #Brexit and #USElection. European Economic Review, 136, 103772. https://doi.org/10.1016/j.euroecorev.2021.103772
   Web of Science ®Google Scholar
• Grimmelikhuijsen, S. G., & Meijer, A. J. (2015). Does Twitter increase perceived police legitimacy? Public Administration Review, 75(4), 598–607. https://doi.org/10.1111/puar.12378
   Web of Science ®Google Scholar
• Griskeviciene, D., Griskevicius, A., & Griskeviciute-Geciene, A. (2012). A new approach to assessment of infrastructure projects on urban transport systems. Transport and Telecommunication Journal, 13(2), 108–122. https://doi.org/10.2478/v10244-012-0009-5
   Google Scholar
• Happer, C., & Philo, G. (2013). The role of the media in the construction of public belief and social change. Journal of Social and Political Psychology, 1(1), 321–336. https://doi.org/10.5964/jspp.v1i1.96
   Google Scholar
• Hensher, D. A., & Goodwin, P. (2004). Using values of travel time savings for toll roads: Avoiding some common errors. Transport Policy, 11(2), 171–181. https://doi.org/10.1016/j.tranpol.2003.10.002
   Google Scholar
• Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 147–169. https://doi.org/10.1080/10919392.2015.1125187
   Web of Science ®Google Scholar
• Johansson, A. C. (2016). Social media and politics in Indonesia. Stockholm School of Economics Asia Working Paper, 42.
   Google Scholar
• Karya, H. (2020). Trans Sumatera. Retrieved August 20, 2020, from https://www.hutamakarya.com/trans-sumatera
   Google Scholar
• Komite Percepatan Penyediaan Infrastruktur Prioritas (KPPIP). (2020). Jalan Tol Trans Sumatera [Trans-Sumatra Toll Road]. Retrieved August 20, 2020, from https://kppip.go.id/proyek-prioritas/jalan/15-ruas-jalan-tol-trans-sumatera/
   Google Scholar
• Lim, M. (2012). Clicks, cabs, and coffee houses: Social media and oppositional movements in Egypt, 2004–2011. Journal of Communication, 62(2), 231–248. https://doi.org/10.1111/j.1460-2466.2012.01628.x
   Web of Science ®Google Scholar
• Lim, M. (2013). Many clicks but little sticks: Social media activism in Indonesia. Digital Activism in Asia Reader, 43(4), 636–657. https://doi.org/10.1080/00472336.2013.769386
   Google Scholar
• Lim, M. (2018). Sticks and stones, clicks and phones: Contextualizing the role of digital media in the politics of transformation. In Digital media and the politics of transformation in the Arab world and Asia (pp. 9–34). Springer VS. https://doi.org/10.1007/978-3-658-20700-7_2
   Google Scholar
• Linders, D. (2012). From e-government to we-government: Defining a typology for citizen coproduction in the age of social media. Government Information Quarterly, 29(4), 446–454. https://doi.org/10.1016/j.giq.2012.06.003
   Web of Science ®Google Scholar
• Li, T. H., Ng, S. T., & Skitmore, M. (2013). Evaluating stakeholder satisfaction during public participation in major infrastructure and construction projects: A fuzzy approach. Automation in Construction, 29, 123–135. https://doi.org/10.1016/j.autcon.2012.09.007
   Web of Science ®Google Scholar
• Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
   Google Scholar
• Meng, M., Rau, A., & Mahardhika, H. (2018). Public transport travel time perception: Effects of socioeconomic characteristics, trip characteristics and facility usage. Transportation Research Part A: Policy and Practice, 114, 24–37. https://doi.org/10.1016/j.tra.2018.01.015
   Web of Science ®Google Scholar
• Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., Danforth, C. M., & Sánchez, A. (2013). The geography of happiness: Connecting twitter sentiment and expression, demographics, and objective characteristics of place. PLOS ONE, 8(5), e64417. https://doi.org/10.1371/journal.pone.0064417
   PubMed Web of Science ®Google Scholar
• Montgomery, K. (2007). Generation digital: Politics, commerce and childhood in the age of the Internet. The MIT Press. https://doi.org/10.7551/mitpress/3222.001.0001
   Google Scholar
• Moon, S. J., & Hyun, K. D. (2009). The salience of stakeholders and their attributes in public relations and business news. Journal of Mass Media Ethics, 24(1), 59–75. https://doi.org/10.1080/08900520802644428
   Web of Science ®Google Scholar
• Mostert, E. (2003). The challenge of public participation. Water Policy, 5(2), 179–197. https://doi.org/10.2166/wp.2003.0011
   Google Scholar
• Pfeffer, J., Mooseder, A., Lasser, J., Hammer, L., Stritzel, O., & Garcia, D. (2023). This sample seems to be good enough! Assessing coverage and temporal reliability of Twitter’s academic API. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 17, pp. 720–729). https://doi.org/10.48550/arXiv.2204.02290
   Google Scholar
• Plotnick, R. D., Romich, J. L., Thacker, J., & Evans, D. (2009). The impacts of tolling on low-income persons in the Puget Sound region (report no. WA-RD 721-1). Washington State Department of Transportation, Office of Research and Library Services. https://www.wsdot.wa.gov/research/reports/fullreports/721.1.pdf
   Google Scholar
• Potkin, F., & Da Costa, A. B. (2019). In Indonesia, Facebook and Twitter are ‘buzzer’ battlegrounds as elections loom. Retrieved March 13, 2019, from https://www.reuters.com/article/us-indonesia-election-socialmedia-insigh-idUSKBN1QU0AS
   Google Scholar
• Pucher, J., & Renne, J. L. (2003). Socioeconomics of urban travel. Evidence from the 2001 NHTS. Transportation Quarterly, 57(3), 49–77. https://doi.org/10.1007/s11116-004-5508-3
   Google Scholar
• Rahman, M. M., Ali, G. G., Li, X. J., Paul, K. C., & Chong, P. H. (2020). Twitter and census data analytics to explore socio-economic factors for post-COVID-19 reopening sentiment. Social Science Research Network. https://doi.org/10.2139/ssrn.3639551
   Google Scholar
• Ramanadhan, S., Mendez, S. R., Rao, M., & Viswanath, K. (2013). Social media use by community-based organizations conducting health promotion: A content analysis. BMC Public Health, 13(1129), 1–10. https://doi.org/10.1186/1471-2458-13-1129
   PubMedGoogle Scholar
• Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14–46. https://doi.org/10.1016/j.knosys.2015.06.015
   Web of Science ®Google Scholar
• Stamati, T., Papadopoulos, T., & Anagnostopoulos, D. (2015). Social media for openness and accountability in the public sector: Cases in the Greek context. Government Information Quarterly, 32(1), 12–29. https://doi.org/10.1016/j.giq.2014.11.004
   Web of Science ®Google Scholar
• Steiger, E., Westerholt, R., Resch, B., & Zipf, A. (2015). Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data. Computers, Environment and Urban Systems, 54, 255–265. https://doi.org/10.1016/j.compenvurbsys.2015.09.007
   Web of Science ®Google Scholar
• Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/COLI_a_00049
   Web of Science ®Google Scholar
• Valentin, V., Naderpajouh, N., & Abraham, D. (2018). Impact of characteristics of infrastructure projects on public opinion. Journal of Management in Engineering, 34(1). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000638
   Web of Science ®Google Scholar
• Van Der Walt, E., & Eloff, J. (2018). Using machine learning to detect fake identities: Bots vs humans. IEEE Access, 6, 6540–6549. https://doi.org/10.1109/ACCESS.2018.2796018
   Google Scholar
• Verma, T., Renu, R., & Gaur, D. (2014). Tokenization and filtering process in RapidMiner. International Journal of Applied Information Systems, 7(2), 16–18. https://doi.org/10.5120/ijais14-451139
   Google Scholar
• Wang, Y., & Fikis, D. J. (2019). Common core state standards on Twitter: Public sentiment and opinion leaders. Educational Policy, 33(4), 650–683. https://doi.org/10.1177/0895904817723739
   Web of Science ®Google Scholar
• We Are Social & Hootsuite. (2020). Digital 2020 Indonesia. Retrieved August 19, 2021, from https://datareportal.com/reports/digital-2020-indonesia
   Google Scholar
• Welbers, K., Van Atteveldt, W., & Benoit, K. (2017). Text analysis in R. Communication Methods and Measures, 11(4), 245–265. https://doi.org/10.1080/19312458.2017.1387238
   Web of Science ®Google Scholar
• Weller, K., Bruns, A., Burgess, J., Mahrt, M., Puschmann, C., Weller, K., Bruns, A., Burgess, J., Mahrt, M., & Puschmann, C. (2013). Twitter and society. Peter Lang. https://doi.org/10.3726/978-1-4539-1170-9
   Google Scholar
• Whitelaw, C., Agrawal, M., Rao, H. R., & Oh, O. (2011). Using social media to study social phenomena: An example using Twitter data. In 2011 Wireless Telecommunications Symposium (WTS) (pp. 1–3). IEEE. https://doi.org/10.1109/WTS.2011.5960865
   Google Scholar
• Widener, M. J., & Li, W. (2014). Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US. Applied Geography, 54, 189–197. https://doi.org/10.1016/j.apgeog.2014.07.017
   Web of Science ®Google Scholar
• Wiseman, Y., & Giat, Y. (2016). Multimodal passenger transportation security in Israel. In Multimodal transport security. Edward Elgar Publishing.
   Google Scholar
• Zavattaro, S. M., French, P. E., & Mohanty, S. D. (2015). A sentiment analysis of US local government tweets: The connection between tone and citizen involvement. Government Information Quarterly, 32(3), 333–341. https://doi.org/10.1016/j.giq.2015.03.003
   Web of Science ®Google Scholar
• Zhai, W., Peng, Z. R., & Yuan, F. (2020). Examine the effects of neighborhood equity on disaster situational awareness: Harness machine learning and geotagged Twitter data. International Journal of Disaster Risk Reduction, 48, 101611. https://doi.org/10.1016/j.ijdrr.2020.101611
   Web of Science ®Google Scholar
• Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E., Yan, H., & Li, X. (2011). Comparing Twitter and traditional media using topic models. In P. Clough, C. Foley, C. Gurrin, G. J. F. Jones, W. Kraaij, H. Lee, & V. Mudoch (Eds.), Advances in information retrieval (Vol. 6611, pp. 338–349). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_34
   Google Scholar