References
- Abraham, K. G., Haltiwanger, J. C., Sandusky, K., & Spletzer, J. R. (2018). Measuring the gig economy: Current knowledge and open issues (No. w24950). National Bureau of Economic Research. https://doi.org/https://doi.org/10.3386/w24950.
- Abraham, K. G., & Houseman, S. N. (2020). Contingent and alternative employment: Lessons from the contingent worker supplement, 1995-2017. Prepared for the US Department of Labor. https://www.dol.gov/agencies/oasp/evaluation/completedstudies/contingent-alternative-employment
- Ahmed, W., Demaerini, G., & Bath, P. A. (2017, March 22–25). Topics discussed on Twitter at the beginning of the 2014 Ebola epidemic in United States. iConference 2017 proceedings, Wuhan, China, March 22-25, 2017. 774–777. https://doi.org/https://doi.org/10.9776/17338.
- AppJobs Institute. (2020). How does the COVID-19 outbreak affect the life of freelancers & gig workers. https://institute.appjobs.com/how-does-the-covid-19-outbreak-affect-the-life-of-freelancers-gig-workers-march-2020
- Baker, M. G. (2020). Nonrelocatable occupations at increased risk during pandemics: United States, 2018. American Journal of Public Health, 110(8), 1126–1132. https://doi.org/https://doi.org/10.2105/AJPH.2020.305738
- Ballotpedia. (2021, September 23). Coronavirus (COVID-19) vaccination rates and distribution plans by state. Retrieved September 27, 2021, from https://ballotpedia.org/Coronavirus_(COVID-19)_vaccination_rates_and_distribution_plans_by_state
- Ballotpedia. (n.d.). California Proposition 22, App-Based Drivers as Contractors and Labor Policies Initiative (2020). Retrieved September 27, 2021, from https://ballotpedia.org/California_Proposition_22,_App-Based_Drivers_as_Contractors_and_Labor_Policies_Initiative_(2020)
- Barberá, P., Casas, A., Nagler, J., Egan, P. J., Bonneau, R., Jost, J. T., & Tucker, J. A. (2019). Who leads? Who follows? Measuring issue attention and agenda setting by legislators and the mass public using social media data. American Political Science Review, 113(4), 883–901. https://doi.org/https://doi.org/10.1017/S0003055419000352
- Beckman, K. L., Monsey, L. M., Archer, M. M., Errett, N. A., Bostrom, A., & Baker, M. G. (2021). Health and safety risk perceptions and needs of app-based drivers during COVID-19. American Journal of Industrial Medicine, 941–951. https://doi.org/https://doi.org/10.1002/ajim.23295
- Belanche, D., Casaló, L. V., Flavián, C., Pérez-Rueda, A., & Pérez-Rueda aperu, A. (2021). The role of customers in the gig economy: How perceptions of working conditions and service quality influence the use and recommendation of food delivery services. Service Business, 15(1), 45–75. https://doi.org/https://doi.org/10.1007/s11628-020-00432-7
- Benner, C., Johanss, E., Feng, K., & Witt, H. (2020). On-demand and on-the-edge: Ride hailing and delivery workers in San Francisco. UCSC Institute for Social Transformation. https://transform.ucsc.edu/on-demand-and-on-the-edge/
- Burstein, P. (2003). The impact of public opinion on public policy: A review and an agenda. Political Research Quarterly, 56(1), 29–40. https://doi.org/https://doi.org/10.1177/106591290305600103
- California Assembly Bill No. 5, 341. (2019). (Testimony of Supreme Court of California). https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id = 201920200AB5
- Carriere, D. (n.d.). Geocoder: Simple, Consistent. Retrieved September 6, 2021, from https://geocoder.readthedocs.io/
- Ceron, A., & Negri, F. (2016). The “social side” of public policy: Monitoring online public opinion and its mobilization during the policy cycle. Policy & Internet, 8(2), 131–147. https://doi.org/https://doi.org/10.1002/poi3.117
- Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/https://doi.org/10.1177/001316446002000104
- Current Population Survey staff. (2018). Electronically mediated work: New questions in the contingent worker supplement. Monthly Labor Review, 2018(9), https://doi.org/https://doi.org/10.21916/mlr.2018.24
- DataReportal. (2021). Twitter Stats and Trends. Retrieved September 27, 2021, from https://datareportal.com/essential-twitter-stats
- Desai, M., & Mehta, M. A. (2017). Techniques for sentiment analysis of Twitter data: A comprehensive survey. Proceeding – IEEE International Conference on Computing, Communication and Automation, ICCCA 2016, 149–154. https://doi.org/https://doi.org/10.1109/CCAA.2016.7813707.
- Dingel, J. I., & Neiman, B. (2020). How many jobs can be done at home? Journal of Public Economics, 189, 104235. https://doi.org/https://doi.org/10.1016/j.jpubeco.2020.104235
- Dubey, A. D., & Tripathi, S. (2020). Analysing the sentiments towards work-from-home experience during COVID-19 pandemic. Journal of Innovation Management, 8(1), 13–19. https://doi.org/https://doi.org/10.24840/2183-0606_008.001_0003
- Edwards, A., Housley, W., Williams, M., Sloan, L., & Williams, M. (2013). Digital social research, social media and the sociological imagination: Surrogacy, augmentation and re-orientation. International Journal of Social Research Methodology, 16(3), 245–260. https://doi.org/https://doi.org/10.1080/13645579.2013.774185
- Farrell, D., Greig, F., & Hamoudi, A. (2018). The online platform economy in 2018: Drivers, workers, sellers, and lessors. JPMorgan Chase Institute. https://www.jpmorganchase.com/content/dam/jpmc/jpmorgan-chase-and-co/institute/pdf/institute-ope-2018.pdf
- Heineke, K., Kloss, B., & Scurtu, D. (2020, July 16). The future of micromobility: Ridership and revenue after a crisis. McKinsey Center for Future Mobility. Retrieved on August 23, 2021, from https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/the-future-of-micromobility-ridership-and-revenue-after-a-crisis
- Hemphill, L., Russell, A., & Schöpke-Gonzalez, A. M. (2021). What drives U.S. Congressional members’ policy attention on Twitter? Policy & Internet, 13(2), 233–256. https://doi.org/https://doi.org/10.1002/poi3.245
- Henao, A., & Marshall, W. E. (2019). An analysis of the individual economics of ride-hailing drivers. Transportation Research Part A: Policy and Practice, 130, 440–451. https://doi.org/https://doi.org/10.1016/j.tra.2019.09.056
- H.R.748 – CARES Act. (2020). (Testimony of Joe Courtney). https://www.congress.gov/bill/116th-congress/house-bill/748
- Instacart. (2020, March 23). Instacart announces plans to bring on 300,000 new personal shoppers over the next 3 months. PRNewswire. https://www.prnewswire.com/news-releases/instacart-announces-plans-to-bring-on-300-000-new-personal-shoppers-over-the-next-3-months-301028409.html
- Katta, S., Badger, A., Graham, M., Howson, K., Ustek-Spilda, F., & Bertolini, A. (2020). (Dis)embeddedness and (de)commodification: COVID-19, Uber, and the unravelling logics of the gig economy. Dialogues in Human Geography, 10(2), 203–207. https://doi.org/https://doi.org/10.1177/2043820620934942
- Khosrowshahi, D. (2020a, March 23). Letter to the President. Uber. https://blogadmin.uberinternal.com/wp-content/uploads/2020/03/UberLetter.pdf
- Khosrowshahi, D. (2020b, October 5). The high cost of making drivers employees. Uber Newsroom. https://www.uber.com/en-CA/newsroom/economic-impact/
- Lobel, O. (2020). We are all gig workers now: Online platforms, freelancers and the battles over employment status and rights during the COVID-19 pandemic. SSRN Electronic Journal, 57(4), 919–946. https://doi.org/https://doi.org/10.2139/ssrn.3725090
- Manza, J., Cook, F. L., & Page, B. I. (2002). Navigating public opinion. Oxford University Press, Incorporated.
- McFarland, M. (2014, May 27). Uber’s remarkable growth could end the era of poorly paid cab drivers. Washington Post. https://www.washingtonpost.com/news/innovations/wp/2014/05/27/ubers-remarkable-growth-could-end-the-era-of-poorly-paid-cab-drivers/
- McNeil, K., Brna, P. M., & Gordon, K. E. (2012). Epilepsy in the Twitter era: A need to re-tweet the way we think about seizures. Epilepsy & Behavior, 23(2), 127–130. https://doi.org/https://doi.org/10.1016/j.yebeh.2011.10.020
- Mejia, C., Pittman, R., Beltramo, J. M. D., Horan, K., Grinley, A., & Shoss, M. K. (2021). Stigma & dirty work: In-group and out-group perceptions of essential service workers during COVID-19. International Journal of Hospitality Management, 93, 102772. https://doi.org/https://doi.org/10.1016/j.ijhm.2020.102772
- Morshed, S. A., Khan, S. S., Tanvir, R. B., & Nur, S. (2021). Impact of COVID-19 pandemic on ride-hailing services based on large-scale Twitter data analysis. Journal of Urban Management, 10(2), 155–165. https://doi.org/https://doi.org/10.1016/j.jum.2021.03.002
- Newman, N., Fletcher, R., Schulz, A., Andı, S., & Nielsen, R. K. (2020). Reuters Institute digital news report 2020. Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2020-06/DNR_2020_FINAL.pdf
- Nexø, L. A., & Strandell, J. (2020). Testing, filtering, and insinuating: Matching and attunement of emoji use patterns as non-verbal flirting in online dating. Poetics, 83, 101477. https://doi.org/https://doi.org/10.1016/j.poetic.2020.101477
- Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), https://doi.org/https://doi.org/10.1177/1609406917733847
- Oscar, N., Fox, P. A., Croucher, R., Wernick, R., Keune, J., & Hooker, K. (2017). Machine learning, sentiment analysis, and tweets: An examination of Alzheimer’s disease stigma on Twitter. The Journals of Gerontology: Series B, 72(5), 742–751. https://doi.org/https://doi.org/10.1093/geronb/gbx014
- Page, B. I., & Shapiro, R. Y. (1983). Effects of public opinion on policy. American Political Science Review, 77(1), 175–190. https://doi.org/https://doi.org/10.2307/1956018
- Paquette, D., & Long, H. (2018, June 7). America’s gig economy is smaller now than before Uber existed, official data show. The Washington Post. https://www.washingtonpost.com/news/wonk/wp/2018/06/07/there-are-fewer-workers-in-the-gig-economy-today-than-before-uber-existed-official-data-show/
- Pinto, M., Smith, R., & Tung, I. (2019). Rights at risk: Gig companies’ campaign to upend employment as we know it. National Employment Law Project. https://www.nelp.org/publication/rights-at-risk-gig-companies-campaign-to-upend-employment-as-we-know-it/
- Proclamation No. 9994. (2020). 85 F.R. 15337 (March 18, 2020). https://www.federalregister.gov/documents/2020/03/18/2020-05794/declaring-a-national-emergency-concerning-the-novel-coronavirus-disease-covid-19-outbreak
- Purkayastha, D., Vanroelen, C., Bircan, T., Vantyghem, M. A., & Gantelet Adsera, C. (2021). Work, health and COVID-19: A literature review. SSRN Electronic Journal, https://doi.org/https://doi.org/10.2139/ssrn.3856915
- Ravenelle, A. J., Kowalski, K. C., & Janko, E. (2021). The side hustle safety net: Precarious workers and gig work during COVID-19. Sociological Perspectives, https://doi.org/https://doi.org/10.1177/07311214211005489
- Salesforce. (n.d.-a). Introducing Social Studio REST API. Social Studio API Developer’s Guide. Retrieved September 28, 2021, from https://developer.salesforce.com/docs/atlas.en-us.230.0.api_social.meta/api_social/1-introduction-to-the-social-studio-marketing-cloud.htm
- Salesforce. (n.d.-b). Posts. Social Studio API Developer’s Guide. Retrieved September 28, 2021, from https://developer.salesforce.com/docs/atlas.en-us.230.0.api_social.meta/api_social/14-posts.htm
- Salesforce. (n.d.-c). Sentiment Model. Retrieved July 15, 2021, from https://help.salesforce.com/articleView?id = sf.mc_ss_sentiment_model.htm
- Saud, M., Mashud, M., & Ida, R. (2020). Usage of social media during the pandemic: Seeking support and awareness about COVID-19 through social media platforms. Journal of Public Affairs, 20(4), e02417. https://doi.org/https://doi.org/10.1002/pa.2417
- Schober, M. F., Pasek, J., Guggenheim, L., Lampe, C., & Conrad, F. G. (2016). Social media analyses for social measurement. Public Opinion Quarterly, 80(1), 180–211. https://doi.org/https://doi.org/10.1093/poq/nfv048
- Shaheen, S., Chan, N., Bansal, A., & Cohen, A. (2015). Shared Mobility: A Sustainability & Technologies Workshop: Definitions, Industry Developments, and Early Understanding. https://escholarship.org/uc/item/2f61q30s
- Smith, R. (2020). Independent contractors & COVID-19: Working without protections. National Employment Law Project. https://www.nelp.org/publication/independent-contractors-covid-19-working-without-protections/
- Spears, R. (2021). The impact of public opinion on large global companies’ market valuations: A Markov switching model approach. Journal of Finance and Economics, 9(3), 115–141. https://doi.org/https://doi.org/10.12691/jfe-9-3-3
- Stemler, S. E., & Tsai, J. (2008). Best practices in interrater reliability three common approaches. In J. Osborne (Ed.), Best practices in quantitative methods (pp. 29–49). SAGE Publications, Inc. https://doi.org/https://doi.org/10.4135/9781412995627.d5.
- Su, L. Y.-F., Cacciatore, M. A., Liang, X., Brossard, D., Scheufele, D. A., & Xenos, M. A. (2017). Analyzing public sentiments online: Combining human- and computer-based content analysis. Information, Communication & Society, 20(3), 406–427. https://doi.org/https://doi.org/10.1080/1369118X.2016.1182197
- Tripathi, S. (2020). Companies, COVID-19 and respect for human rights. Business and Human Rights Journal, 5(2), 252–260. https://doi.org/https://doi.org/10.1017/bhj.2020.16
- Tsai, M. H., & Wang, Y. (2021). Analyzing Twitter data to evaluate people’s attitudes towards public health policies and events in the era of COVID-19. International Journal of Environmental Research and Public Health, 18(12), 6272. https://doi.org/https://doi.org/10.3390/ijerph18126272
- Twitter API. (n.d.). Twitter Standard API v1.1. Retrieved September 7, 2021, from https://developer.twitter.com/en/docs/twitter-api/v1
- Ullmann, T. D. (2019). Automated analysis of reflection in writing: Validating machine learning approaches. International Journal of Artificial Intelligence in Education, 29(2), 217–257. https://doi.org/https://doi.org/10.1007/s40593-019-00174-2
- Unnikrishnan, A., & Figliozzi, M. (2020). A study of the impact of COVID-19 on home delivery purchases and expenditures [Working Paper]. Portland State University. https://archives.pdx.edu/ds/psu/33410
- US Centers for Disease Control and Prevention. (2021). Interim list of categories of essential workers mapped to standardized industry codes and titles. https://www.cdc.gov/vaccines/covid-19/categories-essential-workers.html
- Waheed, S., Herrera, L., Gonzalez-Vasquez, A. L., Shadduck-Hernández, J., Koonse, T., & Leynov, D. (2018). More than a gig: A survey of ride-hailing drivers in Los Angeles. Institute for Research on Labor and Employment. https://www.labor.ucla.edu/publication/more-than-a-gig/
- Webb, L. M., & Wang, Y. (2013). Techniques for sampling online text-based data sets. In Big data management, technologies, and applications (pp. 95–114). IGI Global. https://doi.org/https://doi.org/10.4018/978-1-4666-4699-5.ch005.
- Wojcik, S., & Hughes, A. (2019). Sizing up Twitter users. Pew Research Center. https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/
- Wolcott, H. F. (1994). Transforming qualitative data: Description, analysis, and interpretation. Sage.
- Xiang, X., Lu, X., Halavanau, A., Xue, J., Sun, Y., Lai, P. H. L., & Wu, Z. (2021). Modern senicide in the face of a pandemic: An examination of public discourse and sentiment about older adults and COVID-19 using machine learning. The Journals of Gerontology: Series B, 76(4), e190–e200. https://doi.org/https://doi.org/10.1093/geronb/gbaa128
- Yang, Z. J. (2016). Altruism during Ebola: Risk perception, issue salience, cultural cognition, and information processing. Risk Analysis, 36(6), 1079–1089. https://doi.org/https://doi.org/10.1111/risa.12526
- Zhai, X., Shi, L., & Nehm, R. H. (2021). A meta-analysis of machine learning-based science assessments: Factors impacting machine-human score agreements. Journal of Science Education and Technology, 30(3), 361–379. https://doi.org/https://doi.org/10.1007/s10956-020-09875-z