Data analytics of social media publicity to enhance household waste management
Status PubMed-not-MEDLINE Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články
PubMed
32905054
PubMed Central
PMC7462838
DOI
10.1016/j.resconrec.2020.105146
PII: S0921-3449(20)30463-8
Knihovny.cz E-zdroje
- Klíčová slova
- Digital waste management, Internet of Things, Publicity improvement, Text data mining, User engagement,
- Publikační typ
- časopisecké články MeSH
Household waste segregation and recycling is ranked at a high priority of the waste management hierarchy. Its management remains a great challenge due to the high dependency on social behaviours. The integration of Internet of Things (IoT) and subscription accounts on social media platforms related to household waste management could be an effective and environmentally friendly publicity approach than traditional publicity via posters and newspapers. However, there is a paucity of literature on measuring social media publicity in household waste management, which brings challenges for practitioners to characterise and improve this publicity pathway. In this study, under an integrated framework, data mining approaches are employed or extended for multidimensional publicity analytics using the data of online footprints of propagandist and users. A real-world case study based on a subscription account on the WeChat platform, Shanghai Green Account, is analysed to reveal useful insights for personalised improvements of household waste management. This study suggests that the current publicity related to household waste management leans towards propagandist-centred in both timing and topic dimensions. The identified timing, which has high user engagement, is 12:00-13:00 and 21:00-22:00 on Thursday. The overall relative publicity quality of historical posts is calculated as 0.95. Average user engagement under the macro policy in Shanghai was elevated by 138.5% from 2018 to 2019, during which the collections of biodegradable food waste and recyclable waste were elevated by 88.8% and 431.8%. Intelligent decision support by publicity analytics could enhance household waste management through effective communication.
Zobrazit více v PubMed
Agrawal A., Fu W., Menzies T. What is wrong with topic modeling? and how to fix it using search-based software engineering. Inf. Softw. Technol. 2018;98:74–88.
Akaike H. Akaike’s information criterion. Int. Encycl. Stat. Sci. 2011 25-25.
Aldous K.K., An J., Jansen B.J. Proceedings of the International AAAI Conference on Web and Social Media. 2019. View, like, comment, post: analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations; pp. 47–57.
Blei D.M., Ng A.Y., Jordan M.I. Latent Dirichlet allocation. J. Mach. Learn. Res. 2003;3:993–1022.
Cole K. Communications: social media and the waste management sector: eight strategies to use social media more effectively. Waste Adv. Mag. 2016 https://wasteadvantagemag.com/communications-social-media-and-the-waste-management-sector-eight-strategies-to-use-social-media-more-effectively Accessed 12.06.2020.
Cui R., Gallino S., Moreno A., Zhang D.J. The operational value of social media information. Prod. Oper. Manag. 2018;27(10):1749–1769.
DEEP . Department of Energy and Environmental Protection (DEEP); 2020. Using Social Media to Promote Recycling.https://portal.ct.gov/DEEP/Reduce-Reuse-Recycle/Recycling/Using-Social-Media-to-Promote-Recycling Accessed 12.06.2020.
Desgagné A., Gagnon P. Bayesian robustness to outliers in linear regression and ratio estimation. Braz. J. Probabi. Stat. 2019;33(2):205–221.
Doane D.P., Seward L.E. Measuring skewness: a forgotten statistic? J. Stat. Educ. 2011;19(2):1–18.
Eriksen M.K., Pivnenko K., Olsson M.E., Astrup T.F. Contamination in plastic recycling: influence of metals on the quality of reprocessed plastic. Waste Manag. 2018;79:595–606. PubMed
Facebook . 2020. Waste Management @WasteManagement.https://www.facebook.com/WasteManagement Accessed 22.08.2020.
Fan Y.V., Jiang P., Hemzal M., Klemeš J.J. An update of COVID-19 influence on waste management. Sci. Total Environ. 2020:142014. doi: 10.1016/j.scitotenv.2020.142014. PubMed DOI PMC
Franses P.H. The life cycle of social media. Appl. Econ. Lett. 2015;22(10):796–800.
Frempong J., Chai J., Ampaw E.M., Amofah D.O., Ansong K.W. The relationship among customer operant resources, online value co-creation and electronic-word-of-mouth in solid waste management marketing. J. Clean. Prod. 2020;248
Frigge M., Hoaglin D.C., Iglewicz B. Some implementations of the boxplot. Am. Stat. 1989;43(1):50–54.
Gaenssle S., Budzinski O. Stars in social media: new light through old windows? J. Media Bus. Stud. 2020 doi: 10.1080/16522354.2020.1738694. DOI
Gu B., Wang H., Chen Z., Jiang S., Zhu W., Liu M., Chen Y., Wu Y., He S., Cheng R. Characterization, quantification and management of household solid waste: a case study in China. Resour. Conserv. Recycl. 2015;98:67–75.
Gurcan F., Cagiltay N.E. Big data software engineering: analysis of knowledge domains and skill sets using LDA-based topic modeling. IEEE Access. 2019;7:82541–82552.
Hu K.-.C., Lu M., Huang F.-.Y., Jen W. Click “like” on Facebook: the effect of customer-to-customer interaction on customer voluntary performance for social networking sites. Int. J. Hum.–Comput. Interact. 2017;33(2):135–142.
Jelodar H., Wang Y., Yuan C., Feng X., Jiang X., Li Y., Zhao L. Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey. Multimed. Tools Appl. 2019;78(11):15169–15211.
Jiang P., Fan Y.V., Zhou J., Zheng M., Liu X., Klemeš J.J. Data-driven analytical framework for waste-dumping behaviour analysis to facilitate policy regulations. Waste Manag. 2020;103:285–295. PubMed
Jiang P., Liu X. Hidden Markov model for municipal waste generation forecasting under uncertainties. Eur. J. Oper. Res. 2016;250(2):639–651.
Jiang P., Liu X., Shoemaker C.A. An adaptive particle swarm algorithm for unconstrained global optimization of multimodal functions. Proceedings of the 9th ACM International Conference on Machine Learning and Computing (ICMLC); Singapore; ACM; 2017. pp. 221–226.
Jiang T., Guo Q., Chen S., Yang J. What prompts users to click on news headlines? Evidence from unobtrusive data analysis. Aslib J. Inf. Manag. 2019;72(1):49–66.
Jing F., Wei L. A study on influential factors of WeChat public accounts information transmission hotness (In Chinese) J. Intell. 2016;35(2):157–162.
Klemeš J.J., Fan Y.V., Jiang P. The energy and environmental footprints of COVID-19 fighting measures – PPE, disinfection, supply chains. Energy. 2020 doi: 10.1016/j.energy.2020.118701. PubMed DOI PMC
Klemeš J.J., Fan Y.V., Jiang P. Plastics: friends or foes? The circularity and plastic waste footprint. Energy Sources Part A. 2020 doi: 10.1080/15567036.2020.1801906. DOI
Klemeš J.J., Fan Y.V., Tan R.R., Jiang P. Minimising the present and future plastic waste, energy and environmental footprints related to COVID-19. Renew. Sustain. Energy Rev. 2020;127 PubMed PMC
Knickmeyer D. Social factors influencing household waste separation: a literature review on good practices to improve the recycling performance of urban areas. J. Clean. Prod. 2019
Krounbi L., Enders A., van Es H., Woolf D., van Herzen B., Lehmann J. Biological and thermochemical conversion of human solid waste to soil amendments. Waste Manag. 2019;89:366–378. PubMed PMC
Ksiazek T.B., Peer L., Lessard K. User engagement with online news: conceptualizing interactivity and exploring the relationship between online news videos and user comments. New Media Soc. 2016;18(3):502–520.
Lin H.-.C., Swarna H., Bruning P.F. Taking a global view on brand post popularity: six social media brand post practices for global markets. Bus. Horiz. 2017;60(5):621–633.
Ma Y., Chen J. Wuhan International Conference on e-Business (WHICEB) 2017. Effects of Platform and Content Attributes on Information Dissemination on We Media: a Case Study on WeChat Platform; p. 64.
Mallick R., Bajpai S.P. Impact of social media on environmental awareness, environmental awareness and the role of social media. IGI Global. 2019:140–149.
Martin-Rios C., Demen-Meier C., Gössling S., Cornuz C. Food waste management innovations in the foodservice industry. Waste Manag. 2018;79:196–206. PubMed
Mathworks . MathWorks, Inc.; USA: 2019. Detect Outliers Using Quantile Regression.www.mathworks.com/help/stats/outlier-detection-using-quantile-regression.html#responsive_offcanvas Accessed 12.06.2020.
Mathworks . MathWorks, Inc.; USA: 2020. Choose Number of Topics for LDA Model.https://www.mathworks.com/help/textanalytics/ug/choose-number-of-topics-for-LDA-model.html Accessed 12.06.2020.
Mathworks . MathWorks, Inc.; USA: 2020. Bag-of-n-grams Model.https://www.mathworks.com/help/textanalytics/ref/bagofngrams.html Accessed 12.06.2020.
Meinshausen N. Quantile regression forests. J. Mach. Learn. Res. 2006;7:983–999.
Mintz K.K., Henn L., Park J., Kurman J. What predicts household waste management behaviors? Culture and type of behavior as moderators. Resources. Conserv. Recycl. 2019;145(145):11–18.
Myers J.L., Well A., Lorch R.F. Harper-Collins; New York: 2010. Research Design and Statistical Analysis.
Park J., Ahn C., Lee K., Choi W., Song H.T., Choi S.O., Han S.W. Analysis on public perception, user-satisfaction, and publicity for WEEE collecting system in South Korea: a case study for Door-to-Door Service. Resour. Conserv. Recycl. 2019;144:90–99.
Porteous I., Newman D., Ihler A., Asuncion A., Smyth P., Welling M. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008. Fast collapsed Gibbs sampling for latent Dirichlet allocation; pp. 569–577.
Ramage D., Hall D., Nallapati R., Manning C.D. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. 2009. Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora; pp. 248–256.
Ramzan S., Liu C., Munir H., Xu Y. Assessing young consumers’ awareness and participation in sustainable e-waste management practices: a survey study in Northwest China. Environ. Sci. Pollut. Res. 2019;26(19):20003–20013. PubMed
Sarc R., Curtis A., Kandlbauer L., Khodier K., Lorber K., Pomberger R. Digitalisation and intelligent robotics in value chain of circular economy oriented waste management–a review. Waste Manag. 2019;95:476–492. PubMed
Simon K. 2019. Global Digital 2019 Reports.https://wearesocial.com/blog/2019/01/digital-2019-global-internet-use-accelerates Accessed 12.06.2020.
SLCAAB . 2020. Shanghai Landscaping & City Appearance Administrative Bureau (SLCAAB)http://lhsr.sh.gov.cn/sites/ShanghaiGreen/dyn/ViewIndex.ashx Accessed 12.06.2020.
Sujata M., Khor K.-.S., Ramayah T., Teoh A.P. The role of social media on recycling behaviour. Sustain. Prod. Consum. 2019;20:365–374.
Sun Y., Wang Z., Zhang B., Zhao W., Xu F., Liu J., Wang B. Residents’ sentiments towards electricity price policy: evidence from text mining in social media. Resour. Conserv. Recycl. 2020;160
Wamuyu P.K. Leveraging Web 2.0 technologies to foster collective civic environmental initiatives among low-income urban communities. Comput. Human. Behav. 2018;85:1–14.
Wang Q., Long X., Li L., Kong L., Zhu X., Liang H. Engagement factors for waste sorting in China: the mediating effect of satisfaction. J. Clean. Prod. 2020;267
Wang Z., Guo D., Wang X., Zhang B., Wang B. How does information publicity influence residents’ behaviour intentions around e-waste recycling? Resour. Conserv. Recycl. 2018;133:1–9.
WMR The Last Word: does social media have a place in improving bin behaviour? Waste Manag. Review (WMR) 2015 https://wastemanagementreview.com.au/the-last-word-does-social-media-have-a-place-in-improving-bin-behaviour Accessed 12.06.2020.
Xiao S., Dong H., Geng Y., Francisco M.-.J., Pan H., Wu F. An overview of the municipal solid waste management modes and innovations in Shanghai, China. Environ. Sci. Pollut. Res. 2020 doi: 10.1007/s11356-020-09398-5. PubMed DOI
Young W., Russell S.V., Robinson C.A., Barkemeyer R. Can social media be a tool for reducing consumers’ food waste? A behaviour change experiment by a UK retailer. Resour. Conserv. Recycl. 2017;117:195–203.
Zamri G.B., Azizal N.K.A., Nakamura S., Okada K., Nordin N.H., Othman N., Akhir F.N.M., Sobian A., Kaida N., Hara H. Delivery, impact and approach of household food waste reduction campaigns. J. Clean. Prod. 2019
Zhang J., Yan J., Infield D., Liu Y., Lien F.-.S. Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model. Appl. Energy. 2019;241:229–244.
Zhao O. Beijing Morning PostAccessed 30.07.2020; 2017. Guiding Waste Segregation By Incentives.http://opinion.people.com.cn/n1/2017/0209/c1003-29068217.html
Zorpas A.A. Strategy development in the framework of waste management. Sci. Total Environ. 2020;716 PubMed
Zuo L., Wang C., Sun Q. Sustaining WEEE collection business in China: the case of online to offline (O2O) development strategies. Waste Manag. 2020;101:222–230. PubMed
Impacts of COVID-19 on energy demand and consumption: Challenges, lessons and emerging opportunities
An update of COVID-19 influence on waste management