Data analysis of resident engagement and sentiments in social media enables better household waste segregation and recycling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU141560" target="_blank" >RIV/00216305:26210/21:PU141560 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0959652621030079?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0959652621030079?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jclepro.2021.128809" target="_blank" >10.1016/j.jclepro.2021.128809</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data analysis of resident engagement and sentiments in social media enables better household waste segregation and recycling

Popis výsledku v původním jazyce

Waste segregation, recycling and reduction have been prioritised in the Circular Economy transition of household waste management to reduce environmental impacts. With digitalisation and innovation developments in waste management, residents become more active on waste management-related social media platforms. However, there is still needed a tangible analysis of resident engagement (e.g. user comments and interactions) and related sentiment changes on such platforms to enhance waste management and ease the environmental burden at source. This study develops an integrated solution to analyse resident engagement by leveraging statistical analysis and text-mining methods. Four interrelated components are incorporated in the solution: population behaviour quantification, sentiment analysis and dynamics, popular concerns and probability distribution fitting, and rule-based managerial insight identification. The novel solution is applied to a real-world case study on a subscription account related to waste management in Shanghai. This research produces several major observations based on the studied case: (i) The resident engagement Monday-to-Thursday was more active than Friday-to-Sunday. (ii) Compared to 2018, the resident engagement by commenting on online posts was elevated by 107.1% in 2019 when Shanghai introduced a new management policy. Meanwhile, the yearly resource-type waste collection was increased by 114.5% in 2019. (iii) It took approximately one year to recover positive sentiments in user comments after introducing the policy. However, the comments with negative sentiments assisted in improving waste management. (iv) The best-fitted negative binomial distribution of the number of votes for user comments could guarantee the managerial insight identification from the minority of comments with popular concerns.

Název v anglickém jazyce

Data analysis of resident engagement and sentiments in social media enables better household waste segregation and recycling

Popis výsledku anglicky

Waste segregation, recycling and reduction have been prioritised in the Circular Economy transition of household waste management to reduce environmental impacts. With digitalisation and innovation developments in waste management, residents become more active on waste management-related social media platforms. However, there is still needed a tangible analysis of resident engagement (e.g. user comments and interactions) and related sentiment changes on such platforms to enhance waste management and ease the environmental burden at source. This study develops an integrated solution to analyse resident engagement by leveraging statistical analysis and text-mining methods. Four interrelated components are incorporated in the solution: population behaviour quantification, sentiment analysis and dynamics, popular concerns and probability distribution fitting, and rule-based managerial insight identification. The novel solution is applied to a real-world case study on a subscription account related to waste management in Shanghai. This research produces several major observations based on the studied case: (i) The resident engagement Monday-to-Thursday was more active than Friday-to-Sunday. (ii) Compared to 2018, the resident engagement by commenting on online posts was elevated by 107.1% in 2019 when Shanghai introduced a new management policy. Meanwhile, the yearly resource-type waste collection was increased by 114.5% in 2019. (iii) It took approximately one year to recover positive sentiments in user comments after introducing the policy. However, the comments with negative sentiments assisted in improving waste management. (iv) The best-fitted negative binomial distribution of the number of votes for user comments could guarantee the managerial insight identification from the minority of comments with popular concerns.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Cleaner Production

ISSN

0959-6526

e-ISSN

1879-1786

Svazek periodika

neuveden

Číslo periodika v rámci svazku

319

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

128809-128809

Kód UT WoS článku

000704409500003

EID výsledku v databázi Scopus

2-s2.0-85113556871