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Analysis of Popular Social Media Topics Regarding Plastic Pollution

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Analysis of Popular Social Media Topics Regarding Plastic Pollution. / Teh, Phoey Lee; Piao, Scott; Almansour, Mansour; Ong, Huey Fang ; Ahad, Abdul .

In: Sustainability, Vol. 14, No. 3, 1709, 01.02.2022.

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Teh, Phoey Lee ; Piao, Scott ; Almansour, Mansour ; Ong, Huey Fang ; Ahad, Abdul . / Analysis of Popular Social Media Topics Regarding Plastic Pollution. In: Sustainability. 2022 ; Vol. 14, No. 3.

Bibtex

@article{887defd744d646d785e49c8fe72d6471,
title = "Analysis of Popular Social Media Topics Regarding Plastic Pollution",
abstract = "Plastic pollution is one of the most significant environmental issues in the world. The rapid increase of the cumulative amount of plastic waste has caused alarm, and the public have called for actions to mitigate its impacts on the environment. Numerous governments and social activists from various non-profit organisations have set up policies and actively promoted awareness and have engaged the public in discussions on this issue. Nevertheless, social responsibility is the key to a sustainable environment, and individuals are accountable for performing their civic duty and commit to behavioural changes that can reduce the use of plastics. This paper explores a set of topic modelling techniques to assist policymakers and environment communities in understanding public opinions about the issues related to plastic pollution by analysing social media data. We report on an experiment in which a total of 274,404 tweets were collected from Twitter that are related to plastic pollution, and five topic modelling techniques, including (a) Latent Dirichlet Allocation (LDA), (b) Hierarchical Dirichlet Process (HDP), (c) Latent Semantic Indexing (LSI), (d) Non-Negative Matrix Factorisation (NMF), and (e) extension of LDA—Structural Topic Model (STM), were applied to the data to identify popular topics of online conversations, considering topic coherence, topic prevalence, and topic correlation. Our experimental results show that some of these topic modelling techniques are effective in detecting and identifying important topics surrounding plastic pollution, and potentially different techniques can be combined to develop an efficient system for mining important environment-related topics from social media data on a large scale.",
keywords = "Social Media Analysis, Natural Language Processing, Environment, Plastic Pollution, Topic Modelling",
author = "Teh, {Phoey Lee} and Scott Piao and Mansour Almansour and Ong, {Huey Fang} and Abdul Ahad",
year = "2022",
month = feb,
day = "1",
doi = "10.3390/su14031709",
language = "English",
volume = "14",
journal = "Sustainability",
issn = "2071-1050",
publisher = "MDPI - Open Access Publishing",
number = "3",

}

RIS

TY - JOUR

T1 - Analysis of Popular Social Media Topics Regarding Plastic Pollution

AU - Teh, Phoey Lee

AU - Piao, Scott

AU - Almansour, Mansour

AU - Ong, Huey Fang

AU - Ahad, Abdul

PY - 2022/2/1

Y1 - 2022/2/1

N2 - Plastic pollution is one of the most significant environmental issues in the world. The rapid increase of the cumulative amount of plastic waste has caused alarm, and the public have called for actions to mitigate its impacts on the environment. Numerous governments and social activists from various non-profit organisations have set up policies and actively promoted awareness and have engaged the public in discussions on this issue. Nevertheless, social responsibility is the key to a sustainable environment, and individuals are accountable for performing their civic duty and commit to behavioural changes that can reduce the use of plastics. This paper explores a set of topic modelling techniques to assist policymakers and environment communities in understanding public opinions about the issues related to plastic pollution by analysing social media data. We report on an experiment in which a total of 274,404 tweets were collected from Twitter that are related to plastic pollution, and five topic modelling techniques, including (a) Latent Dirichlet Allocation (LDA), (b) Hierarchical Dirichlet Process (HDP), (c) Latent Semantic Indexing (LSI), (d) Non-Negative Matrix Factorisation (NMF), and (e) extension of LDA—Structural Topic Model (STM), were applied to the data to identify popular topics of online conversations, considering topic coherence, topic prevalence, and topic correlation. Our experimental results show that some of these topic modelling techniques are effective in detecting and identifying important topics surrounding plastic pollution, and potentially different techniques can be combined to develop an efficient system for mining important environment-related topics from social media data on a large scale.

AB - Plastic pollution is one of the most significant environmental issues in the world. The rapid increase of the cumulative amount of plastic waste has caused alarm, and the public have called for actions to mitigate its impacts on the environment. Numerous governments and social activists from various non-profit organisations have set up policies and actively promoted awareness and have engaged the public in discussions on this issue. Nevertheless, social responsibility is the key to a sustainable environment, and individuals are accountable for performing their civic duty and commit to behavioural changes that can reduce the use of plastics. This paper explores a set of topic modelling techniques to assist policymakers and environment communities in understanding public opinions about the issues related to plastic pollution by analysing social media data. We report on an experiment in which a total of 274,404 tweets were collected from Twitter that are related to plastic pollution, and five topic modelling techniques, including (a) Latent Dirichlet Allocation (LDA), (b) Hierarchical Dirichlet Process (HDP), (c) Latent Semantic Indexing (LSI), (d) Non-Negative Matrix Factorisation (NMF), and (e) extension of LDA—Structural Topic Model (STM), were applied to the data to identify popular topics of online conversations, considering topic coherence, topic prevalence, and topic correlation. Our experimental results show that some of these topic modelling techniques are effective in detecting and identifying important topics surrounding plastic pollution, and potentially different techniques can be combined to develop an efficient system for mining important environment-related topics from social media data on a large scale.

KW - Social Media Analysis

KW - Natural Language Processing

KW - Environment

KW - Plastic Pollution

KW - Topic Modelling

U2 - 10.3390/su14031709

DO - 10.3390/su14031709

M3 - Journal article

VL - 14

JO - Sustainability

JF - Sustainability

SN - 2071-1050

IS - 3

M1 - 1709

ER -