Dr.Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond

Ju, Mingxuan; Song, Wei; Sun, Shiyu; Ye, Yanfang; Fan, Yujie; Hou, Shifu; Loparo, Kenneth A.; Zhao, Liang

doi:10.1145/3442381.3449961

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…Emotions (e.g., surprise, disgust, fear, anger, sadness, anticipation, joy, and trust) in replies were also captured from real and false tweets using the National Research Council Canada (NRC) [36] and LIWC [13]. Mingxuan et al [37] measured people's mental health based on emotions extracted from social media data, which was analyzed using machine learning (ML) or NLP techniques [38,39].…”

Section: Related Workmentioning

confidence: 99%

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Measuring Community Resilience During the COVID-19 based on Community Wellbeing and Resource Distribution

Valinejad

Guo

Cho

et al. 2022

Preprint

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The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals’ and communities’ thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of

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Section: Related Workmentioning

confidence: 99%

“…Mingxuan et al . [37] measured people’s mental health based on emotions extracted from social media data, which was analyzed using machine learning (ML) or NLP techniques [38, 39].…”

Section: Introductionmentioning

confidence: 99%

Measuring Community Resilience During the COVID-19 based on Community Wellbeing and Resource Distribution

Valinejad

Guo

Cho

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Aggressive panic buying behaviors were more prominently observed when more misinformation or rumors on the COVID-19 were disseminated [34] . Emotions (e.g., surprise, disgust, fear, anger, sadness, anticipation, joy, and trust) in replies were also captured from real and false tweets using the National Research Council Canada (NRC) [35] and LIWC [12] . Ju et al [36] measured people's mental health based on emotions extracted from social media data, which was analyzed using machine learning (ML) or NLP techniques [37,38] .…”

Section: Related Workmentioning

confidence: 99%

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

et al. 2022

View full text Add to dashboard Cite

The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals' and communities' thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions.

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“…During COVID-19, many text-based methods have been proposed to aid communities such as to understand human's emotion states [21] and to answer peoples' questions [53]. Web search records collected by the web service provider have extensive applications such as customer behavior analysis [14], stock market prediction [58], and disease outbreak monitoring [2,17].…”

Section: Introductionmentioning

confidence: 99%

“…The Third Wave Features(𝐷 1 , 𝐷 2 ) = (21, 7) (𝐷 1 , 𝐷 2 ) = (21, 14) (𝐷 1 , 𝐷 2 ) = (21, 21) , 𝐷 2 ) = (21, 7) (𝐷 1 , 𝐷 2 ) = (21, 14) (𝐷 1 , 𝐷 2 ) =(21,21) …”

mentioning

confidence: 99%

Multiwave COVID-19 Prediction via Social Awareness-Based Graph Neural Networks using Mobility and Web Search Data

Xue,

Yabe,

Tsubouchi

et al. 2021

Preprint

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Recurring outbreaks of COVID-19 have posed enduring effects on global society, which calls for a predictor of pandemic waves using various data with early availability. Existing prediction models that forecast the first outbreak wave using mobility data may not be applicable to the multiwave prediction, because the evidence in the USA and Japan has shown that mobility patterns across different waves exhibit varying relationships with fluctuations in infection cases. Therefore, to predict the multiwave pandemic, we propose a Social Awareness-Based Graph Neural Network (SAB-GNN) that considers the decay of symptom-related web search frequency to capture the changes in public awareness across multiple waves. SAB-GNN combines GNN and LSTM to model the complex relationships among urban districts, inter-district mobility patterns, web search history, and future COVID-19 infections. We train our model to predict future pandemic outbreaks in the Tokyo area using its mobility and web search data from April 2020 to May 2021 across four pandemic waves collected by _ANONYMOUS_COMPANY_ under strict privacy protection rules. Results show our model outperforms other baselines including ST-GNN and MPNN+LSTM. Though our model is not computationally expensive (only 3 layers and 10 hidden neurons), the proposed model enables public agencies to anticipate and prepare for future pandemic outbreaks.

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Dr.Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond

Cited by 6 publications

References 18 publications

Measuring Community Resilience During the COVID-19 based on Community Wellbeing and Resource Distribution

Measuring Community Resilience During the COVID-19 based on Community Wellbeing and Resource Distribution

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

Multiwave COVID-19 Prediction via Social Awareness-Based Graph Neural Networks using Mobility and Web Search Data

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