Trend and co-occurrence network study of symptoms through social media: an example of COVID-19

Wu, Jiageng; Hua, Yining; Li, Minghui; Zhou, Li; Bates, David O.; Yang, Jie

doi:10.1101/2022.09.28.22280462

Cited by 6 publications

(7 citation statements)

References 67 publications

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“…21,22 The increasing trend of individuals using these platforms to openly discuss mental health issues further solidifies our choice. This trend highlights the acceptance and widespread popularity of social media data [21][22][23][24][25][26][27] , making it a valuable asset for our study. It ensures that our dataset is not only diverse but also reflects the complex realities of mental disorders in the real world.…”

Section: Introductionmentioning

confidence: 83%

Enhancing Mental Health Condition Detection on Social Media through Multi-Task Learning

Liu,

2024

Preprint

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Objective: Mental health conditions are traditionally modeled individually, which ignores the complex, interconnected nature of mental health disorders, which often share overlapping symptoms. This study aims to develop an integrated multi-task learning framework to enhance the detection of mental health conditions. Method: Utilizing datasets from Reddit's SuicideWatch and Mental Health Collection (SWMH) and Psychiatric-disorder Symptoms (PsySym), the study develops a BERT-based multi-task learning framework. This framework leverages pre-trained embedding layers of BERT variants to capture linguistic nuances relevant to various mental health conditions from social media narratives. The approach is tested against the two datasets, comparing multitask modeling with a wide array of single-task baselines and large language models (LLM). Results: The multi-task learning framework demonstrated higher performance in efficiently predicting mental health conditions together compared to single-task models and general-purpose LLMs. Specifically, the framework achieved higher F1 scores across multiple conditions, with notable improvements in recall and precision metrics. This indicates more accurate modeling of mental health disorders when considered together, rather than in isolation. Conclusion: The study confirms the effectiveness of a multi-task learning approach in enhancing the detection of mental health conditions from social media data. It sets a new precedent in computational psychiatry and suggests future explorations into multi-task frameworks for deeper insights into mental health disorders.

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Section: Introductionmentioning

confidence: 83%

Enhancing Mental Health Condition Detection on Social Media through Multi-Task Learning

Liu,

2024

Preprint

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“…Both intrasystemic and intersystemic symptoms had strong co-occurrences, such as chills and fever (both systemic symptoms), palpitations (cardiovascular), and dyspnea (respiratory). For clinicians to further explore the co-occurrences of a specific symptom, we provide an interactive online version of this symptom network [54].…”

Section: Co-occurrence Network Of Covid-19 Symptomsmentioning

confidence: 99%

Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study (Preprint)

Wu¹,

Wang²,

Hua³

et al. 2022

Preprint

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BACKGROUND For an emergent pandemic, such as COVID-19, the statistics of symptoms based on hospital data may be biased or delayed due to the high proportion of asymptomatic or mild-symptom infections that are not recorded in hospitals. Meanwhile, the difficulty in accessing large-scale clinical data also limits many researchers from conducting timely research. OBJECTIVE Given the wide coverage and promptness of social media, this study aimed to present an efficient workflow to track and visualize the dynamic characteristics and co-occurrence of symptoms for the COVID-19 pandemic from large-scale and long-term social media data. METHODS This retrospective study included 471,553,966 COVID-19–related tweets from February 1, 2020, to April 30, 2022. We curated a hierarchical symptom lexicon for social media containing 10 affected organs/systems, 257 symptoms, and 1808 synonyms. The dynamic characteristics of COVID-19 symptoms over time were analyzed from the perspectives of weekly new cases, overall distribution, and temporal prevalence of reported symptoms. The symptom evolutions between virus strains (Delta and Omicron) were investigated by comparing the symptom prevalence during their dominant periods. A co-occurrence symptom network was developed and visualized to investigate inner relationships among symptoms and affected body systems. RESULTS This study identified 201 COVID-19 symptoms and grouped them into 10 affected body systems. There was a significant correlation between the weekly quantity of self-reported symptoms and new COVID-19 infections (Pearson correlation coefficient=0.8528; P<.001). We also observed a 1-week leading trend (Pearson correlation coefficient=0.8802; P<.001) between them. The frequency of symptoms showed dynamic changes as the pandemic progressed, from typical respiratory symptoms in the early stage to more musculoskeletal and nervous symptoms in the later stages. We identified the difference in symptoms between the Delta and Omicron periods. There were fewer severe symptoms (coma and dyspnea), more flu-like symptoms (throat pain and nasal congestion), and fewer typical COVID symptoms (anosmia and taste altered) in the Omicron period than in the Delta period (all P<.001). Network analysis revealed co-occurrences among symptoms and systems corresponding to specific disease progressions, including palpitations (cardiovascular) and dyspnea (respiratory), and alopecia (musculoskeletal) and impotence (reproductive). CONCLUSIONS This study identified more and milder COVID-19 symptoms than clinical research and characterized the dynamic symptom evolution based on 400 million tweets over 27 months. The symptom network revealed potential comorbidity risk and prognostic disease progression. These findings demonstrate that the cooperation of social media and a well-designed workflow can depict a holistic picture of pandemic symptoms to complement clinical studies.

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“…While many studies have focused on cross-sectional analyses, there is a growing recognition of the need for more in-depth and continuous tracking of users over time [10,[29][30][31]. Longitudinal social media analysis is essential for comprehending the dynamics of user's health status during the pandemic, particularly concerning public health issues such as disease evolution and user behaviors [32,33].…”

Section: Introductionmentioning

confidence: 99%

“…While current studies leveraging social media data for disease symptom tracking have made meaningful progress, they often face challenges such as insufficient long-term individual tracking or limited noise reduction, which reduces the accuracy of the results [10,[14][15][16][29][30][31]. In this work, we proposed a pipeline that introduces a comprehensive NLP based framework that denoises longitudinal social media for pandemic monitoring.…”

Section: Introductionmentioning

confidence: 99%

Denoising Longitudinal Social Media for Pandemic Monitoring

Lin,

Garay,

Hua

et al. 2024

Preprint

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ObjectiveCurrent studies leveraging social media data for disease monitoring face challenges like noisy colloquial language and insufficient tracking of user disease progression in longitudinal data settings. This study aims to develop a pipeline for collecting, cleaning, and analyzing large-scale longitudinal social media data for disease monitoring, with a focus on COVID-19 pandemic.Materials and MethodsThis pipeline initiates by screening COVID-19 cases from tweets spanning February 1, 2020, to April 30, 2022. Longitudinal data is collected for each patient, two months before and three months after self-reporting. Symptoms are extracted using Name Entity Recognition (NER), followed by denoising with a combination of Graph Convolutional Network (GCN) and Bidirectional Encoder Representations from Transformers (BERT) model to retain only User Symptom Mentions (USM). Subsequently, symptoms are mapped to standardized medical concepts using the Unified Medical Language System (UMLS). Finally, this study conducts symptom pattern analysis and visualization to illustrate temporal changes in symptom prevalence and co-occurrence.ResultsThis study identified 191,096 self-reported COVID-19-positive cases from COVID-19-related tweets and retrospectively collected 811,398,280 historical tweets, of which 2,120,964 contained symptoms information. After denoising, 39% (832,287) of symptom-sharing tweets reflected user-related mentions. The trained USM model achieved an F1 score of 0.926. Further analysis revealed a higher prevalence of upper respiratory tract symptoms during the Omicron period compared to the Delta and wild-type periods. Additionally, there was a pronounced co-occurrence of lower respiratory tract and nervous system symptoms in the wild-type strain and Delta variant.ConclusionThis study established a robust framework for pandemic monitoring via social media, integrating denoising of user-related symptom mentions and longitudinal data. The findings underscore the importance of denoising procedures in revealing accurate prevalence trends, thereby minimizing biases in symptom analysis.

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Trend and co-occurrence network study of symptoms through social media: an example of COVID-19

Cited by 6 publications

References 67 publications

Enhancing Mental Health Condition Detection on Social Media through Multi-Task Learning

Enhancing Mental Health Condition Detection on Social Media through Multi-Task Learning

Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study (Preprint)

Denoising Longitudinal Social Media for Pandemic Monitoring

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