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The research project received approval under the category of “Exemption from complete A*STAR IRB Review” (IRB reference number 2020-258), permitting the use of social media data sourced through authorized Twitter APIs alongside existing anonymous public datasets to investigate issues related to the COVID-19 pandemic.
To address our research inquiries, we compiled daily time-series data from various public platforms, focusing on Singapore’s escalating and stabilizing periods over a span of 18 months, specifically from July 2020 to December 2021.
Criteria for Data and Indicator Selection
The main objective of this study is to explore new data sources and methodologies capable of predicting future mental healthcare demands. Therefore, the foremost criterion is that these data and indicator extraction methods must be readily accessible without incurring significant expenses or challenges.
Moreover, it is crucial that this data be continuously available for analysis as time series. This stipulation renders traditional survey methodologies—typically conducted every few months—inadequate for forecasting daily mental health requirements.
Additionally, both the tools and the data utilized should demonstrate verifiable validity through previous studies or their application in similar domains.
Indicators Relating to Situational Data
To measure situational factors, we relied on datasets provided by health authorities which are systematically gathered and publicly shared as primary metrics reflecting COVID-19 severity. The key indicators encompassed daily counts of COVID-19 cases and fatalities reported cumulatively by WHO23. Furthermore, we monitored daily government communications sourced from MOH24 as a gauge for assessing governmental engagement in managing the pandemic’s impact. These variables served as comparative predictors in our analysis.
Social Media Insights: Processing Data & Emotion Indicators
Twitter was selected as our social media platform because it grants open access to tweet content including text posts, user names, timestamps along with other relevant information suitable for academic inquiry via an application programming interface (API)25. We conducted a keyword search targeting tweets that included at least one term linked with COVID-19: “ncov,” “corona,” or “covid.” Specifically for this investigation, we focused on tweets originating in Singapore based on location details disclosed by users’ profiles. More information regarding our Twitter dataset can be found within Gupta et al.26.
In order to derive effective early indicators from an often chaotic array of social media posts, we employed several structured steps involving: (i) dataset cleansing; (ii) emotion categorization combined with intensity evaluation; (iii) compiling final study records into aggregated daily formats suited for statistical review; and (iv) pre-processing these obtained aggregates. Our first step involved purging unwanted elements such as duplicate entries or promotional content associated with cryptocurrencies (“bitcoin”), dubious links (“click here”), email addresses along with trifling posts consisting solely of single characters. We also excluded tweets generated by influential accounts—such entities being defined where user ratios surpassed one—to ensure improved reflection of general public sentiment rather than celebrity viewpoints; consequently yielding a comprehensive collection comprising 140897 tweets post-removal adjustments accounting for trolls totaling 2 335 instances alongside potential influencers tallying up to [234830].
The emotion assessment was facilitated using CrystalFeel28—a vital API engineered specifically for processing extensive datasets within academia domains systematically. Utilizing Support Vector Machine algorithms entrenched within CrystalFeel allows us not only to delineate emotions—joy, anger fear depression—but also quantify their presence measured along continuous scales scoring zero through one noted across targeted categorical responses proximate—as indicated earlier—the absence versus pronounced extremes exemplified appraisal levels depict overall emotional dynamics experienced across discursive platforms including specific examples featured below(Table 4).
Table 4 Illustrative Instances Concerning Social Media Emotion Classification Alongside Measurement Intensity Factors (Total Tweets Recorded = 140897)
Indicators Related To Mental Healthcare Demands – IMH Visits & Mindline Crisis Support
Mental health needs were appraised through behavioral insights indicative over fluctuating temporal variations witnessed during online/offline interactions vis-à-vis formal channels established under governmental spaces representative handling aid provision:(1)evidence gathered via psychiatric emergency departments accessing visitors count trends registered extending at Institute Mental Health(IMH40)—primary responder facility renewable care-responsive capacities meantime grounded observed triggers essentially underpinning pursuit psychiatric service amenities.
(2)Information collected online featuring self-help infrastructure leveraging utilities facilitated upon Mindline41—a dedicated resource spearheaded amidst timing uncertainties posed arising due consequences resultant ensuing air gaps surfacing leading contractions affecting community resilience distractions largely pivot implemented June systematic timeline broaden coverage according criticality addressing ranges surveying aspects upon communal isolation/unemployment ramifications.Centralized core functionalities include presenting questionnaires indicating wellness benchmarks encompassing sixteen-item metrics formatted against recognized screening criteria validated appropriate consistency melding norms such notably established therein capturing reflective participant outlooks demonstration aimed query submissions seeking clarity stipulated outlines parameters drawn intermediary…”In previous fortnight occupy experiences bothersome categorically captured …?”
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Table b5 Mindline Severity Level Mapping Correspondences encompassing Associated Evaluations Assigned PHQ9 GAD7 Correspondents Behavioural Adaptations Categoryvexation|Present wherever prescribed hospital arrangements stem facultysuccessive tracing ortnotable manifest regulation curves。：)
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### Develop Insight into Statistical Analysis – Prepping Aggregated Results
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### Pursue Granger Causality Examination
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The post Unleashing Big Data: How Social Media Can Forecast Public Mental Health Needs During Crises in Singapore first appeared on Earth-News.info.
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Publish date : 2024-10-05 17:30:02
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