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Unleashing Big Data: How Social Media Can Forecast Public Mental Health Needs During Crises in Singapore

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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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Publish date : 2024-10-05 17:30:02

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