Letters of recommendation for pediatric surgery fellowship: Analysis of linguistic differences based on gender of the applicant

Bludevich, Bryce M.; Irby, Iris; Chang, Hsun-Hsien; Danielson, Paul D.; González, Raquel; Snyder, Christopher W.; Chandler, Nicole M.

doi:10.1016/j.jpedsurg.2021.02.049

Cited by 10 publications

(1 citation statement)

References 7 publications

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“…It enables us to extract the psychological and linguistic features of tweets [18]. LIWC is one of the most commonly used techniques for verbal deception detection and is used in numerous studies concerning psychological processes [19][20][21][22][23]. We quantify three psychological factors using LIWC as follows: anger, anxiety and sadness.…”

Section: Psychological State Analysismentioning

confidence: 99%

Using Natural Language Processing to Assess the Psychological Effect of COVID-19 Pandemic on Insomnia via Tweets: A Pre-Post Retrospective Pilot Study (Preprint)

Maghsoudi¹,

Nowakowski²,

Agrawal³

et al. 2021

Preprint

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BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a higher incidence of insomnia. In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. OBJECTIVE In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. METHODS We designed a pre-post retrospective study using public social media content from Twitter. We categorized tweets based on time into two intervals: prepandemic (01/01/2019 to 01/01/2020) and pandemic (01/01/2020 to 01/01/2021). We used NLP to analyze polarity (positive/negative) and intensity of emotions and also users’ tweets psychological states in terms of sadness, anxiety and anger by counting the words related to these categories in each tweet. Additionally, we performed temporal analysis to examine the effect of time on the users’ insomnia experience. RESULTS We extracted 268,803 tweets containing the word insomnia (prepandemic, 123,293 and pandemic, 145,510). The odds of negative tweets (OR, 1.31; 95% CI, 1.29-1.33), anger (OR, 1.19; 95% CI, 1.16-1.21), and anxiety (OR, 1.24; 95% CI: 1.21-1.26) were higher during the pandemic compared to prepandemic. The likelihood of negative tweets after midnight was higher than for other daily intevals, comprising approximately 60% of all negative insomnia-related tweets in 2020 and 2021 collectively. CONCLUSIONS Twitter users shared more negative tweets about insomnia during the pandemic than during the year before. Also, more anger and anxiety-related content were disseminated during the pandemic on the social media platform. Future studies using an NLP framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

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