Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study.

BACKGROUND: Mental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group. OBJECTIVE: By measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health. METHODS: We explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification. RESULTS: We found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time. CONCLUSIONS: These results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.

Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy.

Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject's historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day's 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016-2021. We measure Trump's narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy-the rate at which a population's stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd's murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography.

The Implementation and Effectiveness of PathElective.com.

The COVID-19 pandemic put most in-person pathology electives on-hold as departments adapted to changes in education and patient care. To address the subsequent void in pathology education, we created a free, virtual, modular, and high-quality pathology elective website. Website traffic from June 1, 2020, to October 1, 2020, was monitored using the built-in analyses on Squarespace. Twitter engagement was analyzed using Twitter analytics and the Symplur Social Graph Score. A voluntary satisfaction survey was sent to all PathElective users and results were analyzed. During this time, the site saw 25 467 unique visitors, over 34 988 visits, 181 302 page views, and 4449 subscriptions from 99 countries. Countries with the highest traffic are the United States (14 682), India (5210), and the Philippines (2195). PathElective's Twitter social graph score increased from 63.59 to 89.3 with the addition of 1637 followers. Data from surveyed users (n = 177) show most to be pathology residents (41%). Most subscribers (89%) are committed to a career in pathology. The majority heard of the website via Twitter (55%). Almost half of those surveyed engaged with the PathTwitter community on Twitter and of those who participated, 99% found that interaction useful. In all survey questions surrounding satisfaction and usefulness, a large majority of the users were either satisfied or very satisfied. PathElective is a novel pathology elective that offers a unique opportunity to educate medical students and residents from around the globe and demonstrates high effectiveness and satisfaction among users.

COVID-19 Presentation in an Acute Motor Incomplete Spinal Cord Injury in Inpatient Rehabilitation.

ABSTRACT: Spinal cord injuries lead to impairment of the central regulation of respiratory muscle activity. This impairs the cough response, which can increase the risk of complications if infected with coronavirus disease 2019. This case describes a 32-yr-old man with an acute traumatic motor incomplete spinal cord injury, C4 American Spinal cord Injury Association Impairment Scale D D, in an inpatient rehabilitation facility who presented with only a fever. Initial infectious workup was negative, and he continued to have elevated temperatures with no other symptoms. He was then tested for coronavirus disease 2019 and found to be positive. This is the first documented case that identifies this potentially lethal disease in an acute motor incomplete spinal cord injury in an inpatient rehabilitation setting. We further discuss how physiatrists need to be aware of milder presentation of coronavirus disease 2019 in patients with spinal cord injuries. Inability to recognize this disease can lead to delayed diagnosis and asymptomatic spread in an inpatient rehabilitation setting.

How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter.

In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 2000 day-scale time series of 1- and 2-grams across 24 languages on Twitter that are most 'important' for April 2020 with respect to April 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for 'virus' in January 2020 followed by a decline through February and then a surge through March and April. The world's collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations.