Using Reddit as a population-level “mental health tracker” during the COVID pandemic
Figure 1. Mention of words related to COVID-19 in mental health support groups. The timeline landmarks were chosen from the NBC News timeline given that US users are the most prevalent on Reddit. Confirmed COVID-19 cases around the world, China and the United States are displayed. Low et al (2020). Using machine learning to track […]

Figure 1. Mention of words related to COVID-19 in mental health support groups. The timeline landmarks were chosen from the NBC News timeline given that US users are the most prevalent on Reddit. Confirmed COVID-19 cases around the world, China and the United States are displayed. Low et al (2020).

Using machine learning to track the impact of the pandemic on mental health (MIT News):

Dealing with a global pandemic has taken a toll on the mental health of millions of people. A team of researchers from MIT and Harvard University have shown that it can measure these effects by analyzing the language people use to express their anxiety online.

Using machine learning to analyze the text of more than 800,000 Reddit posts, researchers were able to identify changes in the tone and content of the language people were using during the first wave of the Covid-19 pandemic, in January. to April 2020. Their analysis revealed several key changes in conversations about mental health, including an overall increase in discussions about anxiety and suicide.

"We found that there were these natural clusters that emerged linked to suicidality and loneliness, and the number of positions in these clusters more than doubled during the pandemic compared to the same months of the previous year, this which is of great concern, ”said Daniel Low, a graduate student in the Biosciences and Speech and Hearing Technologies program at Harvard and MIT and lead author of the study.

The study:

Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Increased Health Anxiety on Reddit During COVID-19: Observational Study (Journal of Medical Internet Research). From the summary:

  • Context: The COVID-19 pandemic is impacting mental health, but it is unclear how people with different types of mental health issues were affected differently when the initial wave of cases hit.
  • Goal: The aim of this study is to leverage natural language processing (NLP) to characterize changes in 15 of the world's largest mental health support groups (e.g., r / schizophrenia, r / SuicideWatch, r / Depression) found on the Reddit website, as well as 11 non-mental health groups (eg, R / PersonalFinance, r / conspiracy) during the initial phase of the pandemic.
  • Results: We found that the r / HealthAnxiety forum showed spikes in COVID-19 postings in early January, about 2 months before other support groups started posting about the pandemic. There were many characteristics that increased significantly during COVID-19 for specific groups, including “economic stress”, “isolation” and “home” categories, while others like “movement” have significantly increased. decreases. We found that attention deficit / hyperactivity disorder, eating disorder and anxiety support groups exhibited the most negative semantic change during the pandemic among all mental health groups. . Health anxiety became a general topic on Reddit thanks to independent, supervised and unsupervised machine learning analyzes ... Using unsupervised clustering, we found that the suicidality and loneliness clusters more than doubled. in the number of messages during the pandemic.
  • Conclusions: Using a wide range of NLP techniques and analyzing a baseline of prepandemic messages, we uncovered patterns of how specific mental health issues manifested in language, identified users at risk, and revealed the distribution of concerns across Reddit, which could help improve resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and therefore may have utility during the pandemic in courses and other world-changing events such as elections and protests.

The authors further explain that “in many subredits, we have seen a significant increase in token usage related to isolation (eg, 'lonely', 'can't see anyone', 'quarantine "), Economic tensions (for example," rent, "" Debt "," pay the bills ") and at home (" refrigerator "," pet "," lease "), and a decrease in the related lexicon to movement (eg, "walk", "visit", "travel" ")."

The study in context:

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