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Hype or hope? Ketamine for the treatment of depression: results from the application of deep learning to Twitter posts from 2010 to 2023

Summary

Researchers analyzed over 18,000 Twitter posts from 2010 to 2023 to understand what the public thinks about using ketamine to treat depression. They found that public opinion became much more positive after the FDA approved ketamine as a depression treatment in 2019. Most discussions consisted of personal stories from people who found ketamine helpful, especially those whose depression didn’t respond to other medications. While some people expressed caution and concerns, overall the public seems hopeful about ketamine’s potential.

Background

Depression is a major driver of global disability with significant public health implications. Traditional antidepressants have limitations including delayed onset and suboptimal response rates. Ketamine has emerged as a potential rapid-acting antidepressant, with the FDA approving esketamine for treatment-resistant depression in 2019.

Objective

To investigate societal perceptions of ketamine’s use in depression therapy by analyzing Twitter posts from January 1, 2010 to April 1, 2023, using deep learning and natural language processing techniques to identify prevalent topics and themes in public discourse.

Results

Analysis identified 12 topics grouped into three major themes: changing regulatory landscape (12.1%), cautious optimism (8.1%), and personal experiences (69.0%). A significant spike in discussions occurred post-FDA approval in 2019, with subsequent temporal trends showing sustained interest in personal accounts of benefits for treatment-resistant depression.

Conclusion

Public perception of ketamine for depression treatment is multifaceted and leans towards optimism regarding its therapeutic potential. While debates and concerns persist, the overarching sentiment reflects growing hope in ketamine as a viable treatment option, particularly for treatment-resistant depression cases.
  • Published in:Frontiers in Psychiatry,
  • Study Type:Social Media Analysis with Natural Language Processing,
  • Source: 10.3389/fpsyt.2024.1369727, PMC11117142, 38800065
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