An Min Ho, Kim Min-Gyu, Kim Jueon, Chang Seheon, Lee Dong Yun, Park Rae Woong
Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.
Department of Medical Sciences, Graduate School of Ajou University, Suwon, Republic of Korea.
PLoS One. 2025 Sep 4;20(9):e0318464. doi: 10.1371/journal.pone.0318464. eCollection 2025.
Antidepressants play a crucial role in treating mental health disorders such as depression and anxiety. Understanding of patients' perspective on antidepressants is essential for improving treatment outcomes; however, year-to-year change in the public's perception of antidepressants remains unclear. We aimed to analyze changes in public sentiments and predominant perceptions regarding antidepressants using artificial intelligence pipeline.
This study analyzed online discussions related to antidepressants on Reddit from January 1, 2009, to December 31, 2022. Antidepressant-associated communities were explored to collect a list of discussions relevant to antidepressant therapy. Discussion topics on antidepressants were identified using BERTopic, and the sentiments were analyzed using a RoBERTa model. Trends were assessed using the Mann-Kendall test to evaluate shifts in sentiments over time.
We analyzed 429,510 antidepressant-related discourse over 14 years and found a predominance in negative sentiments. Key discussion topics include the benefits and side effects of antidepressants, experiences with drug switching, and specific concerns regarding bupropion therapy. In trend analyses, negative sentiments decreased, while neutral sentiments increased over time. This aligns with a decline in the annual proportion of topics associated with side effects within each cluster.
Negative perceptions toward antidepressants are prevalent on social media, mainly focusing on efficacy and side effects. However, a decade-long analysis shows a decline in negative sentiments, with an increase in neutral sentiments with a downturn in yearly proportion of side-effected related topics within each cluster. These trends and information may help improve strategies to address barriers to antidepressant use and adherence.
抗抑郁药在治疗抑郁症和焦虑症等心理健康障碍方面发挥着关键作用。了解患者对抗抑郁药的看法对于改善治疗效果至关重要;然而,公众对抗抑郁药看法的逐年变化仍不明确。我们旨在使用人工智能管道分析公众对抗抑郁药的情绪变化和主要看法。
本研究分析了2009年1月1日至2022年12月31日Reddit上与抗抑郁药相关的在线讨论。探索了与抗抑郁药相关的社区,以收集与抗抑郁治疗相关的讨论列表。使用BERTopic识别抗抑郁药的讨论主题,并使用RoBERTa模型分析情绪。使用曼-肯德尔检验评估趋势,以评估情绪随时间的变化。
我们在14年中分析了429,510条与抗抑郁药相关的论述,发现负面情绪占主导。关键讨论主题包括抗抑郁药的益处和副作用、换药经历以及对安非他酮治疗的具体担忧。在趋势分析中,负面情绪减少,而中性情绪随时间增加。这与每个聚类中与副作用相关主题的年度比例下降一致。
社交媒体上对抗抑郁药的负面看法普遍存在,主要集中在疗效和副作用上。然而,长达十年的分析显示负面情绪有所下降,中性情绪增加,且每个聚类中与副作用相关主题的年度比例呈下降趋势。这些趋势和信息可能有助于改进应对抗抑郁药使用和依从性障碍的策略。