Castilla-Puentes Ruby, Dagar Anjali, Villanueva Dinorah, Jimenez-Parrado Laura, Valleta Liliana Gil, Falcone Tatiana
Center for Clinical and Translational Science and Training, University of Cincinnati Academic Health Center, Cincinnati, OH, USA.
Neuroscience- Janssen Research & Development, LLC, Titusville, NJ, USA.
Ann Gen Psychiatry. 2021 Nov 29;20(1):50. doi: 10.1186/s12991-021-00372-0.
Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open-source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology.
Advanced machine-learning empowered methodology was used to mine and structure open-source digital conversations of self-identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. In this cross-sectional study, only unique posts were included in the analysis and were primarily analyzed for their tone, topic, and attitude towards depression between the two groups using descriptive statistical tools.
A total of 441,000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray "negative tone" due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about 'living with' depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics.
In this first of its kind big data analysis of nearly a half-million digital conversations about depression using machine learning, we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.
数字对话能够提供有关抑郁症患者在正式临床环境之外的态度的独特信息，并有助于为医疗治疗规划生成有用信息。我们的研究旨在利用机器学习技术，探索西班牙裔抑郁症患者在开源数字对话中的大数据，特别是比较西班牙裔和非西班牙裔对抑郁症的态度。
采用先进的机器学习方法，挖掘和构建自我认定为西班牙裔和非西班牙裔抑郁症患者的开源数字对话，这些患者参与了关于抑郁症的语气、话题和态度的对话。搜索范围限于源自美国互联网协议（IP）地址的12个月内的内容。在这项横断面研究中，分析仅纳入独特的帖子，并主要使用描述性统计工具分析两组之间关于抑郁症的语气、话题和态度。
共发布了441,000条关于抑郁症的独特对话，其中西班牙裔患者的对话有43,000条（9.8%）。来源分析显示，48%的对话源自主题网站，而社交媒体上的占16%。西班牙裔和非西班牙裔之间存在几个关键差异。更高比例的西班牙裔患者在对话中因抑郁症表现出“负面语气”（66%对非西班牙裔的39%），表现出听天由命/绝望的态度（44%对30%），且是关于“与抑郁症共存”（44%对25%）。西班牙裔和非西班牙裔患者对话背后作者确定的情绪存在重要差异。
在这项首次使用机器学习对近50万条关于抑郁症的数字对话进行的大数据分析中，我们发现西班牙裔比非西班牙裔更频繁地参与关于抑郁症的负面、听天由命和绝望态度的在线对话。
