@Article{信息:doi 10.2196 / /公共健康。4488，作者="Adrover, Cosme和Bodnar, Todd和Huang, zhujie和Telenti, Amalio和Salath{\'e}， Marcel"，标题="识别艾滋病毒药物治疗的不良影响和使用Twitter的相关情绪"，期刊="JMIR公共卫生监测"，年="2015"，月=" 7月"，日="27"，卷="1"，数="2"，页="e7"，关键词="Twitter;艾滋病毒;艾滋病;药物警戒;mTurk;背景:社交媒体平台日益被视为广泛健康问题的数据来源。由于Twitter的公共性质，它对公共卫生监测特别有意义。然而，推特等社交媒体平台的公共性可能会成为公共卫生监测的障碍，因为人们可能不愿公开披露自己的健康信息。在艾滋病毒/艾滋病等与某种程度的耻辱有关的疾病的情况下，这一点尤其令人关切。 Objective: The objective of the study is to assess whether adverse effects of HIV drug treatment and associated sentiments can be determined using publicly available data from social media. Methods: We describe a combined approach of machine learning and crowdsourced human assessment to identify adverse effects of HIV drug treatment solely on individual reports posted publicly on Twitter. Starting from a large dataset of 40 million tweets collected over three years, we identify a very small subset (1642; 0.004{\%}) of individual reports describing personal experiences with HIV drug treatment. Results: Despite the small size of the extracted final dataset, the summary representation of adverse effects attributed to specific drugs, or drug combinations, accurately captures well-recognized toxicities. In addition, the data allowed us to discriminate across specific drug compounds, to identify preferred drugs over time, and to capture novel events such as the availability of preexposure prophylaxis. Conclusions: The effect of limited data sharing due to the public nature of the data can be partially offset by the large number of people sharing data in the first place, an observation that may play a key role in digital epidemiology in general. ", issn="2369-2960", doi="10.2196/publichealth.4488", url="http://publichealth.www.mybigtv.com/2015/2/e7/", url="https://doi.org/10.2196/publichealth.4488", url="http://www.ncbi.nlm.nih.gov/pubmed/27227141" }