@Article{信息:doi 10.2196 / / jmir。4476，作者=“McIver, David J和Hawkins, Jared B和Chunara, Rumi和Chatterjee, Arnaub K和Bhandari, Aman和Fitzgerald, Timothy P和Jain, Sachin H和Brownstein, John S”，标题=“使用Twitter描述睡眠问题”，期刊=“J Med Internet Res”，年=“2015”，月=“Jun”，日=“08”，卷=“17”，数=“6”，页=“e140”，关键词=“睡眠问题;社交媒体;失眠;新方法;情绪;背景:失眠等睡眠问题影响着超过5000万美国人，并可能导致严重的健康问题，包括抑郁和肥胖，还可能增加受伤的风险。像Twitter这样的社交媒体平台在研究和识别疾病和社会现象方面提供了令人兴奋的潜力。目的:我们的目的是确定社交媒体是否可以作为一种方法来进行关注睡眠问题的研究。方法:根据推文中出现的几个关键词，如失眠、“睡不着”、安必恩等，收集并整理推文，以确定用户是否表现出睡眠问题的迹象。 Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. Results: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues. Conclusions: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered. ", issn="1438-8871", doi="10.2196/jmir.4476", url="//www.mybigtv.com/2015/6/e140/", url="https://doi.org/10.2196/jmir.4476", url="http://www.ncbi.nlm.nih.gov/pubmed/26054530" }