Characterizing Sleep Issues Using Twitter.
Characterizing Sleep Issues Using Twitter.
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DOI：
10.2196/jmir.4476
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发表时间：
2015-06-08
影响因子：
7.4
通讯作者：
Brownstein JS
中科院分区：
文献类型：
--
作者：
McIver DJ;Hawkins JB;Chunara R;Chatterjee AK;Bhandari A;Fitzgerald TP;Jain SH;Brownstein JS
失眠等睡眠问题影响着超过5000万美国人，可能导致严重的健康问题，包括抑郁和肥胖，并可能增加受伤的风险。Twitter等社交媒体平台为研究和识别疾病和社会现象提供了令人兴奋的潜力。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研究，重点关注睡眠问题。Twitter帖子被收集和策划，以确定用户是否表现出睡眠问题的迹象，这是基于推文中存在的几个关键词，如失眠，“睡不着”，Ambien等。用户的推文包含任何关键字被指定为有自我识别的睡眠问题（睡眠组）。没有自我识别的睡眠问题的用户（非睡眠组）是从不包含预定义的单词或短语的推文中选择的，这些单词或短语被用作睡眠问题的代理。用户数据，如推文数量，朋友，追随者和位置，以及推文的时间和日期被收集。此外，每个推文的情绪和每个用户的平均情绪被确定，以研究非睡眠组和睡眠组之间的差异。结果发现，睡眠组用户在Twitter上的活跃度明显较低（P=.04），与其他用户相比，朋友较少（P<.001），追随者较少（P<.001），调整每个用户帐户的活跃时间后。睡眠组用户在典型的睡眠时间比其他人更活跃，这可能表明他们有睡眠困难。睡眠组用户在他们的推文中也有显着较低的情绪（P<.001），表明睡眠和心理社会问题之间可能存在关系。我们已经展示了一种研究睡眠问题的新方法，可以快速，经济高效和可定制的数据收集。
Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, “can’t sleep”, Ambien, and others. 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. 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. We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.