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Characterizing Sleep Issues Using Twitter.

McIver DJ, Hawkins JB, Chunara R, Chatterjee AK, Bhandari A, Fitzgerald TP, Jain SH, Brownstein JS - J. Med. Internet Res. (2015)

Bottom Line: 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 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Boston Children's Hospital, Harvard Medical School, Boston, MA, United States. david.mciver@childrens.harvard.edu.

ABSTRACT

Background: 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.

Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues.

Methods: 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.

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.

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Related in: MedlinePlus

Proportion of statuses posted each hour by user group.
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figure1: Proportion of statuses posted each hour by user group.

Mentions: A larger proportion of tweets between 12 am-5:59 am were from sleep group users (P<.001), as well as between 6 pm-11:59 pm (P<.001 for both). Conversely, more tweets from between 6 am-11:59 am were from non-sleep group users (P<.001). An hourly proportion of statuses posted by both groups is presented in Figure 1. In addition, a larger proportion of tweets that were submitted on Saturday, Sunday, Monday, and Tuesday, were from sleep group users (P<.001), whereas tweets on Wednesday, Thursday, and Friday, were more often from non-sleep group users (P<.001) (Figure 2).


Characterizing Sleep Issues Using Twitter.

McIver DJ, Hawkins JB, Chunara R, Chatterjee AK, Bhandari A, Fitzgerald TP, Jain SH, Brownstein JS - J. Med. Internet Res. (2015)

Proportion of statuses posted each hour by user group.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526927&req=5

figure1: Proportion of statuses posted each hour by user group.
Mentions: A larger proportion of tweets between 12 am-5:59 am were from sleep group users (P<.001), as well as between 6 pm-11:59 pm (P<.001 for both). Conversely, more tweets from between 6 am-11:59 am were from non-sleep group users (P<.001). An hourly proportion of statuses posted by both groups is presented in Figure 1. In addition, a larger proportion of tweets that were submitted on Saturday, Sunday, Monday, and Tuesday, were from sleep group users (P<.001), whereas tweets on Wednesday, Thursday, and Friday, were more often from non-sleep group users (P<.001) (Figure 2).

Bottom Line: 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 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Boston Children's Hospital, Harvard Medical School, Boston, MA, United States. david.mciver@childrens.harvard.edu.

ABSTRACT

Background: 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.

Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues.

Methods: 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.

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.

Show MeSH
Related in: MedlinePlus