ライフサイエンスコーパス: tweet

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1 footprints, such as their Facebook Likes or Tweets.

2 diovascular disease and the content of these Tweets.

3 n to identify medically-relevant concepts in tweets.

4 eeting, re-tweeting and replying to existing tweets.

5 ation of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) c

6 Most Tweets (2338 of 2500 [93.5%]) were associated with a hea

7 Of the sample of 772 tweets, 83% (n = 640) were primarily categorized as a ge

8 Users who Tweeted about cardiovascular disease were more likely to

9 Main Outcomes and Measures: The volume of Tweets about cardiovascular disease and the content of t

10 Here, we collected and geocoded tweets about measles-mumps-rubella vaccine and classifie

11 idual and global level, such as tweeting, re-tweeting and replying to existing tweets.

12 tive users who generate more than 58 million tweets and 2.1 billion search queries every day.

13 ets, spam, and repeat users, we analyzed 772 tweets and calculated frequencies.

14 More happy tweets and lower caloric density of food tweets in a zip

15 ctive: To describe the volume and content of Tweets associated with cardiovascular disease as well as

16 Results: Of 550338 Tweets associated with cardiovascular disease, the terms

17 ers (e.g., Twitter users sending 500 million tweets/day), CI finds influencers in 2.5 hours on a sing

18 tion network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth

19 st percentage of happy and physically-active tweets had lower obesity prevalence-accounting for indiv

20 call data records, and geographically tagged tweets, have entered infectious diseases epidemiology as

21 ppy tweets and lower caloric density of food tweets in a zip code were associated with lower individu

22 ciated with a health topic; common themes of Tweets included risk factors (1048 of 2500 [41.9%]), awa

23 nguistic style, and context from participant tweets (N = 279,951) and built models using these featur

24 of approximately 10 billion English-language Tweets originating from US counties from July 23, 2009,

25 1000 tweets was randomly selected from 4859 tweets over 7 non-consecutive days.

26 ties at individual and global level, such as tweeting, re-tweeting and replying to existing tweets.

27 sponding network of individuals who posted a tweet related to the Higgs boson discovery.

28 k correlation with equivalent weekly data on Tweets related to disease or disease-related keyword sea

29 We characterized each Tweet relative to estimated user demographics.

30 We collected 422,094 tweets sent from Utah between April 2015 and March 2016.

31 After excluding ambiguous tweets, spam, and repeat users, we analyzed 772 tweets a

32 We use a collection of 37 million geolocated tweets to characterize the movement patterns of 180,000

33 A random subset of 2500 Tweets was hand-coded for content and modifiers.

34 A set of 1000 tweets was randomly selected from 4859 tweets over 7 non

35 on people who have generated several billion Tweets, yet little work has focused on the potential app

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