ライフサイエンスコーパス: tweet

1 commenting on the shared information (quote tweeting).

2 high winds in a locality using only a single tweet.

3 d scoring system) in the sources shared in a tweet.

4 al foodborne outbreaks in massive historical tweets.

5 ntific research literature, news, blogs, and tweets.

6 s from the average hour a participant posted Tweets.

7 nd speeds based only on the language used in tweets.

8 equent meaningful word on "allergy rhinitis" tweets.

9 ical mobility measured by public geolocation tweets.

10 , leading to an analysis of nearly 4 million tweets.

11 t enhancing sentiment analysis in text-based Tweets.

12 informing on the emotional tone of assessed tweets.

13 geolocated sample of 149 thousand Ukrainian tweets.

14 data from 310 adult participants and 18,288 Tweets.

15 footprints, such as their Facebook Likes or Tweets.

16 diovascular disease and the content of these Tweets.

17 n to identify medically-relevant concepts in tweets.

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

19 Twitter bots and to analyze global trends in tweets.

20 ed with a smaller number of "climate change" tweets.

21 h 85 handles (94.4%) returning at least 3000 tweets.

22 Engagement rates were higher for negative tweets.

23 A total of 274 tweets (0.097%) from 67 handles (74.4%) used a hashtag t

24 first tweet was published in 2018 and only 4 tweets (1.5%) predated the killing of George Floyd.

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

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

27 cebook (1,063,298 links) and Twitter (44,529 tweets, 24,007 users) and two behavioral experiments (14

28 We categorized 413 tweets (4.62%, n = 8934) as uncivil.

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

30 ional information about these genes and (vi) tweeting a weekly report about the most trending genes o

31 nd-to-end system, i.e. given a collection of tweets, a list of prevalent concerns is returned, provid

32 Users who Tweeted about cardiovascular disease were more likely to

33 arly self-promotion over six years using 23M tweets about 2.8M research papers authored by 3.5M scien

34 o analyze emotions expressed or solicited in tweets about 2020 Black Lives Matter protests.

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

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

37 emotions perceived in this specific domain: tweets about protests in May 2020 following the death of

38 eptical accounts generated a larger share of tweets about vaccines, increased in virality, and contin

39 ly constant opinions, but that the number of tweeting accounts grows in each category during controve

40 ilding sector using 256,717 English-language tweets across a 13-year time frame.

41 ed on a large-scale analysis of social media tweets across ~ 200 million users across 7 events.

42 Upon analyzing 653 k HPV-related post-2020 tweets, adverse effects, personal anecdotes, and vaccine

43 ck whether the conceptions of honesty in the tweets align with those in their replies.

44 f Republican senators, especially when those tweets also mention political outgroups.

45 Separate tweet analysis showed that vaccination rates tracked reg

46 simple form of coordination that we call co-tweeting and co-retweeting.

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

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

49 kes as our study cases, we collected 510,579 tweets and 45,770 Reddit posts (including 1437 submissio

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

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

52 Using four samples of tweets and news articles (n = 6,183), we show that ChatG

53 s 15 datasets (n = 47,925 manually annotated tweets and news headlines), we tested whether different

54 the rises in the number of COVID-19 related tweets and officially reported deaths by Public Health E

55 re the conceptions of honesty of a sample of tweets and replies using computational text processing,

56 ecrease negative sentiment in climate change tweets and the emotions related to worry and anxiety, su

57 n Twitter (7331 users and 12.7 million total tweets) and two preregistered behavioral experiments (N

58 ge collection of wind-related Twitter posts (tweets) and UK Met Office wind speed observations to exp

59 Movie Database (IMDB) movie reviews and Urdu tweets are examined in this study using Urdu sentiment a

60 eractions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implem

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

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

63 This tool was used to collect and analyze tweets at scale in real time to study sentiment and key

64 at the intensity of initial COVID-19 related tweet attention at the beginning of the pandemic across

65 of book descriptions) and the typicality of tweets authored by US Congress members in the Democratic

66 by analysing the emotional content of their tweets before and after they explicitly report experienc

67 1,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling t

68 First, we studied tweets by elected legislators from major political parti

69 7,000 hour of radio content and 26.6 million tweets by elite journalists, politicians, and general us

70 Most uncivil tweets contained profanity (n = 135, 32.7%), sexually ex

71 ler, an ongoing curation of over 100 billion tweets containing 1 trillion 1-grams from 2008 to 2021.

72 ied in a semi-supervised manner to recognise tweets containing location keywords within the unlabelle

73 By analysing 132,230 tweets containing the hashtag #archaeology from 2021 to

74 se on Twitter, with education being the most tweeted content topic.

75 papers, suggesting that criticism-expressing tweets could contain factual information about problemat

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

77 If a tweet did not contain any hashtags from these categories

78 gfully worse mental wellbeing than those who Tweeted during the daytime.

79 average hour that a participant posted their Tweets explained less of the variation in their depressi

80 Our study involved examining 56,789 Tweets, focusing on sentiment shifts in both textual con

81 ralized way across approximately 10 years of tweets for all the hospital handles within the data set.

82 e framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural

83 Twitter or to a control arm (with no active tweeting from ESC channels) and aimed to assess whether

84 rs holding office from 2013 to 2021 (861,104 tweets from 140 senators), we identify a psycholinguisti

85 cation, we analyzed Twitter data (16,830,997 tweets from 5,541,457 users).

86 We examined around 2 million tweets from 522,893 persons in the UK from November 2020

87 A total of 281 850 tweets from 90 unique social media accounts were collect

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

89 We retrieved tweets from England on "allergy," "asthma," and "allergi

90 ained COVID-19 related regionally geolocated tweets from Italian, Spanish, and United States regions.

91 Study 1 examines over 500,000 tweets from over 160,000 Twitter users about 46 unambigu

92 rospective longitudinal cohort study design, tweets from the top 100 ranked hospitals were collected,

93 ding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to

94 Participants were shown tweets generated by six female celebrities, counterbalan

95 erall, our results show that the majority of tweets had a negative tone, and that the days with large

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

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

98 In 2008, the US Embassy in Beijing began tweeting hourly air-quality information from a newly ins

99 uencer) and the informational novelty of the tweet in the diffusion of several key types of informati

100 Here, we analyze around 13 million geotagged tweets in 49 cities across the US from the first few mon

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

102 Here, we use a dataset of 171 million tweets in the five months preceding the election day to

103 0 (36.8%) generated those 413 uncivil posts, tweeting inappropriately at least once over a period of

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

105 vestigation is immediately followed by Trump tweeting increasingly about unrelated issues.

106 A case study on tweets indicating foodborne illnesses showed that the di

107 es implementing six principal functions: (i) tweeting information about under-studied genes including

108 EnrichrBot is a bot that tracks and tweets information about human genes implementing six pr

109 g correlations and regressions of geolocated Tweet Intensity (TI) during the initial social media att

110 For each day, we break tweets into 1-, 2-, and 3-grams across 100+ languages, g

111 ical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories.

112 Among the tweets labeled BLM, the first tweet was published in 201

113 plore the relationship between tweet volume, tweet language and wind speeds in the UK.

114 des explanations in rumor detection based on tweet-level texts only without referring to a verified n

115 iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association stud

116 t with the hypothesis that President Trump's tweets may also successfully divert the media from topic

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

118 ge-based classification models to filter out tweets not related to COVID-19 and those unlikely publis

119 Analyzing every tweet of all US senators holding office from 2013 to 202

120 further find that greed communication in the tweets of Democratic senators is associated with greater

121 self-promotion is associated with increased tweets of papers compared to no self-promotion, the incr

122 eting compared to greed communication in the tweets of Republican senators, especially when those twe

123 g this model to a large corpus (10.5 million tweets) of misinformation events that occurred during th

124 are identified within the corpus of COVID-19 tweets, of which the themes ranged from retail to govern

125 at the days with larger numbers of published tweets often coincided with major U.S. events related to

126 Tweets on "allergic rhinitis" displayed a seasonal patte

127 We observed seasonal patterns for tweets on "allergic rhinitis," both in their frequency a

128 llergy," 7767 tweets on "asthma," and 11,974 tweets on "allergic rhinitis." Food-related words were p

129 rho = 0.866) and sentiment (rho = -0.474) of tweets on "allergic rhinitis." For tweets on "asthma," n

130 We analysed a total of 13,605 tweets on "allergy," 7767 tweets on "asthma," and 11,974

131 is." Food-related words were preponderant on tweets on "allergy," while "eyes" was the most frequent

132 ted the correlation between the frequency of tweets on "asthma" and "allergic rhinitis" and English s

133 a total of 13,605 tweets on "allergy," 7767 tweets on "asthma," and 11,974 tweets on "allergic rhini

134 0.474) of tweets on "allergic rhinitis." For tweets on "asthma," no such patterns/correlations were o

135 In this study, we aimed to analyse tweets on allergic conditions, comparing them with surve

136 of words frequently occurring together) for tweets on each assessed condition.

137 ent in the contents of 210 million geotagged tweets on the Chinese largest microblog platform Sina We

138 We analyzed language contained in 730,000 tweets on the following dimensions of bias: (1) threat a

139 ment and emotional content of climate change tweets on the other.

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

141 1000 tweets was randomly selected from 4859 tweets over 7 non-consecutive days.

142 The two tweeting patterns were modeled using biexponential model

143 .5-60) participants and 312 (IQR, 205-427.5) tweets per session.

144 method to detect covert and overt signals in tweets posted before the 2020 US presidential election a

145 study was to describe the characteristics of tweets posted by nurses and nursing students on Twitter

146 nce, content, and characteristics of uncivil tweets posted by nurses and nursing students.

147 noff election, and those who liked or shared tweets promoting fraud-related conspiracy theories were

148 Tweet publication dates ranged from 2009 to 2020.

149 ation study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr da

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

151 installed over 50 monitors around the world, tweeting real-time reports on air quality in those locat

152 appropriation comprises more than 50% of the tweets referencing a given preprint.

153 These findings reflect the low signal of tweets regarding the Black community and social justice

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

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

156 We characterized each Tweet relative to estimated user demographics.

157 checking sites, and their associated 289,202 tweets/retweets generated by 176,362 users, we find that

158 Content analysis of tweets revealed a steady rise in rumination and emotiona

159 ntiment analysis on a subset of mask related tweets revealed sentiment spikes corresponding to major

160 250,000 Congressional remarks and 1 million tweets revealed that Black and Latinx conservatives (det

161 However, the proportion of negative tweets reverted back to an average of around 40% within

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

163 ger causality test further demonstrated that tweet sentiment scores may help predict vaccination rate

164 However, the number of high-wind tweets shows a strong temporal correlation with local wi

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

166 esty used in replies align with those of the tweets, suggesting a "contagion".

167 The user tweeted that his car kept stopping abruptly at a particu

168 Nurses and nursing students share uncivil tweets that could tarnish the image of the profession an

169 Tweet: The authors discuss harm reduction strategies and

170 Participants who, on average, Tweeted through the night (23:00 to 05:00) showed meanin

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

172 tim-blaming was influenced by victim Initial Tweet Valence (greater victim-blaming associated with mo

173 ed severity was influenced by victim Initial Tweet Valence, Volume of Abuse received, and observer Ma

174 We used a 3 (Initial Tweet Valence; negative, neutral, positive) x 2 (Abuse V

175 that has focused on the relationship between tweet volume and severity.

176 rvations to explore the relationship between tweet volume, tweet language and wind speeds in the UK.

177 rning system using the distribution of total tweet volume.

178 cative of weather conditions, independent of tweet volume.

179 Among the tweets labeled BLM, the first tweet was published in 2018 and only 4 tweets (1.5%) pre

180 vidence the average hour participants posted Tweets was associated with depressive symptoms, anxiety

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

182 The analysis of 8934 tweets was performed by a combination of SAS 9.4 for des

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

184 Here using nearly a billion tweets, we analyse the change in Twitter's news media la

185 g a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level

186 ltmetric Attention Score and number of users tweeting were positive predictors for the number of cita

187 gher volumes of depression and schizophrenia tweets were associated with higher numbers of same-day c

188 Tweets were manually identified based on 4 categories: B

189 (v) responding to mentions of human genes in tweets with additional information about these genes and

190 Tweets with average novelty spread the least.

191 , and optimism), which are more prevalent in tweets with explicit pro-BlackLivesMatter hashtags and c

192 Tweets with high novelty propagated the most, primarily

193 The proportion of tweets with negative vaccine content varied, with reduct

194 Our findings reveal that tweets with positive sentiment and non-threatening langu

195 simple text classifier to detect 'high-wind' tweets with reasonable accuracy; this can be used to det

196 Each handle returned at least 1279 tweets, with 85 handles (94.4%) returning at least 3000

197 zed similarity score to examine alignment of tweets within and between communities.

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