Fraction of tweets mentioning others at different hours.
- Yue Hu (201714)
- Jichang Zhao (644842)
- Junjie Wu (393541)
Publication date
January 2016
DOIAbstract
<p>The ambivalent tweets contain significantly more mentions, especially at 20:00 and 22:00.</p
<p>The ambivalent tweets contain significantly more mentions, especially at 20:00 and 22:00.</p
This paper uses geolocated Twitter histories from approximately 25,000 individuals in 6 different ti...
Early spreaders are users that have been among the first 1/10 percentile of users to share a debunk....
<p>Time stamps in <i>Euro14</i> and <i>Italy</i> are in local time while time stamps in <i>Bulgaria1...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...
<p>The horizontal axis corresponds to the hours of the day, in hourly bins from 0 (midnight) to 23 h...
<p>We show the number of tweets measured in a time window, Δ<i>T</i> = 10min, for a few brands. Note...
<p>(A) Tweets posted by hour and day of the week. (B) Number of active users by hour and day of the ...
<p>Increasing amount of tweets from midday (12:00) to midnight (00:00) is shown in the yellow shaded...
<p>Shows how many times different usernames were mentioned in the collected tweets. The data is pres...
In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspe...
Early spreaders are users that have been among the first 1/30 percentile of users to share a fake ne...
This study advances insights into the distribution of tweets captured by Altmetric.com in terms of t...
While topics on Twitter may be categorized according to their predictability and sustainability, som...
The potential of Twitter in the dissemination of scientific publications has raised considerable sch...
<p>Tweet probability distribution, and the rankings of the popularity and the enhancement of key ter...
This paper uses geolocated Twitter histories from approximately 25,000 individuals in 6 different ti...
Early spreaders are users that have been among the first 1/10 percentile of users to share a debunk....
<p>Time stamps in <i>Euro14</i> and <i>Italy</i> are in local time while time stamps in <i>Bulgaria1...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...
<p>The horizontal axis corresponds to the hours of the day, in hourly bins from 0 (midnight) to 23 h...
<p>We show the number of tweets measured in a time window, Δ<i>T</i> = 10min, for a few brands. Note...
<p>(A) Tweets posted by hour and day of the week. (B) Number of active users by hour and day of the ...
<p>Increasing amount of tweets from midday (12:00) to midnight (00:00) is shown in the yellow shaded...
<p>Shows how many times different usernames were mentioned in the collected tweets. The data is pres...
In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspe...
Early spreaders are users that have been among the first 1/30 percentile of users to share a fake ne...
This study advances insights into the distribution of tweets captured by Altmetric.com in terms of t...
While topics on Twitter may be categorized according to their predictability and sustainability, som...
The potential of Twitter in the dissemination of scientific publications has raised considerable sch...
<p>Tweet probability distribution, and the rankings of the popularity and the enhancement of key ter...
This paper uses geolocated Twitter histories from approximately 25,000 individuals in 6 different ti...
Early spreaders are users that have been among the first 1/10 percentile of users to share a debunk....
<p>Time stamps in <i>Euro14</i> and <i>Italy</i> are in local time while time stamps in <i>Bulgaria1...
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