<p>(A) Tweets posted by hour and day of the week. (B) Number of active users by hour and day of the week. We used the UTC-offset information provided by the REST API to normalize time stamps to local time (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134270#pone.0134270.s001" target="_blank">S1 File</a>).</p
Abstract By modeling macro-economical indicators using digital traces of human activities on mobile ...
<p>The ambivalent tweets contain significantly more mentions, especially at 20:00 and 22:00.</p
This study advances insights into the distribution of tweets captured by Altmetric.com in terms of t...
In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspe...
Spatio-temporal dynamics of Twitter activity in American urban areas. Activity during an average day...
This paper uses geolocated Twitter histories from approximately 25,000 individuals in 6 different ti...
Social constraints have replaced the natural cycle of light and darkness as the main determinant of ...
<p>The horizontal axis corresponds to the hours of the day, in hourly bins from 0 (midnight) to 23 h...
<p>Time stamps in <i>Euro14</i> and <i>Italy</i> are in local time while time stamps in <i>Bulgaria1...
Spatio-temporal dynamics of Twitter activity in Asian urban areas. Activity during an average day ac...
This dataset contains top 50 trending topics (trends) of Twitter, obtained from Twitter Trends API i...
<p>From left to right: weekdays (aggregation from Monday to Thursday), Friday, Saturday and Sunday.<...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...
Social media activity in different geographic regions can expose a varied set of temporal patterns. ...
Twitter has gained phenomenal popularity over time, especially in Saudi Arabia, where it enjoys unma...
Abstract By modeling macro-economical indicators using digital traces of human activities on mobile ...
<p>The ambivalent tweets contain significantly more mentions, especially at 20:00 and 22:00.</p
This study advances insights into the distribution of tweets captured by Altmetric.com in terms of t...
In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspe...
Spatio-temporal dynamics of Twitter activity in American urban areas. Activity during an average day...
This paper uses geolocated Twitter histories from approximately 25,000 individuals in 6 different ti...
Social constraints have replaced the natural cycle of light and darkness as the main determinant of ...
<p>The horizontal axis corresponds to the hours of the day, in hourly bins from 0 (midnight) to 23 h...
<p>Time stamps in <i>Euro14</i> and <i>Italy</i> are in local time while time stamps in <i>Bulgaria1...
Spatio-temporal dynamics of Twitter activity in Asian urban areas. Activity during an average day ac...
This dataset contains top 50 trending topics (trends) of Twitter, obtained from Twitter Trends API i...
<p>From left to right: weekdays (aggregation from Monday to Thursday), Friday, Saturday and Sunday.<...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...
Social media activity in different geographic regions can expose a varied set of temporal patterns. ...
Twitter has gained phenomenal popularity over time, especially in Saudi Arabia, where it enjoys unma...
Abstract By modeling macro-economical indicators using digital traces of human activities on mobile ...
<p>The ambivalent tweets contain significantly more mentions, especially at 20:00 and 22:00.</p
This study advances insights into the distribution of tweets captured by Altmetric.com in terms of t...
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