Publication date
May 2015
DOIAbstract
<p>We show the number of tweets measured in a time window, Δ<i>T</i> = 10min, for a few brands. Note the regular daily variation and the irregular bursty behavior.</p
<p>We show the number of tweets measured in a time window, Δ<i>T</i> = 10min, for a few brands. Note the regular daily variation and the irregular bursty behavior.</p
Human behaviour is highly individual by nature, yet statistical structures are emerging which seem t...
<div><p>We draw a parallel between hashtag time series and neuron spike trains. In each case, the pr...
We draw a parallel between hashtag time series and neuron spike trains. In each case, the process pr...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...
(A) Minute-by-minute changes in Twitter volume for a sample episode (NY Med). Shaded areas denote co...
<p>Time series is per minute and normalized by the total number of tweets. In the plot of individual...
<p>Sudden spikes in frequency indicate the impact of continuous posting of tweets by bots. The numbe...
<p>Tweet probability distribution for March 11, 2011, the day of the Tohoku-oki earthquake, showing ...
Many studies have shown that social data such as tweets are a rich source of information about the r...
In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspe...
Detecting underlying trends in time series is important in many settings, such as market analysis (s...
Online social media such as the micro-blogging site Twitter has become a rich source of real-time da...
<p>(A) Distribution of number of tweets per user during the whole time span. The distribution follo...
<p>The time series are extracted from 56-day observation period(<i>x</i>-axis = days; <i>y</i>-axis ...
This work provides a novel measurement-based analysis of the tweet arrival traffic process at Twitte...
Human behaviour is highly individual by nature, yet statistical structures are emerging which seem t...
<div><p>We draw a parallel between hashtag time series and neuron spike trains. In each case, the pr...
We draw a parallel between hashtag time series and neuron spike trains. In each case, the process pr...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...
(A) Minute-by-minute changes in Twitter volume for a sample episode (NY Med). Shaded areas denote co...
<p>Time series is per minute and normalized by the total number of tweets. In the plot of individual...
<p>Sudden spikes in frequency indicate the impact of continuous posting of tweets by bots. The numbe...
<p>Tweet probability distribution for March 11, 2011, the day of the Tohoku-oki earthquake, showing ...
Many studies have shown that social data such as tweets are a rich source of information about the r...
In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspe...
Detecting underlying trends in time series is important in many settings, such as market analysis (s...
Online social media such as the micro-blogging site Twitter has become a rich source of real-time da...
<p>(A) Distribution of number of tweets per user during the whole time span. The distribution follo...
<p>The time series are extracted from 56-day observation period(<i>x</i>-axis = days; <i>y</i>-axis ...
This work provides a novel measurement-based analysis of the tweet arrival traffic process at Twitte...
Human behaviour is highly individual by nature, yet statistical structures are emerging which seem t...
<div><p>We draw a parallel between hashtag time series and neuron spike trains. In each case, the pr...
We draw a parallel between hashtag time series and neuron spike trains. In each case, the process pr...
Add to ORKG