<p>The figure represents the density of tweets received (left) and sent (right) as a function of the cumulative fraction of active users. For each day, data are normalized by the number of active users at that date. As a reference, the horizontal line corresponds to 50% of emitted/received tweets. Note that, on , less than 1% of the nodes receive half of the messages. On the contrary, the pattern of tweets sent hardly evolves from the beginning of the movement: 10% of the active nodes produce 50% of the messages. This asymmetry is coherent with the differences observed for the strength distributions.</p
<p>Probability density of user activity (number of daily tweets N) grouped by country (A) and langu...
<p>(a,c) The out- and in-degree statistics of user-to-user reply network. (b,d) The out- and in-degr...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...
<p>Colors correspond to different volume bins, see legend in <a href="http://www.plosone.org/article...
Part 3: Social Media and Mobile Applications of AIInternational audienceThe prediction of social med...
<p>Distribution of support ratios before and after resolving tweets for true and false rumours, as w...
<p>(A) Distribution of number of tweets per user during the whole time span. The distribution follo...
In this work we study Twitter data to understand influence dynamics in social networks. We define us...
In this study we use the concept of power law distribution to explore and analyze the network evolut...
<p>(A) The distribution of the number of Twitter followers for each legislator. (B) The distribution...
The differential distributions represent the difference between fractional counts of target and refe...
International audienceSocial networks can have asymmetric relationships. In the online social networ...
Information propagation on online social network focuses much attention in various domains as varied...
<p>Distribution of evidentiality ratios before and after resolving tweets for true and false rumours...
<p>(Left) A small proportion of observed messages for Week 5 (<25%) may explain the spike in the est...
<p>Probability density of user activity (number of daily tweets N) grouped by country (A) and langu...
<p>(a,c) The out- and in-degree statistics of user-to-user reply network. (b,d) The out- and in-degr...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...
<p>Colors correspond to different volume bins, see legend in <a href="http://www.plosone.org/article...
Part 3: Social Media and Mobile Applications of AIInternational audienceThe prediction of social med...
<p>Distribution of support ratios before and after resolving tweets for true and false rumours, as w...
<p>(A) Distribution of number of tweets per user during the whole time span. The distribution follo...
In this work we study Twitter data to understand influence dynamics in social networks. We define us...
In this study we use the concept of power law distribution to explore and analyze the network evolut...
<p>(A) The distribution of the number of Twitter followers for each legislator. (B) The distribution...
The differential distributions represent the difference between fractional counts of target and refe...
International audienceSocial networks can have asymmetric relationships. In the online social networ...
Information propagation on online social network focuses much attention in various domains as varied...
<p>Distribution of evidentiality ratios before and after resolving tweets for true and false rumours...
<p>(Left) A small proportion of observed messages for Week 5 (<25%) may explain the spike in the est...
<p>Probability density of user activity (number of daily tweets N) grouped by country (A) and langu...
<p>(a,c) The out- and in-degree statistics of user-to-user reply network. (b,d) The out- and in-degr...
<p>(a) The hourly pattern. (b) The weakly pattern. In both (a) and (b), insets show the absolute fra...