Research on modeling time series text corpora has typically focused on predicting what text will come next , but less well studied is predicting when the next text event will occur . In this paper we address the latter case , framed as modeling continuous inter-arrival times under a log-Gaussian Cox process , a form of inhomogeneous Poisson process which captures the varying rate at which the tweets arrive over time . In an application to rumour modeling of tweets surrounding the 2014 Ferguson riots , we show how interarrival times between tweets can be accurately predicted , and that incorporating textual features further improves predictions . Twitter is a popular micro-blogging service which provides real-time information on events happening across the world . Evolution of events over time can be monitored there with applications to disaster management , journalism etc . For example , Twitter has been used to detect the occurrence of earthquakes in Japan through user posts ( Sakaki et al . , 2010 ) . Modeling the temporal dynamics of tweets provides useful information about the evolution of events . Inter-arrival time prediction is a type of such modeling and has application in many settings featuring continuous time streaming text corpora , including journalism for event monitoring , real-time disaster monitoring and advertising on social media . For example , journalists track several rumours related to an event . Predicted arrival times of tweets can be applied for ranking rumours according to their activity and narrow the interest to investigate a rumour with a short interarrival time over that of a longer one . Modeling the inter-arrival time of tweets is a challenging task due to complex temporal patterns exhibited . Tweets associated with an event stream arrive at different rates at different points in time . For example , Figure 1a shows the arrival times ( denoted by black crosses ) of tweets associated with an example rumour around Ferguson riots in 2014 . Notice the existence of regions of both high and low density of arrival times over a one hour interval . We propose to address inter-arrival time prediction problem with log-Gaussian Cox process ( LGCP ) , an inhomogeneous Poisson process ( IPP ) which models tweets to be generated by an underlying intensity function which varies across time . Moreover , it assumes a non-parametric form for the intensity function allowing the model complexity to depend on the data set . We also provide an approach to consider textual content of tweets to model inter-arrival times . We evaluate the models using Twitter rumours from the 2014 Ferguson unrest , and demonstrate that they provide good predictions for inter-arrival times , beating the baselines e.g. homogeneous Poisson Process , Gaussian Process regression and univariate Hawkes Process . Even though the central application is rumours , one could apply the proposed approaches to model the arrival times of tweets corresponding to other types of memes , e.g. discussions about politics . This paper makes the following contributions : 1 . Introduces log-Gaussian Cox process to predict tweet arrival times . 2 . Demonstrates how incorporating text improves results of inter-arrival time prediction . This paper introduced the log-Gaussian Cox processes for the problem of predicting the interarrival times of tweets . We showed how text from posts helps to achieve significant improvements . Evaluation on a set of rumours from Ferguson riots showed efficacy of our methods comparing to baselines . The proposed approaches are generalizable to problems other than rumours , e.g. disaster management and advertisement campaigns .

Challenge: Modeling the event inter-arrival time of tweets is challenging due to complex temporal patterns but few works aim to predict the next text event occurrence.
Approach: They propose to apply a log-Gaussian Cox process model which captures the varying arriving rate over time coupled with the textual contents of tweets.
Outcome: The proposed model outperforms baseline models on an inter-arrival time prediction task around a riots rumour and shows that it improves with textual features.