Modeling Tweet Arrival Times using Log-Gaussian Cox Processes

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 250–255, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. Modeling Tweet Arrival Times using Log-Gaussian Cox Processes Michal Lukasik,1 P.K. Srijith,1 Trevor Cohn,2 and Kalina Bontcheva1 1Department of Computer Science, The University of Sheffield 2Department of Computing and Information Systems, The University of Melbourne {m.lukasik, pk.srijith, k.bontcheva}@shef.ac.uk t.cohn@unimelb.edu.au Abstract Research on modeling time series text cor- pora 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 ad- dress the latter case, framed as modeling continuous inter-arrival times under a log- Gaussian Cox process, a form of inhomo- geneous Poisson process which captures the varying rate at which the tweets ar- rive over time. In an application to ru- mour modeling of tweets surrounding the 2014 Ferguson riots, we show how inter- arrival times between tweets can be ac- curately predicted, and that incorporating textual features further improves predic- tions. 1 Introduction Twitter is a popular micro-blogging service which provides real-time information on events happen- ing across the world. Evolution of events over time can be monitored there with applications to dis- aster 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 evo- lution 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 monitor- ing, real-time disaster monitoring and advertising on social media. For example, journalists track several rumours related to an event. Predicted ar- rival times of tweets can be applied for ranking rumours according to their activity and narrow the interest to investigate a rumour with a short inter- arrival 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 pro- cess (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 com- plexity to depend on the data set. We also pro- vide 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 pro- vide good predictions for inter-arrival times, beat- ing the baselines e.g. homogeneous Poisson …

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