Software Open Access
Xingsheng He; Yu-Ru Lin
Abstract (our paper)
There has been growing interest in leveraging Web-based social and communication technologies for better crisis response. How might the Web platforms be used as an observatory to systematically understand the dynamics of the public’s attention during disaster events? And how could we monitor such attention in a cost-effective way? In this work, we propose an ‘attention shift network’ framework to systematically observe, measure, and analyze the dynamics of collective attention in response to real-world exogenous shocks such as disasters. Through tracing hashtags that appeared in Twitter users’ complete timeline around several violent terrorist attacks, we study the properties of network structures and reveal the temporal dynamics of the collective attention across multiple disasters. Further, to enable an efficient monitoring of the collective attention dynamics, we propose an effective stochastic sampling approach that accounts for the users’ hashtag adoption frequency, connectedness and diversity, as well as data variability. We conduct extensive experiments to show that the proposed sampling approach significantly outperforms several alternative methods in both retaining the network structures and preserving the information with a small set of sampling targets, suggesting the utility of the proposed method in various realistic settings.
We collected tweets from the local users around multiple shocking terrorist attacks in 2015 and 2016 - the Paris attacks on 13 November 2015, the San Bernardino shooting on 2 December 2015, the Brussels bombings on 22 March 2016 and the Orlando nightclub shooting on 12 June 2016. For the Paris attack event, we chose two other cities New York City and London for comparison. We also collected the followers’ tweets for the two major presidential candidates during the 2016 US presidential election.
For each event, we have the user activity during the event week with the event day centered in the middle.
This data set was created for our study. If you make use of this data set, please cite:
He X, Lin Y R. Measuring and monitoring collective attention during shocking events[J]. EPJ Data Science, 2017, 6(1): 30.