Measuring and monitoring collective attention during shocking events

Xingsheng He,Yu-Ru Lin

doi:10.1140/epjds/s13688-017-0126-4

Xingsheng He, Yu-Ru Lin

Open Access

https://doi.org/10.1140/epjds/s13688-017-0126-4

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Abstract

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.

Highlights

The proliferation of Web-based social and communication technologies has provided an unprecedented opportunity for researchers to collect and study data about collective human behavior at large scales
We systematically study the collective attention during multiple shocking terrorist attack events in and and reveal several properties of network structures and temporal dynamics that are consistent across events
We formulate a new problem for efficient monitoring of the collective attention dynamics, and we propose a cost-efficient sampling strategy that takes the users’ hashtag adoption frequency, connectedness and diversity into account, with a stochastic sampling algorithm to cope with the variability of the sampling targets

Summary

Introduction

The proliferation of Web-based social and communication technologies has provided an unprecedented opportunity for researchers to collect and study data about collective human behavior at large scales. Figure illustrates the collective attention before and after the Paris attacks event based on how Paris users shift their attention to various topics - captured by the use of different hashtags. We propose a new sampling problem that samples the set of users while evaluating the sampling quality on their attention shift captured from the hashtag graphs.

Results

Conclusion