Abstract
In response to an extreme event, individuals on social media demonstrate interesting behaviors depending on their backgrounds. By making use of the large-scale datasets of posts and search queries collected from Twitter and GoogleTrends, we first identify the distinct categories of human collective online concerns and durations based on the distributions of solo tweets and new incremental tweets about events. Such a characterization enables us to gain a better understanding of dynamic changes in human behaviors corresponding to different types of events. Next, we observe the heterogeneity of individual responses to events through measuring the fraction of event-related tweets relative to the tweets released by an individual, and thus empirically confirm the heterogeneity assumption as adopted in the meta-population models for characterizing collective responses to events. Finally, based on the correlations of information entropy in different regions, we show that the observed distinct responses may be caused by their different speeds in information propagation. In addition, based on the detrended fluctuation analysis, we find that there exists a self-similar evolution process for the collective responses within a region. These findings have provided a detailed account for the nature of distinct human behaviors on social media in presence of extreme events.
Highlights
MethodsBy snowball sampling using Twitter REST API [27], we first extracted publicly available Twitter data from March 8 to March 31, 2011, as shown in Table A in S1 Appendix
Data Availability Statement: Data are stored at Figshare
Events in Fig A(a3) and Fig A(a5) in S1 Appendix have the similar dynamic changes in the tweet count over time, they attract different concerns because of the different compositions of time series
Summary
By snowball sampling using Twitter REST API [27], we first extracted publicly available Twitter data from March 8 to March 31, 2011, as shown in Table A in S1 Appendix In this period, there existed many events that can be used to differentiate collective online behaviors in presence of social unrests (e.g., Libya crisis) and extreme events (e.g., earthquake, tsunami or nuclear crisis). For some uncertain events, especially for their unpredictable subsequent consequences (e.g., nuclear leaking or Lybia crisis), people share information via social media and take part in event-related discussions for gaining more information and reduce their uncertainties During such events, we can extract more solo tweets (which mean more different opinions related to an event are emerged) and find that such discussions will last for a relatively long time.
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