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Network structure and community evolution on Twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami.

Lu X, Brelsford C - Sci Rep (2014)

Bottom Line: We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average.While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content.This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.

View Article: PubMed Central - PubMed

Affiliation: 1] College of Information System and Management, National University of Defense Technology, 410073 Changsha, China [2] Flowminder Foundation, 17177 Stockholm, Sweden [3] Department of Public Health Sciences, Karolinska Institutet, 17177 Stockholm, Sweden [4] Department of Sociology, Stockholm University, 10691 Stockholm, Sweden.

ABSTRACT
To investigate the dynamics of social networks and the formation and evolution of online communities in response to extreme events, we collected three datasets from Twitter shortly before and after the 2011 earthquake and tsunami in Japan. We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average. By investigating the evolution of communities, we find that the behavior of joining or quitting a community is far from random: users tend to stay in their current status and are less likely to join new communities from solitary or shift to other communities from their current community. While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content. This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.

Show MeSH
Community dynamics.(a)–(c) show the number of survived, dissolved and split communities before the earthquake and (d)–(f) show the number of survived, formed and merged communities after the earthquake. Dashed lines represent corresponding communities from the  model.
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f5: Community dynamics.(a)–(c) show the number of survived, dissolved and split communities before the earthquake and (d)–(f) show the number of survived, formed and merged communities after the earthquake. Dashed lines represent corresponding communities from the model.

Mentions: The above analysis on network density and node degree shows that individuals in the Japanese network became more active in posting tweets and interacting with others after the earthquake. To further investigate these changes in social behavior, in this section we evaluate the dynamics of communities: the social groups which are formed among intensely interactive users and are detected by the Infomap algorithm2223. To identify significant patterns, we generate a hypothesis scenario in which the size distribution of communities is fixed both before and after the earthquake, but each user joins some detected community at random after the earthquake. Based on changes in each users community between the truly observed community in the in the before case and the hypothesis scenario in the after case, the number of communities that survive, dissolve or form, and split or merge is then plotted in Fig. 5.


Network structure and community evolution on Twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami.

Lu X, Brelsford C - Sci Rep (2014)

Community dynamics.(a)–(c) show the number of survived, dissolved and split communities before the earthquake and (d)–(f) show the number of survived, formed and merged communities after the earthquake. Dashed lines represent corresponding communities from the  model.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4209381&req=5

f5: Community dynamics.(a)–(c) show the number of survived, dissolved and split communities before the earthquake and (d)–(f) show the number of survived, formed and merged communities after the earthquake. Dashed lines represent corresponding communities from the model.
Mentions: The above analysis on network density and node degree shows that individuals in the Japanese network became more active in posting tweets and interacting with others after the earthquake. To further investigate these changes in social behavior, in this section we evaluate the dynamics of communities: the social groups which are formed among intensely interactive users and are detected by the Infomap algorithm2223. To identify significant patterns, we generate a hypothesis scenario in which the size distribution of communities is fixed both before and after the earthquake, but each user joins some detected community at random after the earthquake. Based on changes in each users community between the truly observed community in the in the before case and the hypothesis scenario in the after case, the number of communities that survive, dissolve or form, and split or merge is then plotted in Fig. 5.

Bottom Line: We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average.While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content.This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.

View Article: PubMed Central - PubMed

Affiliation: 1] College of Information System and Management, National University of Defense Technology, 410073 Changsha, China [2] Flowminder Foundation, 17177 Stockholm, Sweden [3] Department of Public Health Sciences, Karolinska Institutet, 17177 Stockholm, Sweden [4] Department of Sociology, Stockholm University, 10691 Stockholm, Sweden.

ABSTRACT
To investigate the dynamics of social networks and the formation and evolution of online communities in response to extreme events, we collected three datasets from Twitter shortly before and after the 2011 earthquake and tsunami in Japan. We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average. By investigating the evolution of communities, we find that the behavior of joining or quitting a community is far from random: users tend to stay in their current status and are less likely to join new communities from solitary or shift to other communities from their current community. While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content. This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.

Show MeSH