<abstract xmlns="http://www.w3.org/1999/xhtml">

<p> Most analyses of the social structure of a network implicitly assume that the relationships in the network are relatively stable. We present evidence that this is not the case. The focal network of this study grew in bursts rather than monotonously over time, and the bursts were highly localized. Links were added and deleted in nearby localities and are not randomly dispersed throughout the network. Also changes in structure lead to simultaneous changes in self-stated interests of its members. For SNA marketing applications the findings suggest interesting improvements. Local bursts around a seed can change the structure of the network dramatically and therefore a marketer’s influence and his chances of success. Therefore, network measurements should be carried out more frequently and closer to the actual implementation of a seeding campaign. To detect these abrupt, dramatic local changes marketers also use a finer resolution. Further, recommendation algorithms that simultaneously account for changes in network structure and content should be applied.</p>
</abstract>

Most analyses of the social structure of a network implicitly assume that the relationships in the network are relatively stable. We present evidence that this is not the case. The focal network of this study grew in bursts rather than monotonously over time, and the bursts were highly localized. Links were added and deleted in nearby localities and are not randomly dispersed throughout the network. Also changes in structure lead to simultaneous changes in self-stated interests of its members. For SNA marketing applications the findings suggest interesting improvements. Local bursts around a seed can change the structure of the network dramatically and therefore a marketer’s influence and his chances of success. Therefore, network measurements should be carried out more frequently and closer to the actual implementation of a seeding campaign. To detect these abrupt, dramatic local changes marketers also use a finer resolution. Further, recommendation algorithms that simultaneously account for changes in network structure and content should be applied.

Seeding on Moving Ground: How Understanding Network Instability Can Improve Message Dissemination

NIM Marketing Intelligence Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Most analyses of the social structure of a network implicitly assume that the relationships in the network are relatively stable. We present evidence that this...

Seeding on Moving Ground: How Understanding Network Instability Can Improve Message Dissemination

Publié en ligne: 24 oct. 2017

Pages: 42 - 47

DOI: https://doi.org/10.1515/gfkmir-2017-0017

Mots clés
Social Network Analysis, Network Dynamics, Seeding Campaigns, Influencer Marketing

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Seeding on Moving Ground: How Understanding Network Instability Can Improve Message Dissemination

Publié en ligne: 24 oct. 2017

Pages: 42 - 47

DOI: https://doi.org/10.1515/gfkmir-2017-0017

Mots clésSocial Network Analysis, Network Dynamics, Seeding Campaigns, Influencer Marketing

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Social Network Analysis, Network Dynamics, Seeding Campaigns, Influencer Marketing