Publication Type

Conference Proceeding Article

Publication Date

10-2016

Abstract

In social media, the magnitude of information propagation hinges on the virality and susceptibility of users spreading and receiving the information respectively, as well as the virality of information items. These users' and items' behavioral factors evolve dynamically at the same time interacting with one another. Previous works however measure the factors statically and independently in a restricted case: each user has only a single adoption on each item, and/or users' exposure to items are observable. In this work, we investigate the inter-relationship among the factors and users' multiple adoptions on items to propose both new static and temporal models for measuring the factors without requiring user - item exposure. These models are designed to cope with even more realistic propagation scenarios where an item may be propagated many times from the same user(s) to the same other user(s). We further propose an incremental model for measuring the factors in large data streams. We evaluated the proposed models and existing models through extensive experiments on a large Twitter dataset covering information propagation in one month. The experiments show that our proposed models can effectively mine the behavioral factors and outperform the existing ones in a propagation prediction task. The incremental model is shown more than 10 times faster than the temporal model, while still obtains very similar results.

Keywords

Virality, Susceptibility, User behavior, Information propagation

Discipline

Computer Sciences | Databases and Information Systems | Social Media

Research Areas

Data Management and Analytics

Publication

CIKM 2016: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management: Indianapolis, October 24-28, 2016

First Page

1059

Last Page

1068

ISBN

9781450340731

Identifier

10.1145/2983323.2983800

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2983323.2983800

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