Abstract

When a piece of information (microblog, photograph, video, link, etc.) starts to spread in a social network, an important question arises: will it spread to "viral" proportions - where "viral" is defined as an order-of-magnitude increase. However, several previous studies have established that cascade size and frequency are related through a power-law - which leads to a severe imbalance in this classification problem. In this paper, we devise a suite of measurements based on "structural diversity" - the variety of social contexts (communities) in which individuals partaking in a given cascade engage. We demonstrate these measures are able to distinguish viral from non-viral cascades, despite the severe imbalance of the data for this problem. Further, we leverage these measurements as features in a classification approach, successfully predicting microblogs that grow from 50 to 500 reposts with precision of 0.69 and recall of 0.52 for the viral class - despite this class comprising under 2% of samples. This significantly outperforms our baseline approach as well as the current state-of-the-art. Our work also demonstrates how we can tradeoff between precision and recall.

Original languageEnglish (US)
Title of host publicationProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
PublisherAssociation for Computing Machinery, Inc
Pages1610-1613
Number of pages4
ISBN (Print)9781450338547
DOIs
StatePublished - Aug 25 2015
EventIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France
Duration: Aug 25 2015Aug 28 2015

Other

OtherIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
CountryFrance
CityParis
Period8/25/158/28/15

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications

Cite this

Guo, R., Shaabani, E., Bhatnagar, A., & Shakarian, P. (2015). Toward order-of-magnitude cascade prediction. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 1610-1613). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808797.2809358