LookingGlass

Kim, Nyunsu; Gokalp, Sedat; Davulcu, Hasan; Woodward, Mark

doi:10.1145/2492517.2500275

Cited by 2 publications

References 14 publications

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A comparison of methods for cascade prediction

Guo

Shakarian

2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Information cascades exist in a wide variety of platforms on Internet. A very important real-world problem is to identify which information cascades can "go viral". A system addressing this problem can be used in a variety of applications including public health, marketing and counter-terrorism. As a cascade can be considered as compound of the social network and the time series. However, in related literature where methods for solving the cascade prediction problem were proposed, the experimental settings were often limited to only a single metric for a specific problem formulation. Moreover, little attention was paid to the run time of those methods. In this paper, we first formulate the cascade prediction problem as both classification and regression. Then we compare three categories of cascade prediction methods: centrality based, feature based and point process based. We carry out the comparison through evaluation of the methods by both accuracy metrics and run time. The results show that feature based methods can outperform others in terms of prediction accuracy but suffer from heavy overhead especially for large datasets. While point process based methods can also run into issue of long run time when the model can not well adapt to the data. This paper seeks to address issues in order to allow developers of systems for social network analysis to select the most appropriate method for predicting viral information cascades.

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A comparison of methods for cascade prediction

Guo

Shakarian

2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Leveraging Cross-Domain Social Media Analytics to Understand TV Topics Popularity

Pensa

Sapino

Schifanella

et al. 2016

IEEE Comput. Intell. Mag.

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The way we watch television is changing with the introduction of attractive Web activities that move users away from TV to other media. The social multimedia and user-generated contents are dramatically changing all phases of the value chain of contents (production, distribution and consumption). We propose a concept-level integration framework in which users' activities on different social media are collectively represented, and possibly enriched with external knowledge, such as information extracted from the Electronic Program Guides, or available ontological domain knowledge. The integration framework has a knowledge graph as its core data model. It keeps track of active users, the television events they talk about, the concepts they mention in their activities, as well as different relationships existing among them. Temporal relationships are also captured to enable temporal analysis of the observed activity. The data model allows different types of analysis and the definition of global metrics in which the activity on different media concurs with the measure of success.

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LookingGlass

Cited by 2 publications

References 14 publications

A comparison of methods for cascade prediction

A comparison of methods for cascade prediction

Leveraging Cross-Domain Social Media Analytics to Understand TV Topics Popularity

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