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.
Leveraging Cross-Domain Social Media Analytics to Understand TV Topics Popularity
PENSA, Ruggero Gaetano;SAPINO, Maria Luisa;SCHIFANELLA, CLAUDIO;
2016-01-01
Abstract
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.File | Dimensione | Formato | |
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