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

机译:利用跨域社交媒体分析来了解电视主题的受欢迎程度

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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.
机译:随着有吸引力的网络活动的推出,我们观看电视的方式正在发生变化,这些活动将用户从电视转移到其他媒体。社交多媒体和用户生成的内容正在极大地改变内容价值链的所有阶段(生产,分发和消费)。我们提出了一个概念级别的集成框架,在该框架中,可以共同表示用户在不同社交媒体上的活动,并且可能会丰富外部知识,例如从《电子程序指南》中提取的信息或可用的本体论领域知识。集成框架将知识图作为其核心数据模型。它跟踪活跃用户,他们谈论的电视事件,他们在活动中提及的概念以及他们之间存在的不同关系。还捕获了时间关系,以便能够对观察到的活动进行时间分析。数据模型允许进行不同类型的分析和定义全局指标,其中不同媒体上的活动与成功程度相一致。

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