Semantics-enabled social network analysis

鉴于通过对呼叫中心社交媒体平台的系统监测和分析来了解客户兴趣的关键意义，该拟议研究项目将解决与社交媒体分析相关的重要研究挑战。我们的工作将集中在制定一个多方面的框架，将共同考虑社会互动（连接的网络结构），时间行为（活动的时间顺序）和社会内容的语义（用户内容的潜在含义），以执行社会媒体分析。我们的工作将包括开发和/或适应i）分析社交网络的网络结构的图形技术; ii）发现和建模社交媒体内容的潜在语义的语义网技术;和iii）模式挖掘算法，可以指向社交平台内重复出现的行为和上下文模式。我们研究工作的重点将是开发同时考虑这三个方面的技术;因此，将社交网络图视为一个不断发展的图，其中包含代表个人用户的异构顶点，社交人工制品（如用户生成的内容）和潜在语义概念。我们对问题的多方面的方法，同时考虑网络结构，内容语义和时间方面是这个建议的强有力的研究重点。

Given the pivotal significance of understanding customers' interests through systematic monitoring and analysis of social media platforms for call centers, this proposed research project will address important research challenges related to social media analytics. Our work will concentrate on formulating a multi-faceted framework that will collectively consider social interactions (network structure of connections), temporal behaviour (chronological order of activities), and semantics of social content (the underlying meaning of user content) in order to perform social media analytics. Our work will consist of the development and/or adaptation of i) graph techniques that analyze the network structure of social networks; ii) Semantic Web technology that would discover and model latent semantics of social media content; and iii) pattern mining algorithms that can point to recurring behavioural and contextual patterns within the social platform. The focus of our research work will be on developing techniques that consider these three aspects at the same time; therefore, viewing the social network graph as an evolving graph with heterogeneous vertices representing individual users, social artefacts such as user generated content, and latent semantic concepts. Our multi-faceted approach towards the problem that simultaneously considers network structure, content semantics and temporal aspects is the strong research focus of this proposal.