Course Summary
Social media and networks are prevalent in our lives. Prominent examples include Facebook, Twitter, LinkedIn and Pinterest. ??Due to their widespread adaption, they provide a great source of behavioural, social and opinion information and have spawned a new analytic field and industry in social media and network analytics. ??This has benefited users and organisations in a large variety of fields.
In this course, you will learn how to analyse social media and networks, about different types of analysis that are possible and the algorithms and techniques to perform these analyses. As much of social media and networks are unstructured data, the focus will be in analysis of unstructured data and you will learn about:?
- Social network analytics: much data is relational, allowing many exciting forms of networks analysis. This course will cover topics in social network analysis such as centrality (identifying the important nodes/people in the network), network clustering analysis and influence propagation (if you have to market something, which people in the network do you give samples to so they can spread how good your products are).
- Text analytics: data are textual also, e.g., tweets in Twitter. Hence it is important to analyse them from a textual perspective. In this course you will engage with how to apply basic natural language processing (NLP) to extract meaningful document representations, then use them to understand tweets, their authors, and perform sentiment analysis.