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Web and Social Media Analysis/Mining

Web and Social Media Analysis/Mining

Level
Advanced, Specialised, Theory, Algorithmic.
This topic concerns AI for analysis of WWW and social media data, as well as knowledge mining from large-scale on-line sources.
Web and Social Media Analysis/Mining

Learning outcomes

Content /
Knowledge

Students should be able to:

  • Understand/describe graph-theoretic methods for complex network analysis, basic relevant concepts and algorithms (e.g., for link analysis, centrality measures, etc.) as well as their applications in WWW and social media platforms.
  • Understand/describe random graph models, as well as algorithms for community detection, node classification and network information diffusion.
  • Understand/describe Semantic Web technologies and standards (e.g., RDF, SPARQL, OWL).
  • Understand/describe content-based information retrieval methods across various modalities.
  • Understand/describe recommender systems, the most important relevant concepts and algorithms and their applications in on-line platforms.
Methodological
skills
Students should be able to:
  • Analyse and develop (in C/C++, MATLAB, R or Python) the taught algorithms, by practically applying their gained knowledge in a systematic manner.
  • Evaluate implementations of the taught algorithms, by employing common and appropriate task-specific metrics.
  • Use SPARQL.
Transferrable/
Application
Students should be able to:
  • Work effectively with others in an interdisciplinary and/or international team.
  • Design and manage individual projects.
  • Clearly and succinctly communicate their ideas to technical audiences.