Short Title:Social Media Analysis
Full Title:Social Media Analysis
Language of Instruction:English
Module Code:COIS H4001
 
Credits: 5
Field of Study:Computer Science
Module Delivered in 7 programme(s)
Reviewed By:FINBARR FEENEY
Module Author:JOHN CARDIFF
Module Description:To instill in the student the fundamental principles, issues, opportunities and challenges in enabling information to be modelled, published, and reasoned about on the web; to develop an understanding of the means by which data can be integrated and manipulated as a unified body of information; to be cognisant of the value of, and potential applications of user generated content published on the web.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Describe the importance of Social Media tools and techniques and explain how new knowledge can be elicited from user generated content
LO2 Describe the language and measures of networks as a basis for quantitative analysis.
LO3 Be able to perform behaviour and content analysis on networks, and to make inferences about actors such as mutual trust and tie strength.
LO4 Discuss and apply visualisation techniques to effectively analyse, filter, and interpret social media networks.
LO5 Explain the importance of the Web 2.0 and Semantic Web tools, techniques and applications.
 

Module Content & Assessment

Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical/Skills Evaluation There are likely to be two practical assessments for this module. The following are indicators of the types of assessments that will be used to assess the learning outcomes: Construct a dataset from a specific social media source such as Twitter (using software such as NodeXL) and to perform subsequent analysis to investigate specifc forms of relationship between actors; given specific information on a web application, to design an ontology to support and model the informational requirements and where diverse sources of information exist to design a strategy for integrating their contents into a unified structure. 1,2,3,4 50.00 n/a
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3,4,5 50.00 End-of-Semester

TU Dublin – Tallaght Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture 2.00 Every Week 2.00
Lab Lab 1.00 Every Week 1.00
Independent Learning Independent Learning 3.00 Every Week 3.00
Total Weekly Learner Workload 6.00
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecturer/Lab Lecture or Lab as required 2.00 Every Week 2.00
Independent Learning Independent learning 4.00 Every Week 4.00
Total Weekly Learner Workload 6.00
Total Weekly Contact Hours 2.00
 

Module Resources

Required Book Resources
  • Hansen, D., Shneiderman, B.and Smith, M. 2010, Social Media Networks with NodeXL: Insights from a Connected World, Morgan Kaufmann.
  • J Golbeck 2013, Analyzing the Social Web, Elsevier
  • Allemang, D., Hendler, J. 2011, Semantic web for the working ontologist modeling in RDF, RDFS and OWL, Wiley
  • Brath, R., Jonker, D. 2015, Graph Analysis and Visualisation [ISBN: 978111884584]
Recommended Book Resources
  • Antoniou, G., van Harmelen, F. 2008, A Semantic Web Primer, MIT Press
  • Jalal Kawash 2015, Online Social Media Analysis and Visualization, Springer
  • Gabor Szabo, Oscar Boykin 2015, Social Media Data Mining and Analytics, Wiley
This module does not have any article/paper resources
Other Resources
  • Web: Online resources, which will vary year to year
 

Module Delivered in

Programme Code Programme Semester Delivery
TA_KCOMP_B (1 year add on) Bachelor of Science (Honours) in Computing 7 Mandatory
TA_KACTM_B Bachelor of Science (Honours) in Computing with Information Technology Management 7 Elective
TA_KCITM_B Bachelor of Science (Honours) in Computing with Information Technology Management - Year 4 ( Add on) 7 Elective
TA_KITMG_B Bachelor of Science (Honours) IT Management (add On) 7 Elective
TA_KACOD_B Bachelor of Science (Hons) in Computing with Data Analytics 7 Mandatory
TA_KCODA_B Bachelor of Science (Hons) in Computing with Data Analytics (Add-On) 1 Mandatory
TA_KCOMP_HD Higher Diploma in Science in Computing 2 Group Elective 3