CourseSpecifications
Valid as from the academic year 2024-2025

Social Media and Web Analytics (F000799)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

B (semester 2)

Englishseminarlecturegroup work

Gent

Lecturers in academic year 2024-2025
Bogaert, Matthias

lectureseminargroup work

EB23

Offered in the following programmes in 2024-2025
Master of Science in Business Engineering(main subject Data Analytics)
Master of Science in Business Engineering (Double Degree)(main subject Data Analytics)
Exchange programme in Economics and Business Administrationlecturer-in-charge
crdts
646offeringABA

Teaching languages
EnglishKeywords
Social media analytics, Web analytics, Network analysis, Sentiment analysis, PageRank,
Social Media Optimization, Network visualization, Natural language processing
Position of the course
This course focuses on the three levels of social media analytics (network, user, message) andthe various aspects related to web analytics. Students will collect their own data from social
media and the web and perform several analyses in R. The global objective of the course is totrain students in the collection and analysis of social media and web data.
ContentsTopics include, but are not limited to:Social Media Analytics: the Network• Network analysis (e.g., adjacency, QAP regression, Exponential-Family Random Graph• Models)• Network visualization (e.g., Fruchterman-Reingold)• Network clustering (e.g., edge betweenness method)• Describing networks in numbers (e.g., density, transitivity, degree, betweenness)• Network representation learning• Data collection from social media
Social Media Analytics: the User
• Event attendance prediction• Tie strength• Movie wachting behavior and box office sales• Donation behavior
Social Media Analytics: the Message(Approved)1• Text mining• Word clouds• Sentiment analysis• Retweet analysis• Word networks• Topic modeling• Word embeddings• Deep learning for sentiment analysis• Transformer models
Web Analytics:• PageRank• Web crawling• Web Mining
Initial competences
Mastery of the basics of R. The course builds on the competencies and skills acquired in thecourse Machine Learning.
Final competences
1 Knowing and understanding the theoretical concepts and analytical methods.
2 Extracting data from social media and the web.
3 Analyzing of social media and web data.
4 Interpreting, communicating and evaluating your findings in a business context.
5 Being able to use R to apply these analytical methods
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Group work, Seminar, Lecture
Study material
Type: Slides
Name: Slides theory
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Available on Ufora : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No
Type: Software
Name: Code files in R
Indicative price: Free or paid by faculty
Optional: noReferences
Several scientific articles:• Newman, M. E. J., Girvan, M., Feb. 2004. Finding and evaluating community structure in• networks. Physical Review E 69 (2), 026126.
• Fruchterman, T. M., Reingold, E. M., 1991. Graph Drawing by Force-directed Placement.
• Software-Practice and Experience 21 (11), 1129–1164.
• Lismont, J., Ram, S., Vanthienen, J., Lemahieu, W., & Baesens, B. (2018). Predicting• interpurchase time in a retail environment using customer-product networks: An empirical• study and evaluation. Expert Systems with Applications, 104, 22-32.
• Ballings, M., Van den Poel, D., 2015. CRM in social media: Predicting increases in Facebook• usage frequency. Eur. J. Oper. Res. 244, 248–260.
• Ballings, M., Van den Poel, D., Bogaert, M., 2016. Social media optimization: Identifying an• optimal strategy for increasing network size on Facebook. Omega-Int. J. Manage. Sci. 59,• 15–25.
• Bogaert, M., Ballings, M., Van den Poel, D., 2016. The added value of Facebook friends data(Approved)2• in event attendance prediction. Decis. Support Syst. 82, 26–34.
• Schetgen, L., Bogaert, M., & Van den Poel, D. (2021). Predicting donation behavior:• Acquisition modeling in the nonprofit sector using Facebook data. Decision Support• Systems, 141, 113446.
Course content-related study coaching
Dodona exercises are made available for all sessions. Several group exercises are solved anddiscussed during the sessions. Students receive extensive tutoring and feedback.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment
Examination methods in case of periodic assessment during the second examination period
Written assessment
Examination methods in case of permanent assessment
Oral assessment, Participation, Presentation, Peer and/or self assessment, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods
During class, the students are asked to prepare and present several small exercises.
The group project is a comprehensive exercise in a realistic context where the key concepts
used in social media analytics are applied.
Students taking the course at 4 credits will be given a small group assignment.
The written exam will investigate whether the student knows and understands the aspects ofsocial media and web analytics that were discussed during class. Both theoretical exercises aswell as applications will be part of the exam.
Calculation of the examination mark6 SP
Written exam (40%) + group project with presentation and written report adjusted by peer
assessment (55%) + participation and presentation of exercises during class (5%).
4 SP
These students will receive a smaller group assisngment. However, the content and structureof the course remains the same which resolves in a higher impact of the class participation andthe written exam on the final grade.
Written exam (50%) + group project with presentation and written report adjusted by peer
assessment (40%) + participation and presentation of exercises during class (10%).
When the student obtains less than 10/20 for either the written exam or the group project, they
can no longer pass the course unit as a whole. If the total score does turn out to be a mark often or more out of twenty, this is reduced to the highest fail mark (9/20).

(Approved)3