02805 Social graphs and interactions

2024/2025

Course information
Sociale grafer og interaktioner
English
10
MSc
Offered as a single course
Technological specialization course (MSc), Technology Entrepreneurship
Technological specialization course (MSc), Human-Centered Artificial Intelligence
Technological specialization course (MSc), Business Analytics
General competence course (MSc), Human-Centered Artificial Intelligence
Programme specific course (MSc), Technology Entreneurship
Programme specific course (MSc), Business Analytics
Programme specific course (MSc), Human-Centered Artificial Intelligence
Autumn E5 (Wed 8-17)
Campus Lyngby
Lectures and group work with projects.
13 weeks
Evaluation of exercises/reports
The grading is based on an overall evaluation of exercises (50%) and final project report (50%). Specifically, the grade is based on individualized group-reports.
All Aids - with access to the internet
7 step scale , internal examiner
02821
02815/02467
02100/02101 , The course involves work with high level programming languages (e.g. Python), so practical programming experience is recommended (e.g. in Python/​Java/​JavaScript/​C/​C++)
Sune Lehmann , Ph. (+45) 4525 3904 , sljo@dtu.dk
01 Department of Applied Mathematics and Computer Science
At the Studyplanner
Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More infor http://www.groendyst.dtu.dk/english
General course objectives
The overall objective of the course is to make students able to access and analyze user generated data and text - as well as analyze and model social relations using network theory.
Learning objectives
A student who has met the objectives of the course will be able to:
  • Explore web APIs for data collection.
  • Apply a high level programming language (e.g. Python) to utilize such APIs for data acquisition.
  • Apply natural language processing to represent statistical structures in text and analyze the content.
  • Apply and discuss the main strategies for detecting sentiment in media (e.g. text, music, images, etc).
  • Apply standard algorithms to recommend media (text, audio, video) according to user preferences and user context (friends, mood, location, etc).
  • Assess basic metrics for complex networks, and model social relations based on network analysis.
  • Implement software for detecting communities in social networks and analyze the communities using network metrics
  • Quantify relations in social networks to analyze their dynamics, using measures from complex network theory.
Content
The course is based on analyzing data from online social networks (e.g. Twitter, Wikipedia, and Facebook), as well as working with quantitative text analysis. The course is structured around short lectures combined with exercises, as well as a high degree of independent project work.
Remarks
It is recommended to bring your own laptop computer to carry out exercises in the course.
Last updated
02. maj, 2024