Text and Media Analytics

Course goals

The course aims to train students in critically applying computational methods to research questions within the broader realms of media studies, culture studies, and communication studies.
Additionally, the methods taught in this course hold practical relevance for professionals within the media and creative sectors.
As a result, the course considers both the academic-scientific and "applied" dimensions.
This inter- and transdisciplinary approach is manifested through the following learning objectives (LOs):

• LO1: Understand digital media data retrieval methods and their associated risks within the domains of media, communications, and culture.
• LO2: Grasp text and media analytics for researching real-world phenomena and for operationalising key concepts related to digital communication.
• LO3: Collaboratively undertake group projects and proficiently report and discuss key findings.
• LO4: Engage in critical reflection regarding the limitations of computational methods and data-driven approaches.
• LO5: Comprehend the practical significance of computational methods for careers in the media and communication sectors.

Assessment
Two assignments, each counting for 50% of the final mark:

• assignment 1, individual: solving 4 challenges in a take-home exam which will be handed out in class
• assignment 2, group: research project (presentation & report)

Elaborate assignment descriptions and assessment rubrics exist for both assignments.

To qualify for a repair of the final result the mark needs to be at least a 4, or “AANV”.

Content

Amid the pervasive digitization of media, culture, and society, an abundance of diverse data becomes available, enabling researchers and students to examine new media practices and their impacts.
This interdisciplinary course blends media and communication studies with data science, equipping students to analyze real-world challenges in the digital media landscape.

Throughout the course, students will explore various data collection and analysis methods, including social media data mining, natural language processing (NLP), and visual media analysis.
Students will gain both methodological expertise and theoretical insights into concepts pertinent to contemporary online communication.
Using data from major online platforms like Instagram, TikTok, Twitter, and YouTube, students will connect their inquiries to critical communication issues, including content toxicity, online activism and misinformation.

Course form
Lectures, seminars.

Additional information

Enrolment in this course is only available by using the pre-enrolment form that will be forwarded to all ADSM students in the second week of September.