Trends in Media Analytics

Master 2026/2027

Type: Elective course (Contemporary Media Research)

Delivered by: Institute of Media

Where: Faculty of Creative Industries

When: 1 year, 3 module

Open to: students of one campus

Language: English

ECTS credits: 3

Contact hours: 32

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Abstract

The course aims to equip students with advanced skills in analyzing media content and its impact across various platforms. It delves into emerging trends in media analytics, including big data analysis, sentiment analysis, and predictive modeling. Students will learn to use analytical tools for measuring audience engagement, tracking media influence, and identifying key performance indicators. The curriculum emphasizes practical application of theoretical frameworks to real-world scenarios, preparing graduates for careers in research, marketing, and strategic communications within the media industry.

Learning Objectives

To acquaint students with the theoretical basics of media analytics
To overview tools for calculating and interpreting key metrics of social media analysis
To analyse cases of using media analytics in different areas
To learn principles for making reports based on collected data

Expected Learning Outcomes

Uses tools for social media analytics for making reports and creating dashboards
Assesses possible implications of data from open sources
Presents cases of using media analytics tools in different spheres
Understands possibilities and limitations of current instruments in media analytics (Big Data, AI etc.)
Interprets data analysis results and concludes how to use them making recommendations.

Course Contents

Foundations of Media Analytics: Key Concepts and Evolution of Approaches
Measuring Audience Engagement: Metrics and Tools
Media Impact Tracking: from Reach to Reputation Effects
Big Data in and AI Media Analytics: Ways of Application
Sentiment Analysis: Overview of Methods and Applications
Predictive Modeling in Media: from Data to Forecasts
Applied Media Analytics: from Data to Decisions
Presentations of Final Projects

Assessment Elements

Participation in seminar activities
Group project
Individual written task
Individual presentation of an article

Interim Assessment

2026/2027 3rd module
0.21 * Individual presentation of an article + 0.4 * Group project + 0.1 * Participation in seminar activities + 0.29 * Individual written task

Bibliography

Recommended Core Bibliography

Social media analytics : effective tools for building, interpreting, and using metrics, Sponder, M., 2012

Recommended Additional Bibliography

Lev Manovich. (2016). The Science of Culture? Social Computing, Digital Humanities and Cultural Analytics. Journal of Cultural Analytics. https://doi.org/10.31235/osf.io/b2y79

Course Syllabus