Challenge

Social media has become a central part of public communication. At the same time, platforms differ significantly in how they represent interactions, reach, and content. While individual platforms establish their own metrics and data models, a common cross-platform reference model is still lacking. This creates major difficulties for scientific analysis of communication dynamics as well as for practical applications, e.g., in the investigation of disinformation or in the field of Open Source Intelligence (OSINT).

Objective

The thesis aims to develop a consistent cross-platform data model in the form of an ontology that harmonizes the key entities of different social media platforms (e.g., users, content, interactions). Building on this, a unified reach metric will be designed to improve comparability between platforms. With this dual approach—semantic structuring and metric harmonization—the thesis contributes to greater transparency and comparability in the analysis of digital publics.

Methodology & Expected Results

The work can be methodologically based on the Design Science Research (DSR) paradigm. Possible research artifacts include:

• a systematic literature research on existing data models and research metrics, elaborating on their research gaps,
• a data model/ontology for social media data,
• a technical mapping between platform APIs and the ontology,
• a prototype for a unified reach metric,
• optionally, a simple visualization demonstrator for evaluation.

An exemplary application using publicly accessible social media data (e.g., Twitter/X, Meta, YouTube) is desirable, if available. The thesis can be held in cooperation with a practice partner (suggestions available).

Impact

The results of this thesis can make an important contribution to digital democracy. By providing a standardized data model and a comparable reach metric, social media data can be analyzed more transparently and across platforms. This opens up new possibilities for the study of disinformation, the dynamics of online participation, and polarization in digital publics. A robust ontology and metric thus support both scientific analysis and practical work in politics, civil society, and media.

Requirements for the Candidate

• Basic knowledge of data modeling or data structures, ideally OSINT
• Programming skills (e.g., Python or JavaScript) for working with APIs and data processing
• Programming skills (e.g., Python or JavaScript) for working with APIs and data processing
• Ability to work independently and in a structured manner