Wissenschaftliche*n Mitarbeiter*in (Doktorand*in) Cross-platform Social Media Mining (KTS-76)

GESIS – Leibniz-Institute for the Social Sciences is an internationally active research institute, funded by federal and state governments and member of the Leibniz Association.

Starting on (subtect to project approval) our Department Knowledge Technologies for the Social Sciences (KTS), Team Information Extraction and Linking located in Cologne is looking for a Research Associate (PhD Candidate) Cross-platform Social Media Mining.

(Salary group 13 TV-L, working time 100 %, limited to 36 months)

The department Knowledge Technologies for Social Sciences (KTS) conducts research at the intersection of information retrieval, natural language processing, semantic technologies and human information interaction as foundation for innovative web portals and platforms for the search and use of research data. KTS innovations are based on a state-of-the‑art software stack and research‑based innovations in the aforementioned fields.

WIEGE is a BMFTR‑funded project that aims to detect cross‑platform political disinformation. Its objective is to protect and build resilience among young people and adolescents on social media. The project takes an interdisciplinary, integrated, and long‑term approach, combining the development of a sustainable data infrastructure with automated methods for identifying political disinformation across multiple platforms, along with effective communication strategies for education and awareness‑raising. KTS’ role in the project is the establishment of a sustainable and reusable database for analyzing the cross‑platform dissemination, formats, and actors of disinformation.

Your profile:

• Scientific Master’s degree in Computer Science or related fields
• Research interests in one or more of the following topics: data collection, information extraction using large language models (LLMs), machine learning (ML), natural language processing (NLP)
• Expertise in software development with Python, PySpark, and machine learning frameworks (e.g., PyTorch)
• Desired: expertise in working with social media data, metadata, and web standards
• Ability to communicate fluently in English; basic knowledge of the German language is desirable

Our Benefits:

• A fast growing and international working environment with a lot of creative scientific freedom and access to unique research data
• Excellent career opportunities in research & teaching as well as in software development and data engineering
• Close collaboration and exchange with excellent national and international research labs in AI and data science
• Flexible working hours and regulations for mobile working
• Very good conditions for reconciling work and family life, e.g. subsidies for childcare for children who have not yet attained the age of compulsory schooling
• Holistic company health management and discounted participation in the university's sports programme
• Generous support for your pension provision as a direct insurance policy
• Promotion of your skills through further training measures

Contact

For further information concerning the tasks please contact Dr. Dimitar Dimitrov via phone or via E-Mail ( ). If you have questions about the application process, please contact Franca Tosetti via E-Mail ( ).

Your tasks will be:

• Design and implement data collection pipelines to gather information from various social media platforms
• Conceive and evaluate information extraction pipelines to identify mentions of actors and topics related to disinformation targeting young adults
• Work closely with other project partners on specific work packages within the WIEGE project, contributing to the project's overall objectives and outcomes
• Conduct applied research in areas like information extraction, machine learning, and artificial intelligence, exploring their applications in the context of social media and cross-platform disinformation detection
• Publish research findings and project results in relevant academic outlets

Please apply via our Online-application portal until .

Our reference number is: KTS-76