Third-Party-Funded Research Projects

In this project, we analyze the prevalence of hate speech on German Twitter using techniques such as natural language processing and network analysis. We will analyze and quantify the effect of the German Network Enforcement Act (NetzDG) on hate speech on Twitter, as well as consider recent changes, such as the takeover of Twitter by Elon Musk.

Project duration: 2023–2025

Funded by: German Foundation for Peace Research

Academic Partners: ZEW, Erasmus Universit Rotterdam

TEAMING.AI will develop a software platform for human-AI teaming. The main developed technologies are dedicated to the Knowledge Graph Engine, the TEAMING.AI Engine and mechanisms for exploiting the underlying knowledge graph for constraint checking and mining for recommendations, in a dynamic setting. The conceptualisation of human-AI teaming by means of a knowledge graph for modelling teaming interdependencies, skills, tasks and workflows is at the heart of the project, complemented by rule-based techniques for checking consistency and more advanced relational machine learning techniques for on-going evaluation.

Project duration: 2021–2024

Funded by: European Commission

Academic Partners: TU Dublin, WU Vienna, IDEKO, Profactor, Software Competence Center Hagenberg

Within the context of the research program for the public welfare-oriented use of artificial intelligence (AI), the project “Kartellrechtskonforme KI (KarekoKI)” (~competition  law-compliant AI) is being implemented as a collaborative effort between the Chair of Public Law, Regulatory Law and Tax Law as well as the Chair of Data Sciene. With the help of a market simulation, which is supported by AI-controlled agents as well as data gained from online shops, a legal framework and strategies for the technical prevention of AI-based price fixing are to be developed. 

Project duration: 2020- 2023
Overall budget: 383.000 EUR
Funded by: BW Stiftung

Within the context of the research program for responsible artificial intelligence, the goal of the interdisciplinary ReNewRS project is to investigate the effects of news recommender systems on social polarization processes, political participation and prosocial behaviour, in a series of experimental studies. From the resulting empirical insights, guidelines for news recommender systems that enable a more balanced opinion formation will be derived.

Project duration: 2020- 2022
Funded by: BW Stiftung
Partners: Institute for Media and Communication Studies (University of Mannheim), FIZ Karlsruhe, KIT Karlsruhe

Our society faces great economic and social challenges. There is widespread consensus that reforms are necessary to cope with these challenges. Yet, we experience that reform initiatives are frequently delayed, compromised, or fail altogether. The multidisciplinary SFB 884 aims to provide scientific insights into success and failure of political reforms, determined by competing interests (Project Group A), contexts (Project Group B) and the political process of reform-making (Project Group C). As the core infrastructure, a data center will collect new data on these three dimensions. The DWS Group participates in the second phase of the SFB actively contributing to Project C4 “Measuring a common space and the dynamics of reform positions” and Z1 “Political Text Analysis Network”.

Duration: 2014 – 2021
Overall Budget: 10M EUR overall (500.000 EUR for our group)
Funded by: German Science Foundation (DFG)
Partners: School of Social Sciences and Department of Economics

In this JOIN-T project with the Language Technology Group of TU Darmstadt we aim at delivering new kinds of knowledge bases combining ontological information from large-scale knowledge bases (like Freebase, YAGO or DBpedia) with distributional semantic information encoded within large (i.e. web-scale) amounts of text.

Project duration: 2015 – 2018 (Phase 1) 2019 – 2021 (Phase 2)
Overall budget: 1M EUR (budget Mannheim: ~500.000 EUR)
Funded by: Deutsche Forschungsgemeinschaft (DFG)

The goal of the research project is to make information on the Web accessible in a Wikipedia-like form through a query-driven interaction paradigm. This research requires a combination of methods from information retrieval and automatic text understanding to provide the user with a synthesis of the information through summarization, sub-topic identification, and article organization.

Project duration: 2016 – 2019
Budget: 110.000 EUR
Funded by: Elite program for Post-Docs of the BW-Stiftung 

The EXPLAIN project investigates methods to computationally analyze and validate arguments in order to

• complement the overtly expressed argument with automatically acquired knowledge that provides missing explanatory links,
• construct a formal, knowledge-enhanced analysis of the argument, and on this basis
• establish and verify the extended argument structure using a combination of machine learning and formal reasoning. 

The project is part of the DFG-funded priority program  Robust Argumentation Machines (RATIO).

Project duration: 2018–2021

Funded by: German Science Foundation (DFG)

Partners: Chair of Computational Linguistics, Heidelberg University.