Current Projects
Instaclone - A Lifelike Social Media Educational Tool to Increase Data and Algorithmic Literacy of Adolescents
While Social Media offers great opportunities for information gathering and networking on a global scale, there are increasing reports suggesting that social media also has alarming effects on health and well-being. To counter these trends, a web-based environment is being developed that mimics the functionality and appearance of Instagram, but is fully controllable and therefore can be used for educational and scientific purposes. The tool will be embedded in the learning and teaching research and will be compatible with computer science teaching at the secondary level. InstaClone will be freely available to teachers and can be used by anyone without further IT experience.
Social Media Networks, adolescent sleep and mental health: New evidence to optimize health and wellbeing
Healthy sleep in adolescence is fundamental for building resilience and mental health. Despite this, fewer than 15% of adolescents are achieving the recommended 8-10 hours of sleep per night. Prolonged social media activity close to bedtime has been associated with poor quality, reduced and disrupted sleep in adolescence, and with increases in mental ill-health such as depression and anxiety. The mechanisms underlying this relationship are unclear. The present project seeks to bring together leading researchers in the fields of social networks, sleep and mental health at the new School of Social Science and Technology at TUM and the Faculty of Humanities and Social Sciences at The University of Queensland (UQ), in order to identify novel research goals aimed at better understanding the drivers of poor mental health and sleep problems in adolescents. This partnership is based on our common interest in both understanding and learning how to optimize online (and offline) human relationships for better health. The proposed research partnership will bring together expertise in complex social network analysis, psycho-physiological measurement of sleep and emotions, and social media and content analysis in order to better understand social media use behaviors in adolescents, and associated health outcomes. By conducting this research we will be able to offer a better understanding of the needs and functions of social media use behaviors that can, in turn, provide new evidence for developing better sleep health programs. Details.
Understanding, Detecting, and Mitigating Online Misogyny against Politically Active Women
Billions of people use social media every day. Many of them discuss political topics online. Radicalization, extreme speech, and in particular online misogyny against politically active women have become alarming negative features of online discussions. In this interdisciplinary project, we will employ mixed-methods approaches to three case studies in Germany, India, and Brazil to better understand the content and dynamics of online misogyny against politically active women and to develop methods for early identification of such emerging dynamics. We will collaborate with subject matter experts in India and Brazil as well as with media partners and affected female politicians. With citizen social science tools we will involve the general public in the process of identifying emerging campaigns of online misogyny against politically active women. This project will also develop policy briefs and regulatory approaches to address online misogyny.
Details.
Online Firestorms and Resentment Propagation on Social Media: Dynamics, Predictability and Mitigation
Social media serves as a place to gather information, interact, and form opinions. More recently, online firestorms, fake news and hate speech have shaken our beliefs and hopes about the positive power of social media to their very foundations. While negative emotions are in the core of human behavior, algorithms on social media, enhanced by Artificial Intelligence (AI) can produce and reinforce new dynamics. In this project, we will address the mathematical modeling of the formation and dynamics of opinions in large groups of interacting people on social media. Our primary objective is to understand the driving factors of social media group level phenomena that lead to negative dynamics and to offer approaches on how to detect, react to, and possibly mitigate these dynamics early on. The fundamental goal is to reveal the possible relationship between the simple “social forces” acting at individual level, being the “first principles” of social interaction or the game rules, and the potential emergence of a global behavior. The results of our study will provide insights of ethical relevance by discussing responsibility, delegation and control mechanisms in human-AI interacting systems.
Online-Offline Spillovers - Potential Real-World Implications of Online Manipulation
This project analyzes the previously unexplored questions of whether people’s online behavior spills over to their behavior in the offline world and what mediates the respective effects. Employing a two-stage experimental setup, we first use field experiments on social media for online manipulations of our study participants. Second, we study the potential spillovers to our participants’ offline behavior in a laboratory setting. Specifically, we investigate whether attention from others on social media leads to a polarization of people’s political opinions and erodes their commitment to truth. We hypothesize that the treatment group receiving a relatively high levels of attention on social media will show more polarized profiles of political opinions.
Social Media for Large Studies of Behavior
Over 1 billion people use social media every day. When researchers want to utilize these data to study human behavior on global scale, several issues arise. Do people on online platforms represent society? Do people behave online like they do offline? Are our scientific methods appropriate for social media data?
Changing Power Relations in Data Use & Governance
This project confronts the juxtaposition of two new forces in global politics: the growing pervasiveness of digital technologies in politics and our daily lives and the concurrent shift in power relations in global affairs. How and why are governments using and governing (big) data? To what extent are actors developing new paradigms of data governance? What are the implications of political digitization for citizens, the Global South, global governance and the world economy? We explore these questions by examining data governance and use in Brazil, India, China and the US at the national and international levels.
Algorithms for Context-Aided Network Analysis
It is only context information that allows for qualitative grading of modeled network structure and structural analysis. However, current network analysis methods lack a thorough support of considering context data for analysis tasks. As a consequence, such data is often disregarded. To tackle this shortcoming, we develop novel algorithms that effectively deal with data heterogeneity and efficiently process context information for several steps of network analysis - from the creation of networks to their structural analysis as well as their visualization.
Critical Data Studies
Critical Data Studies (CDS) explore the cultural, ethical and socio-technical challenges at the interface of computer science, social science and society. Together with students we focus on issues of big data, data science, data ethics, privacy, fake news, and elaborate how data systems and algorithms can help solve societal problems while at the same time conforming to principles of responsible research and innovation.
Networks of Political Prosecution
In this project, we study the extent to which the decisions and processes of the Austrian judiciary were influenced by the Austrofascist regime between 1933 and 1938. Using historical network analysis and based on court proceedings from this era, we draw novel inferences regarding how charges, the political convictions and agency of diverse actors (e.g., judges, lawyers, police), as well as broader social cohesion and political alignment of the accused evolved under the fascist regime.
Ontology Based Interoperability for Big Social Data
Everyday we are confronted with a growing amount of distributed and heterogeneous social data. This large data can be an important source of information when a meaningful data management allowing for precise data analysis is applied. Ontologies and semantic web technologies provide a pertinent means for expressing knowledge and information at semantic level. These approaches are of paramount importance to Computational Social Science.
Network Visualization & Perception
Visualizing social structure has been a key feature of social network analysis since its earliest days and visualization is supposed to be - together with – measurement – a key factor responsible for the rapid development of the field of social network analysis. When seeing network visualization as a communicative effort, successful visualization of network figures becomes a challenge that includes both graphical encoding of information and perceptual decoding by the consumer of the visualization. We know very little about how people perceive our visualizations and about the cognitive processes related to consuming and understanding network visualizations. In this project, the receiver of our communicative efforts is studied.