1.3k citations · 3.2k across the 145 of their papers we have counts for
23 papers · 1 filter
Russian propaganda on social media during the 2022 invasion of Ukraine
Dominique Geissler, Dominik Bär, Nicolas Pröllochs +1
The Russian invasion of Ukraine in February 2022 was accompanied by practices of information warfare, yet existing evidence is largely anecdotal while large-scale empirical evidenc…
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
Zhenrui Yue, Huimin Zeng, Bernhard Kratzwald +2
Question answering (QA) has recently shown impressive results for answering questions from customized domains. Yet, a common challenge is to adapt QA models to an unseen target dom…
The Virality of Hate Speech on Social Media
Abdurahman Maarouf, Nicolas Pröllochs, Stefan Feuerriegel
Online hate speech is responsible for violent attacks such as, e.g., the Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to vulnerable groups and society…
Analyzing Social Media Activities at Bellingcat
Dominik Bär, Fausto Calderon, Michael Lawlor +3
Open-source journalism emerged as a new phenomenon in the media ecosystem, which uses crowdsourcing to fact-check and generate investigative reports for world events using open sou…
Normalizing Flows for Interventional Density Estimation
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Existing machine learning methods for causal inference usually estimate quantities expressed via the mean of potential outcomes (e.g., average treatment effect). However, such quan…
"Is It My Turn?" Assessing Teamwork and Taskwork in Collaborative Immersive Analytics
Michaela Benk, Raphael Weibel, Stefan Feuerriegel +1
Immersive analytics has the potential to promote collaboration in machine learning (ML). This is desired due to the specific characteristics of ML modeling in practice, namely the…
Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model
Tobias Hatt, Stefan Feuerriegel
In order to steer e-commerce users towards making a purchase, marketers rely upon predictions of when users exit without purchasing. Previously, such predictions were based upon hi…
Estimating individual treatment effects under unobserved confounding using binary instruments
Dennis Frauen, Stefan Feuerriegel
Estimating conditional average treatment effects (CATEs) from observational data is relevant in many fields such as personalized medicine. However, in practice, the treatment assig…
Locating disparities in machine learning
Moritz von Zahn, Oliver Hinz, Stefan Feuerriegel
Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematica…
Algorithmic Fairness in Business Analytics: Directions for Research and Practice
Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky
The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising lega…
Mechanisms of True and False Rumor Sharing in Social Media: Collective Intelligence or Herd Behavior?
Nicolas Pröllochs, Stefan Feuerriegel
Social media platforms disseminate extensive volumes of online content, including true and, in particular, false rumors. Previous literature has studied the diffusion of offline ru…
Data-driven dynamic treatment planning for chronic diseases
Christof Naumzik, Stefan Feuerriegel, Anne Molgaard Nielsen
In order to deliver effective care, health management must consider the distinctive trajectories of chronic diseases. These diseases recurrently undergo acute, unstable, and stable…
Contrastive Learning for Unsupervised Domain Adaptation of Time Series
Yilmazcan Ozyurt, Stefan Feuerriegel, Ce Zhang
Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain.…
Finding Qs: Profiling QAnon Supporters on Parler
Dominik Bär, Nicolas Pröllochs, Stefan Feuerriegel
The social media platform "Parler" has emerged into a prominent fringe community where a significant part of the user base are self-reported supporters of QAnon, a far-right conspi…
Causal Transformer for Estimating Counterfactual Outcomes
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Estimating counterfactual outcomes over time from observational data is relevant for many applications (e.g., personalized medicine). Yet, state-of-the-art methods build upon simpl…
Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine
Theresa Blümlein, Joel Persson, Stefan Feuerriegel
Dynamic treatment regimes (DTRs) are used in medicine to tailor sequential treatment decisions to patients by considering patient heterogeneity. Common methods for learning optimal…
Online Emotions During the Storming of the U.S. Capitol: Evidence from the Social Media Network Parler
Johannes Jakubik, Michael Vössing, Dominik Bär +2
The storming of the U.S. Capitol on January 6, 2021 has led to the killing of 5 people and is widely regarded as an attack on democracy. The storming was largely coordinated throug…
Web Mining to Inform Locations of Charging Stations for Electric Vehicles
Philipp Hummler, Christof Naumzik, Stefan Feuerriegel
The availability of charging stations is an important factor for promoting electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for city planners, the crucial…
Interpretable Off-Policy Learning via Hyperbox Search
Daniel Tschernutter, Tobias Hatt, Stefan Feuerriegel
Personalized treatment decisions have become an integral part of modern medicine. Thereby, the aim is to make treatment decisions based on individual patient characteristics. Numer…
Estimating Conditional Average Treatment Effects with Missing Treatment Information
Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel
Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE es…
Estimating average causal effects from patient trajectories
Dennis Frauen, Tobias Hatt, Valentyn Melnychuk +1
In medical practice, treatments are selected based on the expected causal effects on patient outcomes. Here, the gold standard for estimating causal effects are randomized controll…
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
Tobias Hatt, Jeroen Berrevoets, Alicia Curth +2
Estimating heterogeneous treatment effects is an important problem across many domains. In order to accurately estimate such treatment effects, one typically relies on data from ob…
Detecting False Rumors from Retweet Dynamics on Social Media
Christof Naumzik, Stefan Feuerriegel
False rumors are known to have detrimental effects on society. To prevent the spread of false rumors, social media platforms such as Twitter must detect them early. In this work, w…