Search Results for author:
Found 27 papers, 14 papers with code
Interpreting Emoji with Emoji
We study the extent to which emoji can be used to add interpretability to embeddings of text and emoji.
FANG-COVID: A New Large-Scale Benchmark Dataset for Fake News Detection in German
As the world continues to fight the COVID-19 pandemic, it is simultaneously fighting an ‘infodemic’ – a flood of disinformation and spread of conspiracy theories leading to health threats and the division of society.
Recommendation Fairness in Social Networks Over Time
We further study how interventions on network properties influence fairness by examining counterfactual scenarios with alternative evolution outcomes and differing network properties.
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest.
Toxic comments reduce the activity of volunteer editors on Wikipedia
We find that toxic comments consistently reduce the activity of editors, leading to an estimated loss of 0. 5-2 active days per user in the short term.
SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings
To address this limitation, we present SensePOLAR, an extension of the original POLAR framework that enables word-sense aware interpretability for pre-trained contextual word embeddings.
Properties of Group Fairness Metrics for Rankings
By virtue of their diverse application contexts, we argue that such a comparative analysis is not straightforward.
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.
Minorities in networks and algorithms
In this chapter, we provide an overview of recent advances in data-driven and theory-informed complex models of social networks and their potential in understanding societal inequalities and marginalization.
Structack: Structure-based Adversarial Attacks on Graph Neural Networks
In this work, we study adversarial attacks that are uninformed, where an attacker only has access to the graph structure, but no information about node attributes.
Redescription Model Mining
This paper introduces Redescription Model Mining, a novel approach to identify interpretable patterns across two datasets that share only a subset of attributes and have no common instances.
A Comparative Evaluation of Quantification Methods
More generally, we find that the performance on multiclass quantification is inferior to the results obtained in the binary setting.
Volunteer contributions to Wikipedia increased during COVID-19 mobility restrictions
Wikipedia, the largest encyclopedia ever created, is a global initiative driven by volunteer contributions.
Computers and Society
The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairness
The FairCeptron framework is an approach for studying perceptions of fairness in algorithmic decision making such as in ranking or classification.
Quota-based debiasing can decrease representation of already underrepresented groups
The most common approach to this issue is debiasing, for example via the introduction of quotas that ensure proportional representation of groups with respect to a certain, often binary attribute.
How Gamification Affects Software Developers: Cautionary Evidence from a Natural Experiment on GitHub
We examine how the behavior of software developers changes in response to removing gamification elements from GitHub, an online platform for collaborative programming and software development.
Software Engineering Computers and Society Social and Information Networks
The Effects of Randomness on the Stability of Node Embeddings
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i. e., the random variation of their outcomes given identical algorithms and graphs.
Word-Emoji Embeddings from large scale Messaging Data reflect real-world Semantic Associations of Expressive Icons
We train word-emoji embeddings on large scale messaging data obtained from the Jodel online social network.
Joint Multiclass Debiasing of Word Embeddings
Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction.
The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings
We introduce POLAR - a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials.
Privacy Attacks on Network Embeddings
In that setting, we analyze whether after the removal of the node from the network and the deletion of the vector representation of the respective node in the embedding significant information about the link structure of the removed node is still encoded in the embedding vectors of the remaining nodes.
iLCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data
The iLCM project pursues the development of an integrated research environment for the analysis of structured and unstructured data in a "Software as a Service" architecture (SaaS).
How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs
In particular, we were able to identify seven distinct browsing-behavior types, which all make use of different functionality provided by BioPortal.
A System for Probabilistic Linking of Thesauri and Classification Systems
This paper presents a system which creates and visualizes probabilistic semantic links between concepts in a thesaurus and classes in a classification system.
Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data
By applying this combination of approaches to taxi data in Manhattan, we can discover and explain different patterns in human mobility that cannot be identified in a collective analysis.
The Polylingual Labeled Topic Model
In this paper, we present the Polylingual Labeled Topic Model, a model which combines the characteristics of the existing Polylingual Topic Model and Labeled LDA.
Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains
For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50, 000 classes representing a vast variety of different diseases and causes of death.
