Niklas Stoehr

Public intellectuals claim that populists thrive on fear of minority groups, but we suggest that what they thrive on is anger against governmental actors. It is governmental actors who increasingly rely on fear, be it to pursue policy objectives or to keep populists at bay. The Brexit referendum, election of Donald Trump, and COVID-19 are cases in point. In a battle for the hearts and minds of the people, governmental and populist actors send fear and anger signals, respectively. We theorize this dance of fear and anger as the fear-anger cycle.

The fear-anger cycle: Governmental and populist politics of emotion

Joerg Friedrichs 5
Niklas Stoehr 11
Giuliano Formisano 5

work in progress

↗ Project Webpage
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↗ GDELT Blog Post
↗ Research Assistant Job Ad

In common law, the outcome of a new case is determined mostly by precedent cases, rather than by existing statutes. However, how exactly does the precedent influence the outcome of a new case? Answering this question is crucial for guaranteeing fair and consistent judicial decision-making. We are the first to approach this question computationally by comparing two longstanding jurisprudential views; Halsbury's, who believes that the arguments of the precedent are the main determinant of the outcome, and Goodhart's, who believes that what matters most is the precedent's facts. We base our study on the corpus of legal cases from the European Court of Human Rights (ECtHR).

What About the Precedent: An Information-Theoretic Analysis of Common Law

Josef Valvoda 12
Tiago Pimentel 12
Niklas Stoehr 11
Ryan Cotterell 11,12
Simone Teufel 12

NAACL-HLT 2021

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Classical graph modeling approaches such as Erdos-Renyi (ER) random graphs or Barabasi-Albert (BA) graphs, here referred to as stylized models, aim to reproduce properties of real-world graphs in an interpretable way. While useful, graph generation with stylized models requires domain knowledge and iterative trial and error simulation.

Recovering Barabasi-Albert Parameters of Graphs through Disentanglement

Cristina Guzman 11
Daphna Keidar 11
Andreas Opedal 11
Tristan Meynier 11
Niklas Stoehr 11

ICLR 2021 Geometrical and Topological Representation Learning

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The CoRisk-Index extends the spectre of existing, but less frequently updated economic indicators by tapping into an unexploited and highly abundant data source. The new indicator can be considered business-internally since corporations provide information themselves and data fidelity is self-incentivized due to law enforcement.

The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time

Fabian Stephany 5
Niklas Stoehr 9
Philipp Darius 10
Leonie Neuhäuser 10
Ole Teutloff 10
Fabian Braesemann 5

CEPR Covid Economics

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↗ OII Blog Post
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↗ Wirtschaftswoche
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This work approaches graph generation (decoding) as the inverse of graph compression (encoding). We show that in a disentanglement-focused deep autoencoding framework, specifically Beta-Variational Autoencoders (Beta-VAE), choices of generative procedures and their parameters arise naturally in the latent space.

Disentangling Interpretable Generative Parameters of Random and Real-World Graphs

Niklas Stoehr 3
Emine Yilmaz 3
Marc Brockschmidt 6
Jan Stuehmer 6

Workshop on Graph Representation Learning, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver

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The proximity between newspapers and political parties is strongly subjective and difficult to measure. Yet, political tendencies of newspapers can have a significant impact on voters’ opinion‐forming and ought to be known by the public in a transparent and timely manner. This article introduces the Sentiment Political Compass (SPC), a data‐driven framework for analyzing political bias of newspapers toward political parties.

Measuring Proximity Between Newspapers and Political Parties: The Sentiment Political Compass

Fabian Falck 4
Julian Marstaller 7
Niklas Stoehr 5
Sören Maucher 7
Jeana Ren 7
Andreas Thalhammer 7
Achim Rettinger 7
Rudi Studer 7

Policy & Internet, Wiley Online Library, 2019

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↗ Speakerpolitics
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The digital transformation is driving revolutionary innovations and new market entrants threaten established sectors of the economy such as the automotive industry. Following the need for monitoring shifting industries, we present a network-centered analysis of car manufacturer web pages. Solely exploiting publicly-available information, we construct large networks from web pages and hyperlinks. We tag web pages concerned with topics like e-mobility and environment or autonomous driving, and investigate their relevance in the network.

Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends

Niklas Stoehr 9
Fabian Braesemann 5
Michael Frommelt 9
Shi Zhou 3

CompleNet 2020, published in Springer Nature

↗ arXiv Paper
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↗ IBM Blog Post
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To understand the dynamics of the digital knowledge economy, it is crucial to reveal the geography of global flows of knowledge on digital platforms. This article visualizes a key form of knowledge production in the digital economy: mapping the joint collaborations of users from different cities on Stack Overflow, the world’s most popular question-and-answer website for programming questions.

Global networks in collaborative programming

Fabian Braesemann 5
Niklas Stoehr 3
Mark Graham 5

Regional Studies, Regional Science, Volume 6, 2019

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We present a novel middleware technique called Heatflip, which issues diametrically opposed samples into the temporal and spatial dimensions of the data stored in an external database. Spatial samples provide insights into the temporal distribution and vice versa.

Heatflip: Temporal-Spatial Sampling for Progressive Heat Maps on Social Media Data

Niklas Stoehr 1
Johannes Meyer 1
Volker Markl 1
Qiushi Bai 2
Taewoo Kim 2
De-Yu Chen 2
Chen Li 2

2018 IEEE International Conference on Big Data (Big Data)

↗ Open Paper
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In this work, we thoroughly analyse the ability of text classification models to adapt to transfer learning tasks, whether they are specifically designed for it or not. We directly compare the Transformer Model with an attention-based bi-directional LSTM and naive Logistic Regression as a baseline.

From Accuracy to Versatility: Analysing Text Classification Models regarding Transfer Learning

Niklas Stoehr 3
Luis Sanchez 3
Daniil Gannota 3
Fabian Falck 8

2019 IEEE Social Network Management and Security (SNAMS), Second Workshop on Deep and Transfer Learning

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1 TU Berlin 2 University of California, Irvine 3 University College London 4 Imperial College London 5 University of Oxford 6 Microsoft Research 7 KIT 8 Carnegie Mellon University 9 IBM 10 Hertie School of Governance 11 ETH Zurich 12 University of Cambridge