Julian Kohne

Julian Kohne

PhD Student

GESIS

Ulm University

What I do

At GESIS, I’m part of the Designed Digital Data team, focusing on providing an easy to use, transparent and secure infrastructure for collecting mobile survey and smartphone usage data for social science research.

As a PhD student at Ulm University, I am part of the molecular psychology lab and investigate how interpersonal relationships can be quantified using chat logs, specifically donated WhatsApp chat logs. I am developing interactive methods for transparent data donation methods, and investigate how social relationships are expressed through different communication patterns.

Download my Curriculum Vitae.

Interests
  • Computational Social Science
  • Interpersonal Relationships
  • Data Science
  • Text as Data
  • Group Dynamics
  • Social Networks
Education

  • PhD in Psychology, in progress

    Ulm University

  • M.Sc in Social & Organizational Psychology, 2016

    University of Groningen

  • B.Sc in Psychology, 2014

    University of Groningen

My Research

and other projects

A Practical Guide to WhatsApp Data in Social Science Research
In this chapter, we give a practical overview about working with data from WhatsApp, specifically, donated WhatsApp chatlogs, to answer social science research questions.
Julian Kohne, Jon Elhai, Christian Montag
PDF Cite Code DOI
A Practical Guide to WhatsApp Data in Social Science Research
Call me sexist, but...: Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples.
In this paper, we outline the different dimensions of sexism by grounding them in their implementation in psychological scales. From the scales, we derive a codebook for sexism in social media which we use to annotate existing and novel datasets. Next, we leverage the annotated datasets to generate adversarial examples, and test the reliability of sexism detection methods.
Mattia Samory, Indira Sen, Julian Kohne, Fabian Flöck, Claudia Wagner
PDF Cite Code Dataset DOI
Call me sexist, but...: Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples.
The Role of Network Structure and Initial Group Norm Distributions in Norm Conflict
In this chapter, we present an agent-based model that simulates the adoption of norms in two interacting groups. We explore this phenomenon while varying relative group sizes and homophily/heterophily (two features of network structure), and initial group norm distributions.
Julian Kohne, Nathalie Gallagher, Zeynep Melis Kirgis, Rocco Paolillo, Lars Padmos, Fariba Karimi
PDF Cite Code DOI
The Role of Network Structure and Initial Group Norm Distributions in Norm Conflict

Skills

Social Psychology

Expert

R

Expert

Statistics

Advanced

Shiny

Advanced

Machine Learning

Enthusiast

Git

Enthusiast

Experience

and previous positions

 
 
 
 
 
PhD Student
Ulm University
Sep 2020 – Present Cologne
In my PhD project, I am investigating how interpersonal relationships can be quantified using chat logs, specifically donated WhatsApp chat logs. I am developing interactive methods for transparent, ethical and secure data donation, and investigate how social relationships are expressed through different communication patterns.
 
 
 
 
 
Scientific Advisor for Digial Behavioral Data
GESIS
Oct 2017 – Present Cologne
Coordination of our institute wide efforts to expand the GESIS service portfolio to digital behavioral data, conceptualization of new services and acquisition of third-party funding. The position includes a component for research in data and web science.
 
 
 
 
 
Computational Social Science Internship
GESIS
Apr 2017 – Jul 2017 Cologne
Analysis of text and metadata on token-level on a dataset of 1.3 million Wikipedia revisions. The goal was to quantify the learning curves of editors.
 
 
 
 
 
Scientific Traineeship
Enexis Netbeheer / University of Groningen
Oct 2015 – Oct 2016 Groningen
Evaluation of the „Buurkracht“ project by Enexis, a quantitative long-term study of a bottom-up energy conservation initiative.
 
 
 
 
 
Research Assistant
University of Groningen
Oct 2014 – Jun 2015 Groningen
Questionnaire design and maintenance in Qualtrics, statistical analyses in R and SPSS

Certificates

and professional development

GDPR - Basics for dealing with research data
GESIS Cologne December 1, 2021 – December 1, 2021

Workshop Contents:

  • Overview of GPDR Guidelines
  • Informed Consent
  • Priviliges of Research Projects
  • Personal, anonymous and pseudonomous data
See certificate
Project Manager (IHK)
IHK Cologne May 26, 2021 – June 30, 2021

Workshop Contents:

  • Project management according to DIN 69901
  • Types of project organization
  • Cost and resource management
  • Working with specification sheets
  • Time management
See certificate
Big Data - Introduction to Data Science with Python
GESIS September 16, 2019 – September 18, 2019

Workshop Contents:

  • The Python Data Science Stack
  • Data Exploration and Preprocessing
  • Web Data Acquisition
  • Data visualization
  • Machine Learning
LaTeX
GESIS November 22, 2018 – November 23, 2018

Workshop Contents:

  • Scientific Papers in LaTeX
  • Bibliographies
  • Tables
  • Equations
  • LaTeX with R and Stata
  • LaTeX for posters and presentations
Practical Introduction to Text Mining
GESIS April 18, 2018 – April 19, 2018

Workshop Contents:

  • Computer-assisted text coding
  • Metadata evaluation
  • Linguistic preprocessing
  • Lexicometrics
  • Topic Models
  • Methodological Integration
An Introduction to R Markdown
GESIS April 15, 2018 – April 15, 2018

Workshop Contents:

  • Introduction to R Markdown
  • Writing scienctific papers in R Markdown
  • Citations in R Markdown
The Data Scientists Toolbox
Coursera January 5, 2017 – December 1, 2017
In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
See certificate
Machine Learning
Coursera December 12, 2016 – January 5, 2017
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
See certificate

Contact

  • julian.kohne[at]gesis.org
  • +49 (0221) 47694-222
  • Tweet me