13 citations · 27 across the 7 of their papers we have counts for
7 papers
Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls
José Miguel Moreno, Sergio Pastrana, Jens Helge Reelfs +6
During the first days of the 2022 Russian invasion of Ukraine, Russia's media regulator blocked access to many global social media platforms and news sites, including Twitter, Face…
Characterizing the country-wide adoption and evolution of the Jodel messaging app in Saudi Arabia
Jens Helge Reelfs, Oliver Hohlfeld, Markus Strohmaier +1
Social media is subject to constant growth and evolution, yet little is known about their early phases of adoption. To shed light on this aspect, this paper empirically characteriz…
Anonymous Hyperlocal Communities: What do they talk about?
Jens Helge Reelfs, Oliver Hohlfeld, Niklas Henckell
In this paper, we study what users talk about in a plethora of independent hyperlocal and anonymous online communities in a single country: Saudi Arabia (KSA). We base this perspec…
Differences in Social Media Usage Exist Between Western and Middle-East Countries
Jens Helge Reelfs, Oliver Hohlfeld, Niklas Henckell
In this paper, we empirically analyze two examples of a Western (DE) versus Middle-East (SA) Online Social Messaging App. By focusing on the system interactions over time in compar…
Understanding & Predicting User Lifetime with Machine Learning in an Anonymous Location-Based Social Network
Jens Helge Reelfs, Max Bergmann, Oliver Hohlfeld +1
In this work, we predict the user lifetime within the anonymous and location-based social network Jodel in the Kingdom of Saudi Arabia. Jodel's location-based nature yields to the…
Corona-Warn-App: Tracing the Start of the Official COVID-19 Exposure Notification App for Germany
Jens Helge Reelfs, Oliver Hohlfeld, Ingmar Poese
On June 16, 2020, Germany launched an open-source smartphone contact tracing app ("Corona-Warn-App") to help tracing SARS-CoV-2 (coronavirus) infection chains. It uses a decentrali…
Word-Emoji Embeddings from large scale Messaging Data reflect real-world Semantic Associations of Expressive Icons
Jens Helge Reelfs, Oliver Hohlfeld, Markus Strohmaier +1
We train word-emoji embeddings on large scale messaging data obtained from the Jodel online social network. Our data set contains more than 40 million sentences, of which 11 millio…