90 citations · 210 across the 15 of their papers we have counts for
6 papers · 1 filter
Global gender differences in Wikipedia readership
Isaac Johnson, Florian Lemmerich, Diego Sáez-Trumper +3
Wikipedia represents the largest and most popular source of encyclopedic knowledge in the world today, aiming to provide equal access to information worldwide. From a global online…
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…
Sudden Attention Shifts on Wikipedia During the COVID-19 Crisis
Manoel Horta Ribeiro, Kristina Gligorić, Maxime Peyrard +3
We study how the COVID-19 pandemic, alongside the severe mobility restrictions that ensued, has impacted information access on Wikipedia, the world's largest online encyclopedia. A…
Joint Multiclass Debiasing of Word Embeddings
Radomir Popović, Florian Lemmerich, Markus Strohmaier
Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction. Current approaches show promising progress towards debiasing single bias dimens…
The Effects of Gender Signals and Performance in Online Product Reviews
Sandipan Sikdar, Rachneet Singh Sachdeva, Johannes Wachs +2
This work quantifies the effects of signaling and performing gender on the success of reviews written on the popular amazon shopping platform. Highly rated reviews play an importan…
The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings
Binny Mathew, Sandipan Sikdar, Florian Lemmerich +1
We introduce POLAR - a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials. Semantic differentials are a psychometric con…