11 citations · 28 across the 10 of their papers we have counts for
6 papers · 1 filter
Beyond the Explicit: A Bilingual Dataset for Dehumanization Detection in Social Media
Dennis Assenmacher, Paloma Piot, Katarina Laken +2
Digital dehumanization, although a critical issue, remains largely overlooked within the field of computational linguistics and Natural Language Processing. The prevailing approach…
A Modular Taxonomy for Hate Speech Definitions and Its Impact on Zero-Shot LLM Classification Performance
Matteo Melis, Gabriella Lapesa, Dennis Assenmacher
Detecting harmful content is a crucial task in the landscape of NLP applications for Social Good, with hate speech being one of its most dangerous forms. But what do we mean by hat…
Personas with Attitudes: Controlling LLMs for Diverse Data Annotation
Leon Fröhling, Gianluca Demartini, Dennis Assenmacher
We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs). We investigate the impact of injecting diver…
Sexism Detection on a Data Diet
Rabiraj Bandyopadhyay, Dennis Assenmacher, Jose M. Alonso Moral +1
There is an increase in the proliferation of online hate commensurate with the rise in the usage of social media. In response, there is also a significant advancement in the creati…
The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets
Zehui Yu, Indira Sen, Dennis Assenmacher +5
Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the…
People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection
Indira Sen, Dennis Assenmacher, Mattia Samory +3
NLP models are used in a variety of critical social computing tasks, such as detecting sexist, racist, or otherwise hateful content. Therefore, it is imperative that these models a…