%0 Conference Paper
%1 herde2022concept
%A Herde, Marek
%A Huseljic, Denis
%A Mitrovic, Jelena
%A Granitzer, Michael
%A Sick, Bernhard
%B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD
%D 2022
%K imported itegpub isac-www ActiveLearning MultimodalData SemanticAnnotation PolarizedWebContent HatefulMemes
%P 1--6
%T A Concept for Automated Polarized Web Content Annotation based on Multimodal Active Learning
%U http://ceur-ws.org/Vol-3259/ialatecml_paper1.pdf
%X Active learning (AL) techniques hardly cope with complex annotations tasks, where, 
for example, annotations might express relationships across data modalities. 
As a use case, we consider the task of automatically detecting and reporting multimodal, 
polarized web content (PWC). Samples of this content type emerge dynamically, 
covering a broad spectrum of topics. Thus, training machine learning systems for 
detecting PWC is challenging, particularly if it needs to be done with minimum annotation cost.
In this article, we propose the concept of multimodal AL for complex annotations 
in the context of PWC detection and formulate the resulting challenges as questions for 
future research.