Fake News Detection

A Novel Challenge for Social Media Retrieval

Background 

Due to events of the last years (for example the 2016 Brexit referendum) the public has become increasingly aware of the influence of “fake news”. The topic is highly complex and the evaluation of fake news is a semantically challenging task. In view of the constantly growing amount of data, the question arises whether fake news can be detected automatically by data analysis. 

Content

Current methods mostly focus on one data modality (i.e. text) additionally there is a limited number of datasets available and not all labelled datasets have the necessary quality. For the automatic identification of questionable/controversial content a Fake News detector based on neural network models from the field of AI as well as methods from Natural Language Processing (NLP) is designed, implemented and evaluated. The goal is a learnable system that adapts to new topics and uses metadata from social media to improve the performance of Fake News Detection 

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Research Assistant
Media Computing Research Group
Institute of Creative\Media/Technologies
Department of Media and Digital Technologies
Location: D - Heinrich Schneidmadl-Straße 15
M: +43/676/847 228 622
Funding
netidee Stipendium
Runtime
10/01/2019 – 05/31/2020
Status
finished
Involved Institutes, Groups and Centers
Institute of Creative\Media/Technologies
Research Group Media Computing