Knowledge and Social Context-enhanced Fake News Detection

August 22nd, 2023

In today’s digital era, information disseminates rapidly through online platforms, such as Twitter. Those platforms have fundamentally transformed how societies communicate, share, and perceive information. On the other hand, it also presents challenges, notably in the spread of false information or so-called fake news. The spread of fake news has been shown to sway public opinion, disrupt electoral processes, and even endanger public safety.

Thesis Type
  • Bachelor
Presentation room
Seminar room I5 6202
Stefan Decker
Yongli Mou
Fateme Fathi

While several algorithms and systems have been proposed for fake news detection, most of them primarily focus on the textual content or the source of the information. There is a dire need to develop systems that can tap into the vast knowledge bases available and utilize the social context in which the news is shared to enhance detection accuracy. Incorporating knowledge and social context can provide a holistic understanding of the information, making the detection process more robust and less prone to errors.

The goals of this thesis are listed as followings:

  1. Literature Review: Comprehensive exploration of existing fake news detection approaches.
  2. Framework Development: Propose a comprehensive framework that integrates knowledge databases and social context into the fake news detection process.

If you are interested in this thesis, do not hesitate to contact us via


Knowledge in Knowledge Graphs and Deep Learning (Large Language Models and Graph Neural Networks)
Programming language – Python
Deep Learning Framework – PyTorch, PyGeometric