Nowadays, news spread quickly, and it is not always clear to the reader whether an article is real or fake. Moreover, readers use only a few sources to read the news without knowing the quality of the source. This is due to a lack of up-to-date news or media rankings. Machine learning models can be used to automatically detect fake news. In this work, a Passive-Aggressive-Classifier, a Random-Forest, and an LSTM network are trained to distinguish between fake and non-fake (real) news. Moreover, these models are used to classify news sources according to the amount of possible Fake News they may spread. The models are tested on English and translated German articles. The best results for Fake News detection on English articles is reached with the Passive-Aggressive-Classifier. For automatic news ranking of translated German articles, Random-Forest provides the best result. The correlation of Random-Forest with an actual news ranking reached 0.68. This shows that automated classification can be extended to languages other than English, using this approach. In the future, other machine learning models and translators will be used to extend the approach.
|Name||International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022|
|Konferenz||2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)|
|Zeitraum||16.11.2022 → 18.11.2022|
- Computational modeling
- Machine learning
- Fake news