Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning

Sandler Simone; Krauss Oliver; Diesenreiter Clara; Stöckl Andreas

doi:10.1109/ICECCME55909.2022.9987851

Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781665470957
ISBN (Print)	978-1-6654-7096-4
DOIs	https://doi.org/10.1109/ICECCME55909.2022.9987851
Publication status	Published - 18 Nov 2022
Event	2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) - Maldives, Maldives Duration: 16 Nov 2022 → 18 Nov 2022

Publication series

Name	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

Conference

Conference	2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
Period	16.11.2022 → 18.11.2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Fake News Detection
Machine Learning
Natural Language Processing
News Quality

Access to Document

10.1109/ICECCME55909.2022.9987851

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9987851

Cite this

Simone, S., Oliver, K., Clara, D., & Andreas, S. (2022). Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning. In International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022 (pp. 1-5). Article 9987851 (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022). IEEE. https://doi.org/10.1109/ICECCME55909.2022.9987851

Simone, Sandler ; Oliver, Krauss ; Clara, Diesenreiter et al. / Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022. IEEE, 2022. pp. 1-5 (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022).

@inproceedings{5da9bca58a394572b773f6b11fd7bb85,

title = "Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning",

abstract = "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.",

keywords = "Mechatronics, Correlation, Computational modeling, Machine learning, Fake news, Fake News Detection, Machine Learning, Natural Language Processing, News Quality",

author = "Sandler Simone and Krauss Oliver and Diesenreiter Clara and St{\"o}ckl Andreas",

note = "Funding Information: FUNDING We thank the Austrian Research Promotion Agency (FFG) for funding this research. This research was funded via the program track General Program, in the project Explainable (Artificial) Creativity in Innovation E(A)CI, project number 42479797 and was done in cooperation with the company mobile agreements GmbH. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) ; Conference date: 16-11-2022 Through 18-11-2022",

year = "2022",

month = nov,

day = "18",

doi = "10.1109/ICECCME55909.2022.9987851",

language = "English",

isbn = "978-1-6654-7096-4",

series = "International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022",

publisher = "IEEE",

pages = "1--5",

booktitle = "International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022",

}

Simone, S , Oliver, K, Clara, D & Andreas, S 2022, Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning. in International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022., 9987851, International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022, IEEE, pp. 1-5, 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 16.11.2022. https://doi.org/10.1109/ICECCME55909.2022.9987851

Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning. / Simone, Sandler ; Oliver, Krauss; Clara, Diesenreiter et al.
International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022. IEEE, 2022. p. 1-5 9987851 (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning

AU - Simone, Sandler

AU - Oliver, Krauss

AU - Clara, Diesenreiter

AU - Andreas, Stöckl

N1 - Funding Information: FUNDING We thank the Austrian Research Promotion Agency (FFG) for funding this research. This research was funded via the program track General Program, in the project Explainable (Artificial) Creativity in Innovation E(A)CI, project number 42479797 and was done in cooperation with the company mobile agreements GmbH. Publisher Copyright: © 2022 IEEE.

PY - 2022/11/18

Y1 - 2022/11/18

N2 - 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.

AB - 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.

KW - Mechatronics

KW - Correlation

KW - Computational modeling

KW - Machine learning

KW - Fake news

KW - Fake News Detection

KW - Machine Learning

KW - Natural Language Processing

KW - News Quality

UR - https://www.scopus.com/pages/publications/85146438102

U2 - 10.1109/ICECCME55909.2022.9987851

DO - 10.1109/ICECCME55909.2022.9987851

M3 - Conference contribution

SN - 978-1-6654-7096-4

T3 - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

SP - 1

EP - 5

BT - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

PB - IEEE

T2 - 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

Y2 - 16 November 2022 through 18 November 2022

ER -

Simone S , Oliver K, Clara D, Andreas S. Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning. In International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022. IEEE. 2022. p. 1-5. 9987851. (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022). doi: 10.1109/ICECCME55909.2022.9987851

Detecting Fake News and Performing Quality Ranking of German Newspapers Using Machine Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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