Next Article in Journal
Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles
Next Article in Special Issue
Narrative Construction of Product Reviews Reveals the Level of Post-Decisional Cognitive Dissonance
Previous Article in Journal
Generation of an EDS Key Based on a Graphic Image of a Subject’s Face Using the RC4 Algorithm
Previous Article in Special Issue
Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media
Article

Combating Fake News in “Low-Resource” Languages: Amharic Fake News Detection Accompanied by Resource Crafting

1
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
2
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Information 2021, 12(1), 20; https://doi.org/10.3390/info12010020
Received: 15 December 2020 / Revised: 31 December 2020 / Accepted: 4 January 2021 / Published: 7 January 2021
(This article belongs to the Special Issue Natural Language Processing for Social Media)
The need to fight the progressive negative impact of fake news is escalating, which is evident in the strive to do research and develop tools that could do this job. However, a lack of adequate datasets and good word embeddings have posed challenges to make detection methods sufficiently accurate. These resources are even totally missing for “low-resource” African languages, such as Amharic. Alleviating these critical problems should not be left for tomorrow. Deep learning methods and word embeddings contributed a lot in devising automatic fake news detection mechanisms. Several contributions are presented, including an Amharic fake news detection model, a general-purpose Amharic corpus (GPAC), a novel Amharic fake news detection dataset (ETH_FAKE), and Amharic fasttext word embedding (AMFTWE). Our Amharic fake news detection model, evaluated with the ETH_FAKE dataset and using the AMFTWE, performed very well. View Full-Text
Keywords: fake news; deep learning; Amharic corpus; dataset; word embedding fake news; deep learning; Amharic corpus; dataset; word embedding
Show Figures

Figure 1

MDPI and ACS Style

Gereme, F.; Zhu, W.; Ayall, T.; Alemu, D. Combating Fake News in “Low-Resource” Languages: Amharic Fake News Detection Accompanied by Resource Crafting. Information 2021, 12, 20. https://doi.org/10.3390/info12010020

AMA Style

Gereme F, Zhu W, Ayall T, Alemu D. Combating Fake News in “Low-Resource” Languages: Amharic Fake News Detection Accompanied by Resource Crafting. Information. 2021; 12(1):20. https://doi.org/10.3390/info12010020

Chicago/Turabian Style

Gereme, Fantahun, William Zhu, Tewodros Ayall, and Dagmawi Alemu. 2021. "Combating Fake News in “Low-Resource” Languages: Amharic Fake News Detection Accompanied by Resource Crafting" Information 12, no. 1: 20. https://doi.org/10.3390/info12010020

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop