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Table 1 Summary of related works for Ethiopian languages

From: Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches

Author (s) Approaches Contributions of Author (s) Evaluation Metrics
[11] DL and word embedding They have collected and arranged a sizable Amharic corpus for general use.
They’ve developed an Amharic fasttext word embedding system.
They’ve created a brand-new dataset for detecting bogus news in Amharic.
They’ve developed a DL strategy for detecting bogus news in Amharic.
They ran a series of tests to see how well the word embedding and fake news detection models worked.
Using both word embedding, cc-am-300 and AMFTWE, the fake news detection model performed exceptionally well.
When using the 300 and 200-dimension embedding, the model had a validation accuracy of above 99%.
Finally, they included the experimental results of the model performance utilizing the cc-am-300 and AMFTWE embedding, which were accuracy of 99.36%, precision of 99.30%, recall 99.41%, and f1-score of 99.35%.
[27] ML (i.e., SVM, MNB, LSVM, LR, DT, and RF) They built a model that detects Afan Oromo hate speech on social media using a combination of n-gram and TF-IDF feature extraction methodologies.
They collected 13,600 comments and posts on respective public pages using Facepager (, of which 7000 and 6600 data were acquired from Twitter and Facebook, respectively, between September 2019 and 2020.
They analyzed its performance and discovered that the LSVM classifier has the highest precision, recall, and f1-score values of 66%, 66%, and 64%, respectively.
[25] NLP and PA They’ve created a news corpus called Afan Oromo.
The general architecture of Afan Oromo fake news detection based on text content is provided.
The article addresses the fundamental obstacles in building text content-based false news detection approaches, as well as potential solutions.
The study compares supervised ML methodologies by taking linguistic features and feature extraction methods into account.
The research sets the door for the development of bogus news identification in Afan Oromo, which would boost user confidence.
Despite the dataset’s shortcomings, the Linear PA with TF-IDF vector and unigram model outperforms the competition with 97.2% of precision, 97.9% of recall, and 97.5% of ROC AUC f1-score.
[35] DL (including the Bi-GRU and CNN, and attention-based models) They collected and tagged a dataset of 12,000 news stories to create an automated method for detecting false news. With an accuracy of 93.92%, a precision of 93%, a recall of 95% (which is smaller than Bi-96 LSTM’s %), and an f1-score of 94%, the CNN model outperforms all other models.
The impact of morphological normalization on Amharic fake news identification was investigated using the top two performing models, and the results demonstrated that normalization harms classification performance, lowering both models’ f1-score from 94–92%.
[29] DL (including RNN, Bi-LSTM) They implemented DL models and classified them into pre-defined fine-grained categories to resolve social media fake news for the Afan Oromo language. On a benchmark dataset, the model can predict with an accuracy of 90%, precision of 90%, recall of 89%, and an f1-score of 89%, outperforming the current state of the art utilizing the Bi-LSTM model.
[19] MNB classification approach To best exhibit unambiguous distinctions, the researchers gathered News datasets and accurately categorized them as real and fake news on similar topics. They used TF, TF-IDF, and TF-IDF of unigram and bi-grams, and discovered that TF of unigram of this model identifies fake news sources with a 96% accuracy, with only minor effects on recall.
For real news accuracy, recall, and f1-score, the confusion matrix was computed at 98.6%, 94%, and 96.2%, respectively, and for fake news precision, recall, and f1-score, at 91%, 97.8%, and 94%, respectively.
[26] ML classifiers (including, NB, SVM, LR, SGD, RF, and PA Classifier model) The research has made a substantial contribution to slowing the spread of misinformation in vernacular languages. The experimental results show a precision of 100% RF for both TF-IDF and Count Vectorizer, a recall of 95% using PA classifier for TF-IDF, and an f1-score of 100% in NB and LR classifier for TF-IDF vectorizer using PA classifier.
[30] ML (including SVM, NB, and RF) and text mining feature extraction techniques They gathered posts and comments from Facebook using Face pager’s content retrieval techniques to create the dataset for this investigation. The experiment produced 21 binary and ternary models for each dataset utilizing two datasets.
Both SVM and NB were outperformed by binary models that used RF with word2vec.
SVM with word2vec, on the other hand, outperforms NB and RF models in classification with a 73% of f1-score, a precision of 76%, and a recall of 75%.
In addition, the ternary SVM model using word2vec produced a 53% of f1-score, which is better than the NB and RF models.
Finally, in both datasets utilized in this study, models based on SVM employing word2vec performed marginally better than NB and RF models.
[31] RNN (by using LSTM and GRU with word n-grams for feature extraction and word2vec to represent each unique word by vector representation) Researchers created a tagged massive Amharic dataset by gathering posts and comments from activists who actively participated on Facebook sites. The RNN-LSTM model produced an improved test of 97.9% for all matrices when used with this dataset and different parameters on GRU and LSTM-based RNN models by feature representation of word2vec.
[36] Spark ML Thousands of Amharic posts and comments on suspected social network pages of organizations and individual people’s public pages are crawled as a dataset to execute the various experiments. The NB approach with the word2Vec feature model outperformed the Facebook social network for Amharic language posts and comments in terms of accuracy, ROC score, and area under precision and recall, with 79.83%, 83.05%, and 85.34% accuracy, ROC score, and area under Precision and Recall, respectively.
For the TF-IDF feature model, the NB achieves better results with 73.02%, 80.53%, and 79.93% for accuracy, ROC score, and area under precision and recall, respectively.
The RF with word2vec feature outperforms the TF-IDF with accuracy, ROC score, and area under precision and recall of 65.34%, 70.97%, and 73.07%, respectively.
TF-IDF is next, with 63.55%, 68.44%, and 69.96%, respectively.
[33] Classical GBT, RF, DL, RNN-LSTM, RNN-GRU, and word embedding (Word2Vec) model The suggested method looks into how hate speech detection might be applied to identifying susceptible communities.
Using the example of Amharic text data on Facebook, they were able to identify a potentially vulnerable community in terms of social media hatred.
They gathered and annotated Amharic data to detect hate speech in multicultural Ethiopian society.
Since social media data is very noisy and huge, they used the Apache Spark distributed platform for data pre-processing and feature extraction.
Word2Vec embedding with RNN-GRU had the best performance in the hate speech detection experiments, with an AUC of 97.85%, an accuracy/precision of 92.56%, recall of 97.85%, and an f1-score of 98.42%
[34] ML (including SVM with TF-IDF, N-gram, and W2vec feature extraction) Create a tagged hate speech dataset from social media for the Afaan Oromo language.
They create standard Afan Oromo stop word lists, as well as a brief word expansion dictionary.
For Afan Oromo text hate speeches, they create an SVM model.
They put their new model to the test on hate speech identification and came out on top.
Accuracy, f-score, recall, and precision measurements are used to evaluate the experiment.
In all evaluation measures, the framework based on SVM with n-gram combination and TF-IDF achieves 96% (accuracy, f1-score, precision, and recall).