Demilie and Salau Journal of Big Data (2022) 9:66

https://doi.org/10.1186/s40537-022-00619-x

SURVEY Open Access

Detection of fake news and hate speech

for Ethiopian languages: a systematic review
of the approaches
Wubetu Barud Demilie1*   and Ayodeji Olalekan Salau2   

*Correspondence:
wubetubarud@gmail.com; Abstract
wubetuB@wcu.edu.et With the proliferation of social media platforms that provide anonymity, easy access,
1
Department of Information online community development, and online debate, detecting and tracking hate
Technology, Wachemo speech has become a major concern for society, individuals, policymakers, and
University, Hossana, Ethiopia
Full list of author information researchers. Combating hate speech and fake news are the most pressing societal
is available at the end of the issues. It is difficult to expose false claims before they cause significant harm. Automatic
article fact or claim verification has recently piqued the interest of various research communi-
ties. Despite efforts to use automatic approaches for detection and monitoring, their
results are still unsatisfactory, and that requires more research work in the area. Fake
news and hate speech messages are any messages on social media platforms that
spread negativity in society about sex, caste, religion, politics, race, disability, sexual
orientation, and so on. Thus, the type of massage is extremely difficult to detect and
combat. This work aims to analyze the optimal approaches for this kind of problem,
as well as the relationship between the approaches, dataset type, size, and accuracy.
Finally, based on the analysis results of the implemented approaches, deep learning
(DL) approaches have been recommended for other Ethiopian languages to increase
the performance of all evaluation metrics from different social media platforms. Addi-
tionally, as the review results indicate, the combination of DL and machine learning
(ML) approaches with a balanced dataset can improve the detection and combating
performance of the system.
Keywords: Artificial intelligence, Ethiopian languages, Deep learning, Fake news, Hate
speech, Machine learning, Social media platform

Introduction
In recent years, artificial intelligence (AI) has brought about significant changes in the
domain of information technology and other disciplines, such as the use and develop-
ment of intelligent transportation systems, virtual personal assistants, robotic surgery,
and most significantly, natural language processing (NLP) applications [1]. Accordingly,
the world is rapidly changing in technological aspects. The digital world provides sev-
eral benefits and drawbacks. One of its drawbacks is fake news and hate speech, which
is incredibly simple to spread. Fake news and hate speeches are defined as intention-
ally and verifiably false news [2–4]. Individuals, governments, freedom of speech, news

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Demilie and Salau Journal of Big Data (2022) 9:66 Page 2 of 17

systems, and society are all becoming increasingly vulnerable to it. The rising use of
social media and knowledge sharing has benefited humanity considerably. Today, social
media platforms have a significant impact on people’s daily lives [5]. Such social media
platforms like Facebook and Twitter have aided in the spread of rumors, conspiracy the-
ories, hatred, xenophobia, racism, and prejudice [6].
While technology has many advantages, it can also influence public opinion and reli-
gious views all over the world. It can be used both directly and indirectly to target people
based on race, caste, ethnic origin, religion, ethnicity, nationality, sex, gender identity,
sexual orientation, handicap, or sickness.
The social media sphere, which has long been tightly controlled by the Ethiopian gov-
ernment, appears to be untying itself following the start of the new political reforms in
2018 [7]. Following the transition of the government, it is clear that people are enjoying
greater freedom of expression. On the contrary, the emergence of hate speech attributed
to political, ethnic, and religious underpinnings is said to have subdued the new digital
platform.
Ethiopia’s social media landscape was changing faster than you could refresh your page
in 2021 [8]. It was rife with controversies, disinformation, and organized social media
campaigns, owing primarily to major political events that occurred during that period.
The World Health Organization (WHO) warned against fake news and hate speech
in the COVID-19 infodemic [9, 10] to indicate the proliferation and negative impact of
fake news and hate speech during the current pandemic and that they are a huge threat
to democracy and political stability. Along with the COVID-19 pandemic has emerged
an infodemic of false and misleading information, complicating COVID-19 response
efforts. It was stated that as the virus spreads, misinformation makes the job of the brave
health workers even more difficult; it diverts the attention of the decision-makers, causes
confusion, and spreads fear among the general public; and the list of practical examples
of the effects of fake news and hate speech is growing, and the danger is already immi-
nent [11, 12].
Governments, the technology industry, and individual researchers have all tried to
come up with ways to mitigate the negative impacts of fake news and hate speech. As
a result, some governments have attempted to pass legislative declarations that they
hope will suppress fake news and hate speech. For example, Ethiopia’s government has
enacted the hate speech and disinformation prevention and suppression proclamation
No. 1185/2020 [13]. Ethiopia’s cabinet has approved a notice to combat fake news and
hate speech, which includes expanding Facebook’s third-party fact-checking to Ethio-
pia and other African countries [14, 15]. According to the proclamation of [16], article
19, is concerned about the wording and application of Ethiopia’s hate speech and disin-
formation laws against those who oppose the government’s policies. The proclamation
to prevent the spread of hate speech and false information, which went into effect on
March 23, 2020, is extremely problematic from the standpoint of human rights and free
speech and should be immediately revised. In any case, while the proclamation is still
in effect, it must not be abused, and the government must not abuse its power under
the guise of dealing with the public health crisis. Ethiopians now have unprecedented
civil and political liberties because of the country’s new government. When the press
and broadcast media were censored in previous years, social media gave Ethiopians, like
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many others around the world, the freedom to speak, organize, mobilize, and challenge
the government’s narrative. Despite these changes, one thing has remained constant:
authorities continue to challenge the relative “freedom” that social media platforms have
enabled. While the previous administration surveilled, blocked, and punished dissenting
voices online, prime minister Abiy’s administration has enacted the hate speech and dis-
information prevention and suppression proclamation, which gives the government the
authority to fine and imprison citizens for their social media activities [17].
To address hate speech and disinformation, which have historically troubled the coun-
try, Ethiopia enacted the hate speech and disinformation prevention and suppression
proclamation in March 2020 [18]. However, while government regulation is necessary
to control hate speech, Ethiopia’s new law threatens online freedom of expression and
access to information.
As a result, it seems to be less useful, as fake news and hate speech creators conceal
their work, leaving no record for the law. Using various methods, Facebook, Google,
Twitter, and YouTube tried to take technological precautions.
Linguistic resources are vital in the creation of fake news and hate speech detection
approaches. However, “low-resource” languages, primarily African languages, lack such
tools and resources [11]. Ethiopia has established a policy to introduce four more work-
ing languages in addition to Amharic, which has traditionally served as the country’s
working language. The government will adopt Afan Oromo, Ethiopia’s most frequently
spoken language, as well as Afar, Somali, and Tigrigna as official languages in the future
[19]. Despite this, Ethiopian languages remain among the world’s “low-resource” lan-
guages, lacking the tools and resources required for natural language processing applica-
tions and other techno-linguistic activities. However, a lack of appropriate datasets and
good word embedding have made it difficult to create detection techniques that are reli-
able enough [11]. Recent improvements in natural language processing and understand-
ing have made it possible to detect and counteract fake news and hate speech in textual
streams with greater accuracy by using different approaches.
With the growing influence of social media platforms in affecting public opinion and
ideas around the world, there has been a greater focus on recognizing and combatting
fake news and hate speech on various platforms [20]. Currently, in Ethiopia, hate speech
and the spread of fake news have already impacted the lives of millions of people. Some
schools, public and private universities, or colleges have recently closed; business activi-
ties have been severely hampered due to the closure of major roads in the country; cit-
izen movement has been severely hampered; millions have been displaced, and many
thousands have died due to scarcity of food and shelter [21].
Accordingly, all Ethiopians are suffering more from the harmful effects of social media,
than those in other developing countries [21]. As described in [22], fighting against fake
news and hate information is to save lives. Fake news, misinformation, and hate speech
have flourished in Ethiopia’s media ecosystem, especially in online systems [23]. This is
strongly linked to significant, tragic, real-world consequences, which exacerbated pre-
existing tensions and contributed to violence and conflict. To date, the Ethiopian gov-
ernment’s response to the spread of fake news, misinformation, and hate speech has
been heavy-handed, with the go-to response to escalation being to turn off the inter-
net for the entire country. However, as the Internet and social media communications,
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such as Twitter, YouTube, and Facebook messages, have evolved, so have the chances
and obstacles to developing such solutions. The fake news and hate speech detection
method used to detect and counteract fake news and hate speech on social media is far
from flawless [24].
For foreign and Ethiopian languages, several studies have been undertaken to detect
and counteract fake news and hate speech on various social media platforms. Research-
ers have been conducted to detect and combat fake news and hate speech from vari-
ous social media for Ethiopian languages [11, 19, 20, 25, 26] and have advised future
researchers to collect more corpora from various sources and use different approaches
to improve the performances of the system in detecting and combatting of fake news
and hate speech from various social media platforms. This study is planned to review
the implemented approaches for fake news and hate speech detection research works in
Ethiopian languages and to recommend the best approach regarding the performances
of the evaluation metrics for future researchers of the area to minimize the risks that
come due to the widespread of fake news and hate speech among the societies.
The rest of the paper is organized into different but interrelated sub-sections. The
paper begins by discussing the related works in "Related works" section, results and dis-
cussions in "Results and discussion" section, and the paper is a conclusion and future
works in "Conclusion and recommendation" section.

Related works
In recent years, there has been an increase in scientific interest in detecting and combat-
ing fake news and hate speech. This was caused by the spread of hatred and other nega-
tive emotions on social media platforms. The Amharic language fake news classification
and detection on social media have been developed by using the ML approach [5]. The
author has proposed an AI method to develop a solution to fake news on the internet.
The review attempted to explicitly create, execute, and consider AI and text highlight
extraction techniques for counterfeit news recognition in the Amharic language. The
discussion expanded on the current online media administrations for detecting fake
news.
Authors [11] investigated the identification of fake news in the Amharic language
using DL approaches, and news content, as well as developing many computational lin-
guistic tools for these “low-resource” African languages.
DL approaches and word embedding were employed by the researchers to develop
automatic fake news detection mechanisms. A general-purpose Amharic corpus
(GPAC), a novel Amharic fake news detection dataset (ETH FAKE), and Amharic fast-
text word embedding are among the contributions. As a result, the Amharic fake news
detection model was evaluated using the ETH FAKE dataset and performed excep-
tionally well when utilizing the Amharic fasttext word embedding (AMFTWE). Using
both word embeddings, cc-am-300 and AMFTWE, the fake news detection model per-
formed exceptionally well. When using the 300 and 200 dimension embeddings, the
model had a validation accuracy of above 99%. They have included the experimental
results of the model performance utilizing the cc-am-300 and AMFTWE embeddings,
which were with an accuracy of 99.36%, precision of 99.30%, recall of 99.41%, and an
f1-score of 99.35%. Finally, they suggested using other word embedding approaches,
Demilie and Salau Journal of Big Data (2022) 9:66 Page 5 of 17

such as bidirectional encoder representations from Transformers (BERT), which could

help train a word embedding possibly better than AMFTWE if BERT’s data-hungry
nature was satisfied, even if creating an Amharic fake news dataset and obtaining many
Amharic corpora would be difficult.
The work [27] has outlined that developing hate speech detection for Afan Oromo
social media is essential to eliminate the risk of hate speech on social welfare. They
have conducted experiments six times by applying ML approaches such as support vec-
tor machine (SVM), multinomial Naïve Bayes (MNB), linear support vector machine
(LSVM), logistic regression (LR), and random forest (RF) classifier to build hate speech
detection prototypes for Facebook and Twitter platforms.
Even though, they have developed the Afan Oromo hate speech detection model using
ML approaches by collecting data from Facebook and Twitter platforms. The study only
investigated posts and comments in textual documents. The posts and comments in the
form of images or photos, audio, or video data have not been considered. Researchers
employed performance criteria like accuracy, precision, recall, and f1-score to evaluate
the performance. ML feature selection approaches such as bigram and term frequency-
inverse document frequency (TF-IDF) were used. According to the findings, SVM
achieved an LSVM performance precision of 66%, a recall of 66%, and an f1-score of
64%. The precision of 60%, recall of 65%, and f1-score of 62% were all reached with the
MNB. A precision of 64%, a recall of 64%, and an f1-score of 63% were achieved for the
RF classifier. Performance precision was 65%, a recall was 64%, and f1-score was 61%
for the LR classifier. Accordingly, the SVM achieved a performance precision of 66%,
a recall of 65%, and an f1-score of 63%. They analyzed its performance and discovered
that LSVM has the highest precision, recall, and f1-score values of 66%, 66%, and 64%,
respectively. Therefore, the researchers agreed to use LSVM to deploy the Afan Oromo
hate speech detection model.
The most important limitation of this study lies in applying conventional ML
approaches that need manual labeling of the dataset. The experiments conducted on the
data were small. They have recommended and concluded the research work as future
research can also be conducted by collecting data from other social media platforms. In
addition to collecting data from other social media platforms, researchers can consider
other modes of data collection for further research to be investigated.
According to those researchers, going beyond conventional ML approaches for experi-
ments can also be the next study. Another research work [25] came up with the Afan
Oromo fake news detection system. The proposed system includes preprocessing such
as tokenization, normalization, stop word removal, and abbreviation resolution, fea-
ture extraction such as term frequency-inverted document frequency (TF-IDF), term
frequency (TF), and hash to determine word importance in the news and the corpus,
and N-grams, a powerful natural language processing technique for capturing seman-
tic and syntactic information. With a passive-aggressive classification system, all con-
ceivable combinations of feature extraction techniques and natural language processing
approaches were applied. According to the study, passive-aggressive (PA) outperforms
ensemble methods like gradient boosting and random forest, as well as linear classifi-
ers like MNB. The PA outperforms with 97.2% and an error of 2.8%. Finally, utilizing
the TF-IDF feature extraction using Unigram and PA classification approaches, a Python
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Django was utilized for the web-based deployment of the model. 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% receiver operating char-
acteristic area under the ROC Curve (ROC AUC) f1-score.
Using a DL system, the work [28] aimed to detect Amharic language fake news. They
employed a newly acquired dataset to complete their research because there were no
previously available resources in the area they wanted to investigate. They used the graph
application programming interface (API) to collect data from the Facebook platform,
and two journalists annotated the dataset. To ensure that the data is uniformly anno-
tated across various annotators, guidelines from the news literacy project were used,
resulting in an annotated dataset of 12,000 stories with a binary class. They have used
equal-sized class instances, 6,000 for each fake and genuine class, to avoid an issue with
an imbalance in the number of instances in each class and to be dependable on classifi-
cation reports. With an accuracy of 93.92%, a precision of 93%, recall of 95% (which is
smaller than bidirectional long short-term memory’s (Bi-LSTM’s) 96%), and an f1-score
of 94%, the convolutional neural network (CNN) model outperform 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
to 92%. Finally, CNN was shown to be the most effective model in the investigation.
Furthermore, contrary to their expectations, the attention mechanism used in the
sequential models performs worse than the baseline model. Another finding of the study
was that in the Amharic language fake news dataset, morphological normalization was
not always helpful in improving model performance. According to this study, evaluating
different approaches from other disciplines, such as capsule networks (CN), would be a
good idea. The CN is doing better in the world of computer vision, and applying their
strength to the NLPA challenges could assist in improving the Amharic language fake
news and hate speech detection model. Furthermore, they recommend that research-
ers interested in this field should have to train their embeddings with domain-specific
data to obtain a more semantically strong embedding model, which could lead to better
detection. According to [29], DL approaches have recently gained a lot of attention and
have improved the state-of-the-art for many difficulties that artificial intelligence and
ML approaches have faced for a long time. The goal of the research was to provide a
method for detecting fake news on social media using the DL approach for Afan Oromo
news text. A model to predict and classify Afan Oromo news text must be preprocessed
and trained on the sample dataset. As a result, the researchers looked at one hot encoder
for mapping category integers and used it in the context of word embedding by training
it with Bi-LSTM and a cosine similarity measure, which are supplied as input features to
the neural network (NN). After the classifier was trained to classify, a 0.5 threshold was
applied to the output score to decide whether it was true or fake, and statistical analysis,
a confusion matrix was used to compare across different thresholds, and the suggested
model necessitated a large amount of data.
However, when compared to the dataset created for the English language, the dataset
in the Afan Oromo language is a major concern; the model is trained on very minimal
data. Boosting the consistency of the performance by adding data to the news dataset
Demilie and Salau Journal of Big Data (2022) 9:66 Page 7 of 17

would increase user trust in the system. 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%, outper-
forming the current state of the art applying the Bi-LSTM model. Finally, they concluded
that the Bi-LSTM system prototype can be used as a foundation for future work with
the Afan Oromo news text datasets and other Ethiopian local languages. Another work
on Afan Oromo text content-based fake news detection using MNB [19] found that the
best performing models were an MNB Classifier with word frequency, feature extrac-
tion, and unigram, which had a classification accuracy of 96%. The model was tested
using 0.7 thresholds, which may not be the most reliable for models with poorly cali-
brated probability scores. term frequency performs better, yet frequent but not crucial
terms have an impact on the outcome. These obstacles limited the scope of the study
and prevented it from being more broadly applicable. They used TF, TF-IDF, and TF-
IDF) of unigram and bi-grams, and discovered that the term frequency of unigram of
this model identifies fake news sources with 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. As a result, it was decided that these difficulties,
as well as slang phrases, would be addressed in future work. According to [26], social
media platforms’ quick growth and expansion have filled the information-sharing gap in
everyday life. The Amharic language fake news dataset was created using verified news
sources and social media pages, and six different ML approaches were designed, includ-
ing Naïve Bayes (NB), SVM, LR, SGD, RF, and PA Classifier. The experimental results
show a precision of 100% RF for both TF-IDF and Count Vectorizer (CV), a recall of 95%
using the PA classifier for TF-IDF, and an f1-score of 100% in NB and LR classifier for
TF-IDF vectorizer using PA classifier. The research has made a substantial contribution
to slowing the spread of misinformation in vernacular languages. The work [30] sought
to create, implement, and analyze hate speech detection systems for the Amharic lan-
guage using ML approaches and text feature extraction. According to the study, it was
critical to comprehend and define hate and offensive speech on social media, investigate
existing techniques for addressing the issues and comprehend the Amharic language in-
depth, as well as the various methods used to implement and design models capable of
detecting hate speech. Collecting posts and comments for the dataset, defining anno-
tation rules, preprocessing, features extraction using N-gram, TF-IDF, and word2vec,
model training using SVM, NB, and RF, and model testing are some of the approaches
used. The experiment produced twenty-one (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. The SVM with word2vec, on the other hand, outperforms
NB and RF models in classification with a 73% f1-score. In addition, the ternary SVM
model using word2vec produced a 53% f1-score, which is better than the NB and RF
models. Finally, in both datasets utilized in this study, models based on SVM employ-
ing word2vec performed marginally better than NB and RF models. The work [31] uses
LSTM and GRU with word N-grams for feature extraction and word2vec to repre-
sent each unique word by vector representation to construct recurrent neural network
(RNN) models for automated hate speech post identification from the Amharic lan-
guage posts and comments on Facebook. To train the model and identify the optimum
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hyper-parameters combination for automated hate speech post and comment detection,
an experiment was done on the two models, utilizing 80% of the data set for training
and 10% for validation. The remaining 10% of the dataset was utilized to test the model
after it had been trained. As a result, by training 100 epochs, an LSTM-based RNN with
batch size 128, learning rate of 0.1%, RMSProp optimizer, and 0.5 dropouts achieves an
accuracy of 97.9% in detecting posts as hate speech or free. This was ensured by apply-
ing the model’s performance test and inference on user-generated data to test the mod-
els. The RNN-LSTM model produced an improved test accuracy of 97.9% when used
with this dataset and different parameters on GRU and LSTM based RNN models by
feature representation of word2vec. Finally, they found that using DL neural network
models for the Amharic language text data analysis allowed them to detect hate speech
posts on the Facebook platform, with LSTM outperforming GRU on their dataset. The
accuracy of the DL approach is affected by changes in neural network hyperparameters.
The research work [21] reported on an examination of the first Ethiopic Twitter dataset
for the Amharic language, which was aimed at detecting abusive speech. The research-
ers evaluated the distribution and trend of abusive speech material over time, compared
abusive speech content from Twitter and a general reference Amharic language corpus,
and gathered 144 abusive speech keywords from five native speakers of the language and
classified them as hate and offensive speech.
The research work [32] created an apache spark (AS) model to categorize the Amharic
language Facebook posts and comments into hate and non-hate categories. For learn-
ing, the authors used RF and NB, and for feature selection, they used Word2Vec and
TF-IDF. The NB classifier with the word2Vec feature model outperformed the Face-
book social network for Amharic language posts and comments regarding the accuracy,
ROC score, and the area under precision and recall, with 79.83%, 83.05%, and 85.34%
of 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. The TF-IDF is next with
63.55%, 68.44%, and 69.96% of accuracies, respectively. In [33], a model for detecting
hate speech and identifying vulnerable communities in Amharic texts on the Facebook
platform was developed. They gathered the Amharic language postings and comments
from questionable public profiles of organizations and individuals on social media. To
get a clean corpus, the necessary preprocessing was done according to the language’s
requirements. The word embedding (Word2Vec) model was then trained, and human
annotators were chosen to label texts using the standards and norms that have been
provided. Following that, in the AS environment, feature extraction approaches using
Word2Vec word embedding controlled by TF-IDF, TF-IDF alone, and word N-grams
were used. In their trials, the RNN-LSTM and RNN-GRU DL approaches were com-
pared to the standard GBT and RF approaches. The best performances were achieved
using Word2Vec embedding and RNN-GRU, which had an AUC of 97.85% and an accu-
racy of 92.56% in the hate speech detection experiments. Finally, they suggest that other
inherent problems in the RNN can be solved with a more powerful architecture (that
can handle negation and use information throughout the posts and comments), such as
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tree-LSTM, which can learn meanings from characters and parts of words, rather than
word tokens themselves, as they have done. Automatic hate and offensive speech detec-
tion framework from social media have been implemented for the Afan Oromo language
[34]. The overall goal of this study was to create a framework for categorizing hate and
neutral speech. The researchers recommended using SVM with TF-IDF, N-gram, and
W2vec feature extraction to create a binary classifier dataset for detecting hate speech
in the Afan Oromo language. To create the dataset for this study, they used Face Pager
and Scrap Storm API to scrape data from Facebook posts and comments. Following data
gathering, they divided the information into two categories: hatred and neutrality. Addi-
tionally, when they compared the outcomes of several ML approaches, accuracy, f-score,
recall, and precision measurements were used to evaluate the experiment.
In all evaluation measures, the framework based on SVM with N-gram combination
and TF-IDF achieves a performance of 96% (accuracy, f1-score, precision, and recall).
A summary of the related work that has been used in this review work is presented in
Table 1.
According to the summarization of the relevant related works of the study, Table 1
indicates DL approaches are currently chosen by researchers over ML approaches
because of their efficiency in learning from large-scale corpora in unlabeled text.

Results and discussion

The Internet is one of the most valuable sources of information for its users. Many social
media platforms, such as Facebook and Twitter, allow users to be connected. Many types
of news and speeches are also spread on these platforms. People now prefer to get their
news and speeches from these platforms because they are easy to use and navigate. They
also benefited from the ability to post comments, react, and so on these platforms. These
advantages entice users to visit these platforms. However, because of their advantages,
cyber thieves rely on these platforms as their primary source.
These people can use these platforms to spread fake news and hate speech. There
is also the option of sharing posts or news on different networks, which is useful for
spreading fake news and hate speech. People started to believe such news and spread
it to others. According to [11] and [27], it is impossible to prevent fake news and hate
speech from spreading on these social media platforms. Everyone may sign up for these
platforms and begin spreading the information. A user can construct a page to act as a
news source and propagate fake information. Thus, platforms do not check to see if users
are legitimate publishers or not, and they can spread fake information about individu-
als or organizations in this way. Fake news and hate speech have the potential to hurt
society or political parties. As a result, there is a need to detect and combat the spread
of fake news and hate speech to save individuals, political parties, or organizations’
reputations [11]. Figure 1 shows an analysis of the assessment metrics used in various
studies to assess the performances of all the implemented approaches for hate speech
and fake news systems. The most widely used performance measurements are accuracy,
precision, recall, and f1-score as expressed in Eqs. (1–4). The model needs higher accu-
racy, precision, recall, and f1-score to detect fake news and hate speech effectively and
efficiently. As a result, these four assessment criteria should be used as performance
measures to assess the suggested approaches’ efficiency. Accuracy, recall, f1-score, and
Table 1 Summary of related works for Ethiopian languages
Author (s) Approaches Contributions of Author (s) Evaluation Metrics

[11] DL and word embedding ✓ They have collected and arranged a sizable Amharic corpus for ✓ Using both word embedding, cc-am-300 and AMFTWE, the
general use. fake news detection model performed exceptionally well.
Demilie and Salau Journal of Big Data

✓ They’ve developed an Amharic fasttext word embedding system. ✓ When using the 300 and 200-dimension embedding, the
✓ They’ve created a brand-new dataset for detecting bogus news in model had a validation accuracy of above 99%.
Amharic. ✓ Finally, they included the experimental results of the model
✓ They’ve developed a DL strategy for detecting bogus news in performance utilizing the cc-am-300 and AMFTWE embed-
Amharic. ding, which were accuracy of 99.36%, precision of 99.30%, recall
✓ They ran a series of tests to see how well the word embedding and 99.41%, and f1-score of 99.35%.
(2022) 9:66

fake news detection models worked.

[27] ML (i.e., SVM, MNB, LSVM, LR, DT, and RF) ✓ They built a model that detects Afan Oromo hate speech on social ✓ They analyzed its performance and discovered that the LSVM
media using a combination of n-gram and TF-IDF feature extraction classifier has the highest precision, recall, and f1-score values of
methodologies. 66%, 66%, and 64%, respectively.
✓ They collected 13,600 comments and posts on respective public
pages using Facepager (https://​facep​ager.​softw​are.​infor​mer.​com/3.​6/),
of which 7000 and 6600 data were acquired from Twitter and Facebook,
respectively, between September 2019 and 2020.
[25] NLP and PA ✓ They’ve created a news corpus called Afan Oromo. ✓ Despite the dataset’s shortcomings, the Linear PA with TF-
✓ The general architecture of Afan Oromo fake news detection based on IDF vector and unigram model outperforms the competition
text content is provided. with 97.2% of precision, 97.9% of recall, and 97.5% of ROC AUC
✓ The article addresses the fundamental obstacles in building text f1-score.
content-based false news detection approaches, as well as potential
solutions.
✓ The study compares supervised ML methodologies by taking linguis-
tic features and feature extraction methods into account.
✓ The research sets the door for the development of bogus news identi-
fication in Afan Oromo, which would boost user confidence.
[35] DL (including the Bi-GRU and CNN, and ✓ They collected and tagged a dataset of 12,000 news stories to create ✓ With an accuracy of 93.92%, a precision of 93%, a recall of
attention-based models) an automated method for detecting false news. 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 normali-
zation harms classification performance, lowering both models’
f1-score from 94–92%.
Page 10 of 17
Table 1 (continued)
Author (s) Approaches Contributions of Author (s) Evaluation Metrics
[29] DL (including RNN, Bi-LSTM)
Demilie and Salau Journal of Big Data

✓ They implemented DL models and classified them into pre-defined ✓ On a benchmark dataset, the model can predict with
fine-grained categories to resolve social media fake news for the Afan an accuracy of 90%, precision of 90%, recall of 89%, and an
Oromo language. 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 ✓ They used TF, TF-IDF, and TF-IDF of unigram and bi-grams,
News datasets and accurately categorized them as real and fake news on and discovered that TF of unigram of this model identifies fake
(2022) 9:66

similar topics. 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, ✓ The research has made a substantial contribution to slowing the ✓ The experimental results show a precision of 100% RF for
and PA Classifier model) spread of misinformation in vernacular languages. both TF-IDF and Count Vectorizer, a recall of 95% using PA clas-
sifier 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 min- ✓ They gathered posts and comments from Facebook using Face pager’s ✓ The experiment produced 21 binary and ternary models for
ing feature extraction techniques content retrieval techniques to create the dataset for this investigation. 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 pro-
duced 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.
Page 11 of 17
Table 1 (continued)
Author (s) Approaches Contributions of Author (s) Evaluation Metrics

[31] RNN (by using LSTM and GRU with word ✓ Researchers created a tagged massive Amharic dataset by gather- ✓ The RNN-LSTM model produced an improved test of 97.9%
n-grams for feature extraction and word2vec ing posts and comments from activists who actively participated on for all matrices when used with this dataset and different
to represent each unique word by vector Facebook sites. parameters on GRU and LSTM-based RNN models by feature
Demilie and Salau Journal of Big Data

representation) representation of word2vec.

[36] Spark ML ✓ Thousands of Amharic posts and comments on suspected social ✓ The NB approach with the word2Vec feature model outper-
network pages of organizations and individual people’s public pages are formed the Facebook social network for Amharic language
crawled as a dataset to execute the various experiments. posts and comments in terms of accuracy, ROC score, and area
under precision and recall, with 79.83%, 83.05%, and 85.34%
(2022) 9:66

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 ✓ The suggested method looks into how hate speech detection might ✓ Word2Vec embedding with RNN-GRU had the best perfor-
word embedding (Word2Vec) model be applied to identifying susceptible communities. mance in the hate speech detection experiments, with an AUC
✓ Using the example of Amharic text data on Facebook, they were able of 97.85%, an accuracy/precision of 92.56%, recall of 97.85%,
to identify a potentially vulnerable community in terms of social media and an f1-score of 98.42%
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 extrac-
tion.
[34] ML (including SVM with TF-IDF, N-gram, and ✓ Create a tagged hate speech dataset from social media for the Afaan ✓ Accuracy, f-score, recall, and precision measurements are
W2vec feature extraction) Oromo language. used to evaluate the experiment.
✓ They create standard Afan Oromo stop word lists, as well as a brief ✓ In all evaluation measures, the framework based on SVM
word expansion dictionary. with n-gram combination and TF-IDF achieves 96% (accuracy,
✓ For Afan Oromo text hate speeches, they create an SVM model. f1-score, precision, and recall).
✓ They put their new model to the test on hate speech identification
and came out on top.
Page 12 of 17
Demilie and Salau Journal of Big Data (2022) 9:66 Page 13 of 17

Precision Recall F1 score

99.41
99.35

98.42
97.85
99.3

100
100
97.9

97.9
97.9
97.9
97.8
97.5
97.2

92.56

96
96
96
95

95
94

94
93

91
90

80.53
79.93
89
89

73.02
76
75
73
66
66
64

Fig. 1 Performance of the implemented approaches

precision were the metrics used to evaluate the approach for typical fake news and hate
speech detection systems constructed using DL and ML approaches, as shown in Fig. 1
and Table 1.

TP
Accuracy = (1)
TR

TP
Precision = (2)
TP + FP

TP
Recall = (3)
TP + FN

2*(Precision*Recall)
F1score = (4)
Precision + Recall

where TP represents the number of correctly categorized fake news in the real news cat-
egory, FP represents the number of incorrectly classified fake news in the negative news
category, FN represents the number of fake news incorrectly classified in the negative
news category, and TR represents the total number of the languages news and speech in
the test data. In Fig. 1, the evaluation metrics are different even from the same DL and
ML approaches. Accordingly, this review work presents the approaches from which the
best evaluation performances have been achieved.
Based on the reviewed research work, the researcher discovered that most researchers
achieved better evaluation results in DL approaches than in ML approaches for develop-
ing fake news and hate speech detection systems, as shown in Fig. 2. It can be observed
Demilie and Salau Journal of Big Data (2022) 9:66 Page 14 of 17

96, 9% 99.3, 9%

92.56, 9% 66, 6%

97.2, 9%
79.93, 7%

97.9, 9% 93, 9%

76, 7% 90, 8%

100, 9% 91, 9%

DL and WE SVC, MNB, LSVC, LRDT, and RFC

NLP and PA Bi-GRUand Convoluonal
RNN, Bi-LSTM MNBC
NB, SVM, LR, SGD, RF and PAC SVM, NB, RF, and TMFE
LSTM and GRU with word n-grams W2vec SML
CGBT, RF RNN-LSTM, RNN-GRU, and W2Vec SVM with TF-IDF, N-gram, and W2vec [31]

Fig. 2 Distribution of implemented approaches

that SVM with TF-IDF, N-gram, and W2vec is 9%. DL and WE account for 9% of the
total, while SVC, MNB, LSVC, LRDT, and RFC account for 6%. NLP and PA are 9%,
while Bi-GRU and Convolutional are 9%. RNN, Bi-LSTM is 8%, MNBC is 9%, NB, SVM,
LR, SGD, RF, and PAC is 9%, SVM, NB, RF, and TMFE is 7%, LSTM, and GRU with
word n-grams W2vec is 9%, SML is 7%. CGBT, RF, RNN-LSTM, RNN-GRU, and W2Vec
are 9%. In Fig. 2, most researchers have combined different approaches, such as DL and
ML, to detect and combat fake news and hate speech on various social media platforms.
According to the work reviewed in this study, using ML approaches produced the best
results due to their efficiency in learning from large-scale unlabeled text corpora.
According to the observed results, future researchers in the area can use ML
approaches with great consideration of large-scale datasets to increase the perfor-
mance evaluation metrics (detection rates) of the systems. According to researchers in
[11, 27, 32] the various DL and ML approach trained to recognize fake news and hate
speech should follow the process depicted in Fig. 3.
There has been no research work on detecting and combating fake news and
hate speech that has produced 100% accuracy for Ethiopian languages. As a result,
all researchers in the field have used various fake news and hate speech detection
approaches, which have proven effective in detecting fake news and hate speech on var-
ious social media platforms. Accordingly, the performance of any fake news and hate
speech detection system for the Ethiopian languages depends on a detailed examination
of the datasets, including the size of the data and the platform from which the data has
been collected. The fake news and hate speech detection system, which incorporates
all essential features from various social media platforms, can be used to build the best
and most appropriate fake news and hate speech detection model capable of detecting
Demilie and Salau Journal of Big Data (2022) 9:66 Page 15 of 17

Fig. 3 Dataset training process for fake news and hate speech systems

all types of features for Ethiopian languages. Finally, testing fake news and hate speech
detection systems on large-scale text collections derived from various sources that can
represent all features better than a small-scale pattern will improve the accuracy of the
fake news and hate speech detection systems for Ethiopian languages.

Conclusion and recommendation

This review provides new researchers with up-to-date knowledge, recent researchers’
inclinations, and advancements in the arena by providing a comprehensive assessment
of fake news and hates speech detection approaches based on DL and ML approaches.
A systematic review strategy was used as a method to prioritize and select Ethiopian
language research works in the field of AI-based fakenews and hate speech detection
systems. Based on the analyzed research work, the theoretical concepts of fake news and
hate speech detection approaches have been thoroughly presented.
The approaches used by each research work have then been presented, as well as all
the research’s evaluation metrics regarding the detection models’ competence and dif-
ficulty. According to this review work, new research reveals that using a DL approach
improves the system’s performance regarding all detection rates. Research work that has
been implemented using DL approaches with great consideration of the collected data-
sets will produce the best performance in all evaluation metrics (detection rates) of the
system. Finally, based on the comparative results of the implemented approaches, DL
approaches have been recommended for other Ethiopian languages to increase the per-
formance of all evaluation metrics from different social media platforms. Additionally, as
the review results indicate, the combination of DL and ML approaches with a balanced
dataset can improve the detecting and combating performance of the system.
Acknowledgements
Not applicable.
Demilie and Salau Journal of Big Data (2022) 9:66 Page 16 of 17

Author contributions
WBD: Prepared the manuscript including analysis, data curation, visualization, conceptualization, methodology, and
writing of the original draft. AOS: Performs the tasks including conceptualization, validation, writing, review, and editing
of the final work. Both authors read and approved the final manuscript.

Funding
Not applicable.

Availability of data and materials

Not applicable.

Declarations
Ethics approval and consent to participate
Not applicable.

Consent for publication

Not applicable.

Competing interests
The author declares that no competing interest.

Author details
1
Department of Information Technology, Wachemo University, Hossana, Ethiopia. 2 Department of Electrical/Electronics
and Computer Engineering, Afe Babalola University, Ado‑Ekiti, Nigeria.

Received: 27 January 2022 Accepted: 2 May 2022

References
1. Buzea MC, Trausan-Matu S, Rebedea T. Automatic fake news detection for romanian online news. Information.
2022;13(3):1–13. https://​doi.​org/​10.​3390/​info1​30301​51.
2. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media. ACM SIGKDD Explore News.
2017;19(1):22–36. https://​doi.​org/​10.​1145/​31375​97.​31376​00.
3. Zhou X, Zafarani R. A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Com-
put Surv. 2020;53(5):1–37. https://​doi.​org/​10.​1145/​33950​46.
4. Chakraborty T, Masud S. Nipping in the Bud: Detection, Diffusion, and Mitigation of Hate Speech on Social
Media.  2022: 1–9.
5. Arega KL. Classification and detection of amharic language fake news on social media using machine learning
approach.  Electr Sci Eng. 2022; 4: 1–6.
6. Hadj Ameur MS, Aliane H. “AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News & Hate Speech Detection
Dataset. Procedia CIRP. 2021;189:232–41. https://​doi.​org/​10.​1016/j.​procs.​2021.​05.​086.
7. Chekol MA, Moges MA, Nigatu BA. Social media hate speech in the walk of Ethiopian political reform: analysis of
hate speech prevalence, severity, and natures. Inf Commun Soc. 2021;0(0):1–20. https://​doi.​org/​10.​1080/​13691​18X.​
2021.​19429​55.
8. HaqCheck, Annual Report on Disinformation in Ethiopia _ Addis Zeybe - Digital Newspaper. 2021.
9. WHO. Director-General ’ s remarks at the media briefing on 2019 novel coronavirus on 8th of February 2020,” Who,
no., 2020; 2019–2021
10. Alsenoy B. General data protection regulation. Data protection law in the EU: roles, responsibilities, and liability.
Proce Comput Sci. 2019. https://​doi.​org/​10.​1017/​97817​80688​459.​021.
11. Gereme F, Zhu W, Ayall T, Alemu D. Combating fake news in ‘low-resource’ languages: Amharic fake news detection
accompanied by resource crafting. Inf. 2021;12(1):1–9. https://​doi.​org/​10.​3390/​info1​20100​20.
12. Kovács G, Alonso P, Saini R. “Challenges of Hate Speech Detection in Social Media. SN Comput Sci. 2021;2(2):1–15.
https://​doi.​org/​10.​1007/​s42979-​021-​00457-3.
13.  Gazette FN. Federal Negarit Gazette of the Federal Democratic Republic of Ethiopia, Content. 2020; 2–7.
14. Shaban ARA. Ethiopia cabinet approves bill to combat fake news, hate speech | Africanews.  2019; 1–2.
15. Admin. Facebook expands third-party fact-checking to Ethiopia, more African countries -.  2019; 1–2.
16. Ethiopia. Ethiopia: Hate speech and disinformation law must not be used to suppress the criticism of the Govern-
ment - Article 19. 2021.
17. Taye B. Ethiopia’s hate speech and disinformation law_ the pros, the cons, and a mystery-Access Now. 2020.
18. Wanyama E. Ethiopia’s New Hate Speech and Disinformation Law Weighs Heavily on Social Media Users and Inter-
net Intermediaries.  2020.
19. Gurmessa DK , Mamo G, Biru JD, Afaan Oromo Text Content-Based Fake News Detection using Multinomial Naive
Bayes, 2020: 1(1); 26–36.
20. Perifanos K, Goutsos D. Multimodal hate speech detection in greek social media. Multimodal Technol Interact.
2021;5:2–10. https://​doi.​org/​10.​3390/​mti50​70034.
21.  Yimam SM, Abinew Ali Ayele1, Biemann C. Analysis of the Ethiopic twitter dataset for abusive speech in
amharic. 2018; 1–5
22. Skjerdal T/CC, Fighting false information to help save lives -Information Saves Lives _ Internews. 2021.
Demilie and Salau Journal of Big Data (2022) 9:66 Page 17 of 17

23. E. I. of Peace, Fake News Misinformation and Hate Speech in Ethiopia. 2021.
24. Stewart E. Detecting Fake News: Two Problems for Content Moderation. Philos Technol. 2021. https://​doi.​org/​10.​
1007/​s13347-​021-​00442-x
25. Gurmessa D. Afaan oromo fake news detection using natural language processing and passive-aggres-
sive.  2020; 2(2); 33–40.
26. T T, R R. Building a Dataset for Detecting fake news in amharic language building a dataset for detecting fake news
in amharic language. Int J Adv Res Sci Commun Technol. 2021; 06(1): 2–9. DOI: https://​doi.​org/​10.​48175/​IJARS​
CT-​1362.
27. Defersha NB, Tune KK. Detection of Hate Speech Text in Afan Oromo Social Media using Machine Learning
Approach. Indian J Sci Technol. 2021;14(31):2567–78. https://​doi.​org/​10.​17485/​ijst/​v14i31.​1019.
28. Hailemichael EN, Fake News Detection for Amharic Language Using Deep Learning. Unpublished, no. 2021; 2–100.
29. Gina P. Abebe Waldesanbet, Faculty of engineering and technology postgraduate, vol. Unpublished, 2021; 2–74.
30. Kenenisa Y, Melak T. Adama, Ethiopia, September 2019,” Hate Speech Detect. Amharic Lang Soc Media Using Mach
Learn Tech By, Unpublished, 2019; 1–103.
31.  Tesfaye SG, Tune KK. Automated Amharic Hate Speech Posts and Comments Detection Model Using Recurrent
Neural Network. Res Sq. 2020; 1–14.
32. Mossie Z, Wang J-H. Social network hate speech detection for the amharic language. Comput Sci Informat Technol.
2018. https://​doi.​org/​10.​5121/​csit.​2018.​80604.
33. Mossie Z, Wang JH. Vulnerable community identification using hate speech detection on social media. Inf Process
Manag. 2020;57(3):102087. https://​doi.​org/​10.​1016/j.​ipm.​2019.​102087.
34. Tulu LGKSG. Automatic Hate and Offensive speech detection framework from social media: the case of Afaan Oro-
moo language.” IEEE. Ethiopia: Bahir Dar; 2022. p. 1–15. https://​doi.​org/​10.​1109/​ICT4D​A53266.​2021.​96722​32.
35. Hailemichael MA, Ermias N. Fake news detection for amharic language using deep learning. Unpublished, 2021;
1–100.
36. Mossie Z, Wang J-H. Social network hate speech detection for the amharic language. Comput Sci Informat Tech.
2018. https://​doi.​org/​10.​5121/​csit.​2018.​80604.

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