1. Introduction
Online media, specifically social media, is easily accessible, cheap, suitable for commenting and sharing, and more timely [1,2,3], which enables it to be favored by many, especially youngsters. However, it also has a dark side: the propagation of hate speech and inauthentic information, such as fake news. Fake news refers to news articles that are intentionally and verifiably false [4,5]. Fake news is increasingly becoming a threat to individuals, governments, freedom of speech, news systems, and society as a whole [3,6,7]. It disturbs the authenticity balance of the news system, creating real-life fears in the world’s societies. To express the spread and bad effect of fake news during the current pandemic, the WHO warned against fake news in the COVID-19 infodemic (https://www.who.int/dg/speeches/detail/director-general-s-remarks-at-the-media-briefing-on-2019-novel-coronavirus---8-february-2020). It said that while the virus spreads, misinformation makes the job of our heroic health workers even harder; it diverges the attention of our decision-makers and it causes confusion and spreads fear in the general public. The list of practical examples of the impacts of fake news is becoming extensive and the danger is already eminent.
To reduce the adverse effects of fake news, governments, the tech industry, and individual researchers have been trying to devise various mechanisms. Governments tried to enact legal proclamations that they believe will suppress fake news. For example, the government of Ethiopia has enacted the Hate Speech and Disinformation Prevention and Suppression Proclamation No.1185/2020 (https://www.accessnow.org/cms/assets/uploads/2020/05/Hate-Speech-and-Disinformation-Prevention-and-Suppression-Proclamation.pdf), though this looks less helpful as creators of fake news hide themselves, and this obscurity leaves no trace for the law. Facebook, Google, Twitter, and YouTube tried to take technological measures, using certain tools. In the development of fake news detection tools, linguistic resources play crucial roles. However, “low-resource” languages, mostly African languages, such as Amharic, lack such resources and tools.
Amharic (አማርኛ, Amarəñña), with the only African-origin script named Ethiopic/Fidel, is the second most spoken Semitic language in the World, next to Arabic, and it is the official working language of the Ethiopian government. As more Ethiopians are living outside their home, the Amharic language speakers in different countries of the world is also growing. In Washington DC, Amharic has gotten the status as one of the six non-English working languages [8]. Furthermore, Amharic is considered as a sacred language by Rastafarians across the world. Despite this, the Amharic language is one of the “low-resource” languages in the world, which lacks the tools and resources important for NLP (natural language processing) and other techno-linguistic solutions. To the best of our knowledge, for the Amharic language, there is no fake news detection dataset and we could not find work done to detect fake news written in Amharic. Moreover, there is a lack of quality Amharic word embedding. The available Amharic corpora are not sufficient and some of them are not freely open to the public. This is a total disadvantage, not being able to benefit from technology solutions.
In this work, we tried to narrow down those gaps. We present several contributions that include the following:
- We collected and organized a huge Amharic general purpose corpus.
- We created Amharic fasttext word embedding.
- We prepared a novel fake news detection dataset for the Amharic language.
- We introduced a deep learning-based model for Amharic fake news detection.
- We performed a series of experiments to evaluate the word embedding and fake news detection model.
The rest of this document is organized as follows. In Section 2, we present the general-purpose Amharic corpus (GPAC). Section 3 explains the Amharic fasttext word embedding (AMFTWE). The Amharic fake news detection dataset (ETH_FAKE) is explained in Section 4. Section 5 is dedicated to the experiments, results, and discussion, while Section 6 concludes our work.
2. GPAC: General-Purpose Amharic Corpus
One of the challenges of content-based fake news detection is the absence of sufficient corpora to train word embeddings, which are used in a multiplicity of NLP applications, including fake news detection [1,9,10,11,12,13,14,15], either to represent the features for traditional classifiers, or to initialize the deep neural network embedding layers. Similarly, the shortage of an appropriate dataset to train fake news detection models is the other bottleneck. Especially African languages labeled “under-resourced”, such as Amharic, suffer from a shortage of such resources.
Amharic is a highly influential language with its own ancient script. Not to mention its early existence and applications, it has been the working language of courts, the military, language of trade, and everyday communications since the late 12th century, and remains the official language of the Ethiopian government today [16,17]. Most of the Ethiopian Jewish communities in Ethiopia and Israel speak Amharic. In Washington DC, Amharic became one of the six non-English languages in the Language Access Act of 2004, which allows government services and education in Amharic [8]. Furthermore, Amharic is considered as a sacred language by Rastafarians. Despite Amharic being highly powerful, it is still one of the “low-resource” languages in the world. A lack of sufficient corpora and linguistic tools to help use technology make the language disadvantaged in this regard. However, there have been a few works done to prepare the Amharic corpus and linguistic tools.
The Walta Information Center (WIC) corpus is a small-sized corpus with 210,000 tokens collected from 1065 Amharic news documents [18]. The corpus is manually annotated for POS tags. It is, however, too small for deep learning applications. The HaBit project corpus is another web corpus, which was developed by crawling the web [19]. The corpus is cleaned and tagged for POS using a TreeTagger trained on WIC.
The Crúbadán corpus was developed under the project called corpus building for a large number of under-resourced languages [20]. The Amharic corpus consists of 16,970,855 words crawled from 9999 documents. This corpus is just a list of words with their frequencies, which is inconvenient for word embedding and other deep learning applications.
The Contemporary Amharic Corpus (CACO) [21] is another corpus crawled from different sources. We checked and got about 21 million tokens from 25,000 documents in this corpus. As we can see in Table 1, the WIC is too small and the Crúbadán is just a list of words and thus inconvenient to train quality Amharic word embeddings; the remaining two are not sufficient for the data-hungry word embedding training. Of course, the POS-tagged corpora are not directly usable for this purpose. Thinking to fill these gaps, we created our own general-purpose Amharic corpus (GPAC (https://github.com/Fanpoliti/GPAC)) collected from a variety of sources. This version of GPAC includes about 121 million documents and more than 40 million tokens.
2.1. Data Collection We collected data from diversified sources and prepared a general-purpose Amharic corpus (GPAC). There are two objectives for preparing this corpus. First, it will be used as a general resource for future NLP research and tool development projects for the “low-resource” language Amharic. Second, added to the other corpora, it will be used to create a good-quality Amharic word embedding, which itself has two objectives. As part of this fake news detection work, it is the backbone of the embedding layer. Secondly, it is a vital resource in many NLP applications and others. 2.2. Data Processing
The preprocessing of the documents involves spelling correction, normalization of punctuation marks, and sentence extraction from documents for the purpose of randomizing the documents. Extracting each statement from individual documents and randomizing them helps make the corpus publicly available for researchers and tool developers without affecting the copyrights, if any. Different styles of punctuation marks have been used in the documents or articles. For quotation marks, different representations such as “ ”, “ “, ‹‹ ››, ‘ ’, ‘ ‘, or « » have been used. We normalized all types of double quotes by “ ”, and all single quotes by ‘ ’. Other punctuation marks were normalized as follows: full stops (like:: and ፡፡) by ።, hyphens (like:-, and ፡—) by ፦, and commas (like፥ and ÷) by ፣. Table 2 summarizes the various multi-domain data sources used to build the corpus.
3. Amharic Fasttext Word Embedding (AmFtWe)
Word embeddings have been used to represent the features for traditional classifiers, or as initializations in deep neural networks. Word embeddings are real-valued representations for text by embedding both the semantic and syntactic meanings obtained from an unlabeled large corpus and are perhaps one of the key advances for the remarkable performance of deep learning methods in challenging NLP (natural language processing) problems, such as content-based fake news detection [6,14,22]. They are widely used in NLP tasks, such as sentiment analysis [23], dependency parsing [24], machine translation [25], and fake news detection [1,9,10,11,12,13,14,15].
Considering the difficulty of the fake news detection problem, fake news detection methods using deep learning can benefit from good-quality word embeddings. Publicly available models, which are pre-trained on large amounts of data, have become a standard tool for many NLP applications, but are mostly available for the English language. Word embeddings for “low-resource” languages are absent or are very limited. For the Amharic language, a fasttext-based word embedding (cc_am_300) was trained by [26], using 300 dimensions. However, the number of word vectors are limited and also it contains uncleaned English tokens.
The distributional hypothesis used in [27,28,29] utilizes the idea that the meaning of a word is captured by the contexts in which it appears, to learn word embeddings. Thus, the quality of the word vectors directly depends on the amount and quality of data they were trained on. Based on this fact, in this work, we introduce a high-quality Amharic fasttext word embedding (AmFtWe (https://github.com/Fanpoliti/AMFTWE)) trained on a huge corpus (GPAC_CACO_WIC_am131516) obtained by merging and deduplicating four corpora (discussed in Section 2), namely, GPAC, am131516, WIC, and CACO, using a fasttext model with sub-word information [30]. Table 3 illustrates the architecture of the word embedding. As the quality of word embeddings directly depends on the amount and quality of data used, the AmFtWe is of high quality. This is manifested in the superior performance of our fake news detection model when it uses AmFtWe compared with cc_am_300 [26].
The very reason we chose fasttext is because Amharic is one of the morphologically rich languages and it is possible to improve the vector representations for morphologically rich languages by using character-level information [30].
We evaluated AmFtWe using an extrinsic evaluation. In an extrinsic word embedding evaluation, we use word embeddings as the input features to a downstream task, in our case fake news detection, and measure the changes in performance metrics specific to that task [31,32,33,34]. For comparison purposes, we use the only available Amharic word embedding presented in [26]. This fake news detection, task-oriented evaluation, as presented in Section 5, shows that AmFtWe is a quality word embedding. The objective of preparing an AmFtWe is not only for the consumption of this paper; it is intended to be a valuable resource for future computational linguistic researches. For this reason, we will make it publicly available through our GitHub link (https://github.com/Fanpoliti/AMFTWE), with various dimensions and file formats (as shown in Table 3).
4. ETH_FAKE: A Novel Amharic Fake News Dataset
The fake news problem is a recent phenomenon and already a research issue, although still relatively less explored [35]. Even though there are research studies done, the scarcity of standard datasets was a common issue raised by many researchers. Deep learning-based fake news detection has been shown to be effective [6,22]. However, the data-hungry nature of this approach and the absence of sufficient datasets have made the research outcomes limited. Even the trials made in preparation of fake news detection datasets focused on the English language. Fake news detection in “under-resourced” languages, such as Amharic, is difficult to do because of the absence of a dataset. Though Amharic is a widely used language, as we have discussed in Section 2, and the impact of fake news in the regions using the language is a big concern—both to the government and society—there has not been any fake news detection research done for the Amharic language and there is no Amharic fake news detection dataset.
This critical problem motivated us to do fake news detection research and prepare an Amharic fake news detection dataset. We created the first Amharic fake news detection dataset with fine-grained labels and named it ETH_FAKE (https://github.com/Fanpoliti/ETH_FAKE). ETH_FAKE consists of 3417 real news articles and 3417 fake news articles gathered from Amharic Facebook pages and online newspapers that accounts to a total of 6834 articles. Table 4 summarizes the architecture of the ETH_FAKE dataset. We discussed the data collection and preprocessing of the data under Section 4.1 and Section 4.2.
4.1. Data Collection As there was no existing Amharic fake news detection dataset, it was compulsory to collect data from the scratch. Obtaining well balanced real and fake pieces of Amharic news is not an easy task; especially getting the fake articles was tiresome. Both real news and fake news articles were obtained from Facebook and two well-known Ethiopian private newspapers, Reporter and Addis Admass. Reporter (Amharic: ሪፖርተር) and Addis Admass (Amharic: አዲስ አድማስ) are private newspapers published in Addis Ababa, Ethiopia. Archives of the online Amharic versions of these newspapers were scrapped using a python script. Even though these newspapers are presumed to broadcast real news, we have done fact checking for the news pieces by employing four senior journalists and a linguist. Articles collected from Facebook passed through the same procedure to check whether they contain factual news. Getting the fake news group was the utmost demanding task. As we could not get Amharic fake news sources, we were required to collect them piece-by-piece from scratch. We collected the fake news articles from Facebook and the aforementioned online newspapers. After identifying check-worthy Amharic Facebook pages, groups, and profiles, we scrapped the pages and fact-checked them manually using a group of senior journalists and a linguist. The ridiculously false statements like bombardment of an existing facility, bridge, or dam; contradictions to general truth; etc. are easily picked as FAKE, where as other contents were analyzed thoroughly. A FAKE label is attached to an article if the content is reporting completely false information or most of the content is verifiably false. Both the real news and the fake news come from multiple domains like sport, politics, arts, education, religion, economics, history, etc. Even though most of the fake news comes from Facebook, we tried to balance the domains of the real and fake sources. 4.2. Preprocessing The absence of well-developed tools, like English and other languages, makes Amharic text preprocessing difficult. Its customary to find multiple languages intermingled in web-scraped texts. We cleaned out mixed texts of English from the news articles. 5. Automatic Fake News Detection: Model, Experiments, and Evaluation 5.1. Evaluation Metrics
We evaluated both the word embedding and the fake news detection model. For the fake news detection model, we used the accuracy, precision, recall, and F1 score as the evaluation matrices. For the evaluation of the word embeddings, we used the extrinsic evaluation technique, which is an evaluation of word embeddings using a specific task with metrics [31,32,33,34]. Our word embedding was evaluated against an existing pre-trained word embedding [26] using the same task—fake news detection.
5.2. Experimental Setup
We wrote both the main project code and the data-scraping script in Python 3, using the TensorFlow r1.10, NumPy, and Keras library. Figure 1 depicts the Amharic fake news detection model based on Convolutional Neural Networks (CNNs). Since CNNs have been proven to show superior performance in text classification tasks, specifically in content-based fake news detection [6,14,22], we used them as model building methods. We present specific details as follows. The fake news detection dataset was preprocessed and split for training and validation in an 80/20 ratio (80% of it for training set and the remainder 20% for validation set). We used an embedding dimension of various sizes with 10,000 unique tokens and a 5000 sequence-length post padded with zeros. The output of the embedding layer was fed into a dense network of 128 neurons with the ReLU (Rectified Linear Unit) activation function. Then this output was passed into a one-dimensional GlobalMaxPooling layer. The output of the GlobalMaxPooling layer was again fed into a dense network of 128 neurons with the ReLU activation function, whose output was finally passed into a one-dimensional dense network with a sigmoid activation function. Rmsprop was used as the optimization technique and binary-crossentropy as the loss function.
We used our word embedding (AMFTWE) with the dimensions 50, 100, 200, and 300 to record the performance of the fake news detection model in different dimensions. For the comparison of the two word embeddings, AMFTWE and cc_am_300, we set up a separate experiment with 300 dimensions, because the pre-trained word embedding (cc_am_300 [26]) is available in 300 dimensions only.
5.3. Results and Discussion
As the experimental results depicted in Table 5 show, the Amharic fake news detection model performed very well. The model scored validation accuracy above 99% while using the 300- and 200-dimension embeddings. This good performance might be attributed to both the fake news detection model and the quality of the word embedding. We could not find a content-based fake news detection work for the Amharic language for comparison.
We recorded a higher performance of our model when it uses the Amharic fasttext word embedding AMFTWE than using the existing Amharic word embedding cc_am_300 presented in [26]. Since the evaluation of cc_am_300 and AMFTWE was made in the same experimental setup, we can say that the higher score of the model using AMFTWE is due to the relatively higher quality of AMFTWE. Hence, AMFTWE will be a valuable resource for future related research. Regarding the choice of dimensions, higher dimensions of AMFTWE, obviously, made the model perform better than the lower dimensions. As the results are proximate, we may opt to use either the 200-dimensional or 300-dimensional pre-trained word embedding, based on our memory and storage availability.
6. Conclusions In this paper, we have studied Amharic fake news detection using deep learning and news content accompanied with the preparation of several computational linguistic resources for this “low-resource” African language. The lack of Amharic fake news detection research, especially due to the lack of both a fake news dataset and a good Amharic word embedding, as well as limitations in the existing Amharic corpora, have motivated us to contribute our share to fill these gaps. Together with the Amharic fake news detection model, we contributed several resources of paramount importance for this work and future research. We created ETH_FAKE: a novel Amharic fake news detection dataset with fine grained labels, which was collected from various multi-domain sources. Considering the lack of quality Amharic word embedding, we prepared AMFTWE: Amharic fasttext word embedding with sub-word information. GPAC, a general-purpose Amharic corpus, was the other contribution of this work. We used GPAC merged with other publicly available corpora to train AMFTWE, which, in turn, was used to initialize the embedding layer of our fake news detection model. Our fake news detection model performed very well using both word embeddings, cc_am_300 and AMFTWE. However, it exhibited a higher performance record when AMFTWE was used compared to cc_am_300, which could be attributed to the quality of our word embedding, which was trained on a relatively huge corpus. As deep learning methods require more data, this work may further be improved by increasing the size of ETH_FAKE and GPAC. On the other hand, using other word embedding algorithms, such as BERT (Bidirectional Encoder Representations from Transformers), could help train a word embedding possibly better than AMFTWE, provided the data-hungry nature of BERT is satisfied. However, crafting an Amharic fake news dataset and obtaining a large number of Amharic corpora will be challenging.
| Corpus | Documents | Tokens | Remark |
|---|---|---|---|
| The Walta Information Center (WIC) | 1065 | 210,000 | |
| The HaBit project (am131516) | 75,509 | 25,975,846 | |
| The Crúbadán | 9999 | 16,970,855 | Just a list of words and frequencies |
| Contemporary Amharic Corpus (CACO) | 25,199 | 21,863,015 | |
| General-Purpose Amharic Corpus (GPAC) | 121,071 | 40,601,139 |
| Sources Types | Instances |
|---|---|
| News Papers | AddisAdmass, Reporter, Goolgule |
| Government communication offices pages (Federal, Regional, Zonal, Woreda), university pages, think tank groups pages, media pages, etc. | |
| Portals | EthiopiaNege, EthiopiaZare, Satenaw, ECADF |
| Forums | Ethiopian Review |
| Media | ESAT, Walta Information Center, BBC News Amharic |
| Books | Academic, fiction, historical, etc. |
| Religious Books | The Holy Bible, newspapers, others |
| Embedding Name | Corpus | Dimension | Size of the .vec File | Size of the .bin File |
|---|---|---|---|---|
| amftwe_300.bin/.vec | GPAC_CACO_WIC_am131516 | 300 | 1.91 gb | 4.2 gb |
| amftwe_200.bin/.vec | 200 | 1.27 gb | 2.2 gb | |
| amftwe_100.bin/vec | 100 | 620 mb | 1.2 gb | |
| amftwe_50.bin/vec | 50 | 312 mb | 620 mb |
| News Group | Number of Articles | News Sources | News Domain |
|---|---|---|---|
| Real News | 3417 | Facebook, Addis Admass newspaper, Reporter newspaper | Sport, politics, art, social, religion, education, economics, history |
| Fake News | 3417 | Facebook, Addis Admass newspaper, Reporter newspaper | Sport, politics, art, social, religion, education, economics, history |
| Word Embedding | Dataset | Embedding Dim | Model Performance | |||
|---|---|---|---|---|---|---|
| Acc | Pre | Rec | F1 | |||
| cc_am_300 | ETH_FAKE | 300 | .9883 | .9850 | .9882 | .9866 |
| AmFtWe | ETH_FAKE | 50 | .9715 | .9631 | .9713 | .9672 |
| AmFtWe | ETH_FAKE | 100 | .9890 | .9898 | .9847 | .9872 |
| AmFtWe | ETH_FAKE | 200 | .9921 | .9910 | .9930 | .9920 |
| AmFtWe | ETH_FAKE | 300 | .9936 | .9930 | .9941 | .9935 |
Author Contributions
Conceptualization, F.G. and W.Z.; methodology, F.G.; software, F.G. and D.A.; validation, F.G., W.Z., T.A. and D.A.; formal analysis, F.G.; investigation, F.G.; resources, T.A.; data curation, T.A.; writing-original draft preparation, F.G.; writing-review and editing, F.G.; visualization, D.A.; supervision, W.Z.; project administration, F.G. and W.Z.; All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Informed Consent Statement
Not applicable.
Data Availability Statement
Publicly available corpora were used in this study, papers associated with these resources are properly cited.
Conflicts of Interest
The authors declare no conflict of interest.
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Fantahun Gereme
1,*,
William Zhu
1,
Tewodros Ayall
2 and
Dagmawi Alemu
2
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
2School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
*Author to whom correspondence should be addressed.
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