• PhD candidate in Center for Artificial Intelligence Technology (CAIT), • Master of Software Technology, College of Information Technology and Computer Science, National University of Malaysia, Malaysia- Bangi, 2014-2016 • Bachelor of Computer Science, College of Science, University of Baghdad, Baghdad – Iraq, 2001-2005 Phone: +9647710300030
The proliferation of fake news, exacerbated by social media and modern technology, presents signi... more The proliferation of fake news, exacerbated by social media and modern technology, presents significant challenges across sectors such as health, the economy, politics, and national stability. This study addresses the limitations of current multimodal fake news detection models, which often struggle to effectively integrate heterogeneous modalities like text and images. We propose a hybrid data fusion approach (HF-TIM) that combines the early fusion of multimodal data with the late fusion of unimodal data, leveraging the strengths of both techniques to enhance detection accuracy. Our HF-TIM approach employs a Softmax classifier for early fusion and integrates it with unimodal features extracted from BERT and VGG-19 classifiers through a neural network-based meta-learning classifier. This approach captures the complementary and unique properties of each modality, resulting in a more comprehensive and robust fake news detection model. Experimental results demonstrate that the HF-TIM method significantly improves classification accuracy across various fake news categories by effectively addressing the complex interrelationships between text and images. Our fine-grained detection model, based on the HF-TIM method, achieved a detection accuracy of 93.4%, outperforming state-of-the-art models in related studies. The proposed hybrid fusion HF-TIM approach offers an innovative and effective solution for multimodal fake news detection, with potential applications extending to other domains.
Arabian Journal for Science and Engineering , 2024
The spread of fake news poses significant challenges across various sectors, including health, th... more The spread of fake news poses significant challenges across various sectors, including health, the economy, politics, and national stability. Social media and modern technology have facilitated the rapid dissemination of fake news, predominantly in multimedia formats. Despite advancements, multimodal fake news detection models struggle to achieve optimal accuracy, primarily due to the quality of feature representation. This study aims to enhance feature representation to improve fake news identification. Pre-trained models for feature extraction, typically designed for general public domains, may not suit the specific characteristics of our task using the Fakeddit dataset. We propose a localized fine-tuning strategy, refining pre-trained BERT and VGG-19 models for accurate multimodal feature representation in fake news detection. BERT was fine-tuned by retraining all layers, while only the last block of VGG-19 was fine-tuned. To further enhance the representations, we made structural modifications to VGG-19, including the use of a global average pooling layer and a redesigned classifier. This approach significantly improved our multimodal fake news detection model’s performance, achieving a high accuracy of 92%. Compared to state-of-the-art studies that use generic pre-trained models, our model demonstrates superior performance. Our research underscores the importance of feature representation in multimodal contexts and opens avenues for exploring the synergy between textual and visual modalities in fake news detection.
Nowadays, many people receive news and information about what is happening around them from socia... more Nowadays, many people receive news and information about what is happening around them from social media networks. These social media platforms are available free of charge and allow anyone to post news or information or express their opinion without any restrictions or verification, thus contributing to the dissemination of disinformation. Recently, disinformation about Islam has spread through pages and groups on social media dedicated to attacking the Islamic religion. Many studies have provided models for detecting fake news or misleading information in many domains, such as political, social, economic, and medical, except in the Islamic domain. Due to this negative impact of spreading disinformation targeting the Islamic religion, there is an increase in Islamophobia, which threatens societal peace. In this paper, we present a Bidirectional Long Short-Term Memory-based model trained on an Islamic dataset (RIDI) that was collected and labeled by two separate specialized groups. In addition, using a pre-trained word-embedding model will generate Out-Of-Vocabulary, because it deals with a specific domain. To address this issue, we have retrained the pre-trained Global Vectors for Word Representation (GloVe) model on Islamic documents using the Mittens method. The results of the experiments proved that our proposed model based on Bidirectional Long Short-Term Memory with the retrained GloVe model on the Islamic articles is efficient in dealing with text sequences better than unidirectional models and provides a detection accuracy of 95.42% of Area under the ROC Curve measure compared to the other models.
Nowadays, social media has become the main source of news around the world. The spread of fake ne... more Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public’s response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users’ comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher’s stance, and emotion analysis of comments, which represent the crowd’s stance, contribute to raising the efficiency of the detection model.
International Journal of Advanced Trends in Computer Science and Engineering, 2021
The Holy Quran is the most significant religious text, which is followedby thebelievers of the Is... more The Holy Quran is the most significant religious text, which is followedby thebelievers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages, to assist those who are not familiar with the Arabic language.Most of the Quranic search systems that support theneeds of who are non-native speakers of the Arabic language of the contents of the Holy Quran are basedon keywords matching. Since there are several known English translations of the Holy Quran and each translation has its terminology based on the style of the translator. In addition to that, there are Islamic terms might be not used in the English language, such as "Hajj" or "Saum,"where these terms might vary from one translation to another. Therefore, these search systems do not support the needs of the user, and often there isno matching between the terms of user's questionandtheterms of the Quran.Consequently, this research has used the synon...
In spite of great efforts that have been made to present systems that support the user's need... more In spite of great efforts that have been made to present systems that support the user's need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user's question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user's questions are asking for Fasting and Pilgrimage. Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.
World Academy of Research in Science and Engineering, Feb 2021
The Holy Quran is the most significant religious text, which is followed by the believers of the ... more The Holy Quran is the most significant religious text, which is followed by the believers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages, to assist those who are not familiar with the Arabic language. Most of the Quranic search systems that support the needs of who are non-native speakers of the Arabic language of the contents of the Holy Quran are based on keywords matching. Since there are several known English translations of the Holy Quran and each translation has its terminology based on the style of the translator. In addition to that, there are Islamic terms might be not used in the English language, such as "Hajj" or "Saum," where these terms might vary from one translation to another. Therefore, these search systems do not support the needs of the user, and often there is no matching between the terms of user's question and the terms of the Quran. Consequently, this research has used the synonyms of WordNet to expand the user's question, and to cover all the possibilities of the terms that might be used in the Quran translation, but there are many of Islamic terms are not available in the WordNet database. The WordNet has been enhanced with a set of Islamic synonyms that have been used in the well-known Islamic translations of the English language, which it was created by the researcher, to increase the likelihood of providing a sure matching between the question's words and the contents of the Quran translation. According to the results of this study, the evaluation of this research proved that the method of question expansion by using the both of the WordNet and the Islamic synonyms showed better accuracy and efficiency than the traditional keywords matching systems, or the system that uses only the WordNet synonyms in the question expansion method in terms of matching all user's questions words with documents and retrieve the correct answers to these questions.
The Holy Quran is the most significant religious text which is followed by the believers of the I... more The Holy Quran is the most significant religious text which is followed by the believers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages to assist those who are not familiar with the Arabic language. The Holy Quran consists of 114 Chapters (Surah) and 6236 Verses. These 114 Chapters have different length. The verse is the smallest segment of the texts of the Quran. The Holy Quran as a book is not classified on topics and its verses describe many topics and many verses even from different chapters converge within the same theme. The number of verses and chapters may share similar topics such as faith and morality. One of the solutions to tackle this issue is the Quran classification. Thus, the aim of this study is to classify the Quranic verses by using the Neural Network (NN) classifier based on the predefined topics in order to provide the readers the relevant Quranic verses depending on their need. This research used the most popular and the widespread of the Holy Quran translation in English language by Abdullah Yusuf Ali as the reference dataset. In this regard, the neural network classifier will address this issue through classifying the Al-Baqarah Surah that represents the Quran into two categories which are the Fasting and Pilgrimage topics. Finally, based on the F-measure, the evaluation of Al-Baqarah classification by using NN showed a level of approximately 90%. This proves that NN has succeeded in presenting a encouraging result in this critical area.
In spite of great efforts that have been made to present systems that support the user's need of ... more In spite of great efforts that have been made to present systems that support the user's need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user's question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user's questions are asking for Fasting and Pilgrimage. Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.
The proliferation of fake news, exacerbated by social media and modern technology, presents signi... more The proliferation of fake news, exacerbated by social media and modern technology, presents significant challenges across sectors such as health, the economy, politics, and national stability. This study addresses the limitations of current multimodal fake news detection models, which often struggle to effectively integrate heterogeneous modalities like text and images. We propose a hybrid data fusion approach (HF-TIM) that combines the early fusion of multimodal data with the late fusion of unimodal data, leveraging the strengths of both techniques to enhance detection accuracy. Our HF-TIM approach employs a Softmax classifier for early fusion and integrates it with unimodal features extracted from BERT and VGG-19 classifiers through a neural network-based meta-learning classifier. This approach captures the complementary and unique properties of each modality, resulting in a more comprehensive and robust fake news detection model. Experimental results demonstrate that the HF-TIM method significantly improves classification accuracy across various fake news categories by effectively addressing the complex interrelationships between text and images. Our fine-grained detection model, based on the HF-TIM method, achieved a detection accuracy of 93.4%, outperforming state-of-the-art models in related studies. The proposed hybrid fusion HF-TIM approach offers an innovative and effective solution for multimodal fake news detection, with potential applications extending to other domains.
Arabian Journal for Science and Engineering , 2024
The spread of fake news poses significant challenges across various sectors, including health, th... more The spread of fake news poses significant challenges across various sectors, including health, the economy, politics, and national stability. Social media and modern technology have facilitated the rapid dissemination of fake news, predominantly in multimedia formats. Despite advancements, multimodal fake news detection models struggle to achieve optimal accuracy, primarily due to the quality of feature representation. This study aims to enhance feature representation to improve fake news identification. Pre-trained models for feature extraction, typically designed for general public domains, may not suit the specific characteristics of our task using the Fakeddit dataset. We propose a localized fine-tuning strategy, refining pre-trained BERT and VGG-19 models for accurate multimodal feature representation in fake news detection. BERT was fine-tuned by retraining all layers, while only the last block of VGG-19 was fine-tuned. To further enhance the representations, we made structural modifications to VGG-19, including the use of a global average pooling layer and a redesigned classifier. This approach significantly improved our multimodal fake news detection model’s performance, achieving a high accuracy of 92%. Compared to state-of-the-art studies that use generic pre-trained models, our model demonstrates superior performance. Our research underscores the importance of feature representation in multimodal contexts and opens avenues for exploring the synergy between textual and visual modalities in fake news detection.
Nowadays, many people receive news and information about what is happening around them from socia... more Nowadays, many people receive news and information about what is happening around them from social media networks. These social media platforms are available free of charge and allow anyone to post news or information or express their opinion without any restrictions or verification, thus contributing to the dissemination of disinformation. Recently, disinformation about Islam has spread through pages and groups on social media dedicated to attacking the Islamic religion. Many studies have provided models for detecting fake news or misleading information in many domains, such as political, social, economic, and medical, except in the Islamic domain. Due to this negative impact of spreading disinformation targeting the Islamic religion, there is an increase in Islamophobia, which threatens societal peace. In this paper, we present a Bidirectional Long Short-Term Memory-based model trained on an Islamic dataset (RIDI) that was collected and labeled by two separate specialized groups. In addition, using a pre-trained word-embedding model will generate Out-Of-Vocabulary, because it deals with a specific domain. To address this issue, we have retrained the pre-trained Global Vectors for Word Representation (GloVe) model on Islamic documents using the Mittens method. The results of the experiments proved that our proposed model based on Bidirectional Long Short-Term Memory with the retrained GloVe model on the Islamic articles is efficient in dealing with text sequences better than unidirectional models and provides a detection accuracy of 95.42% of Area under the ROC Curve measure compared to the other models.
Nowadays, social media has become the main source of news around the world. The spread of fake ne... more Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public’s response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users’ comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher’s stance, and emotion analysis of comments, which represent the crowd’s stance, contribute to raising the efficiency of the detection model.
International Journal of Advanced Trends in Computer Science and Engineering, 2021
The Holy Quran is the most significant religious text, which is followedby thebelievers of the Is... more The Holy Quran is the most significant religious text, which is followedby thebelievers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages, to assist those who are not familiar with the Arabic language.Most of the Quranic search systems that support theneeds of who are non-native speakers of the Arabic language of the contents of the Holy Quran are basedon keywords matching. Since there are several known English translations of the Holy Quran and each translation has its terminology based on the style of the translator. In addition to that, there are Islamic terms might be not used in the English language, such as "Hajj" or "Saum,"where these terms might vary from one translation to another. Therefore, these search systems do not support the needs of the user, and often there isno matching between the terms of user's questionandtheterms of the Quran.Consequently, this research has used the synon...
In spite of great efforts that have been made to present systems that support the user's need... more In spite of great efforts that have been made to present systems that support the user's need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user's question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user's questions are asking for Fasting and Pilgrimage. Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.
World Academy of Research in Science and Engineering, Feb 2021
The Holy Quran is the most significant religious text, which is followed by the believers of the ... more The Holy Quran is the most significant religious text, which is followed by the believers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages, to assist those who are not familiar with the Arabic language. Most of the Quranic search systems that support the needs of who are non-native speakers of the Arabic language of the contents of the Holy Quran are based on keywords matching. Since there are several known English translations of the Holy Quran and each translation has its terminology based on the style of the translator. In addition to that, there are Islamic terms might be not used in the English language, such as "Hajj" or "Saum," where these terms might vary from one translation to another. Therefore, these search systems do not support the needs of the user, and often there is no matching between the terms of user's question and the terms of the Quran. Consequently, this research has used the synonyms of WordNet to expand the user's question, and to cover all the possibilities of the terms that might be used in the Quran translation, but there are many of Islamic terms are not available in the WordNet database. The WordNet has been enhanced with a set of Islamic synonyms that have been used in the well-known Islamic translations of the English language, which it was created by the researcher, to increase the likelihood of providing a sure matching between the question's words and the contents of the Quran translation. According to the results of this study, the evaluation of this research proved that the method of question expansion by using the both of the WordNet and the Islamic synonyms showed better accuracy and efficiency than the traditional keywords matching systems, or the system that uses only the WordNet synonyms in the question expansion method in terms of matching all user's questions words with documents and retrieve the correct answers to these questions.
The Holy Quran is the most significant religious text which is followed by the believers of the I... more The Holy Quran is the most significant religious text which is followed by the believers of the Islamic religion. The translations of the Quran are the interpretation of its meaning in different languages to assist those who are not familiar with the Arabic language. The Holy Quran consists of 114 Chapters (Surah) and 6236 Verses. These 114 Chapters have different length. The verse is the smallest segment of the texts of the Quran. The Holy Quran as a book is not classified on topics and its verses describe many topics and many verses even from different chapters converge within the same theme. The number of verses and chapters may share similar topics such as faith and morality. One of the solutions to tackle this issue is the Quran classification. Thus, the aim of this study is to classify the Quranic verses by using the Neural Network (NN) classifier based on the predefined topics in order to provide the readers the relevant Quranic verses depending on their need. This research used the most popular and the widespread of the Holy Quran translation in English language by Abdullah Yusuf Ali as the reference dataset. In this regard, the neural network classifier will address this issue through classifying the Al-Baqarah Surah that represents the Quran into two categories which are the Fasting and Pilgrimage topics. Finally, based on the F-measure, the evaluation of Al-Baqarah classification by using NN showed a level of approximately 90%. This proves that NN has succeeded in presenting a encouraging result in this critical area.
In spite of great efforts that have been made to present systems that support the user's need of ... more In spite of great efforts that have been made to present systems that support the user's need of the answers from the Holy Quran, the current systems of English translation of Quran still need to do more investigation in order to develop the process of retrieving the accurate verse based on user's question. The Islamic terms are different from one document to another and might be undefined for the user. Thus, the need emerged for a Question Answering System (QAS) that retrieves the exact verse based on a semantic search of the Holy Quran. The main objective of this research is to develop the efficiency of the information retrieval from the Holy Quran based on QAS and retrieving an accurate answer to the user's question through classifying the verses using the Neural Network (NN) technique depending on the purpose of the verses' contents, in order to match between questions and verses. This research has used the most popular English translation of the Quran of Abdullah Yusuf Ali as the data set. In that respect, the QAS will tackle these problems by expanding the question, using WordNet and benefitting from the collection of Islamic terms in order to avoid differences in the terms of translations and question. In addition, this QAS classifies the Al-Baqarah surah into two classes, which are Fasting and Pilgrimage based on the NN classifier, to reduce the retrieval of irrelevant verses since the user's questions are asking for Fasting and Pilgrimage. Hence, this QAS retrieves the relevant verses to the question based on the N-gram technique, then ranking the retrieved verses based on the highest score of similarity to satisfy the desire of the user. According to F-measure, the evaluation of classification by using NN has shown an approximately 90% level and the evaluation of the proposed approach of this research based on the entire QAS has shown an approximately 87% level. This demonstrates that the QAS succeeded in providing a promising outcome in this critical field.
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