Arabic rumor detection: A comparative study

Amoudi, Ghada; Albalawi, Rasha M.; Baothman, Fatimah; Jamal, Amani; Alghamdi, Hanan S.; Alhothali, Areej

doi:10.1016/j.aej.2022.05.029

Cited by 15 publications

(13 citation statements)

References 28 publications

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“…A comparative study was conducted to detect covid-19 rumors using the textual features of tweets from different machine learning and deep learning methods [29]. This study compared the performance of different machine learning methods, SVM, stochastic gradient descent (SGD), LR, knearest neighbors (KNN), NB, RF, XGBoost, and DT, using various feature representations and examined the use of ensemble learning.…”

Section: A Rumor Detection Using Unimodal Approachesmentioning

confidence: 99%

Multimodal Arabic Rumors Detection

et al. 2023

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Recently, the use of social media platforms has increased with ease of use and fast accessibility, making such platforms a place of rumor proliferation owing to the lack of posting constraints and content authentication. Therefore, there is a need to leverage artificial intelligence techniques to detect rumors on social media platforms to prevent their adverse effects on society and individuals. Most existing works that detect rumors in Arabic target the textual features of the tweet content. Nevertheless, tweets contain different types of content, such as (text, images, videos, and URLs), and the visual features of tweets play an essential role in rumor diffusion. This study proposes an Arabic rumor detection model to detect rumors on Twitter using textual and visual image features through two types of multimodal fusion: early and late fusion. In addition, we leveraged the transfer learning of the pre-trained language and vision models. Different experiments were conducted to select the best textual and visual feature extractors for building a multimodal model. MARBERTv2 was used as a textual feature extractor, whereas the ensemble of VGG-19 and ResNet50 was used as a visual feature extractor to build the multimodal model. Subsequently, the language and vision models of the single models were used as a baseline to compare their results with those of multimodal models. Finally, the experimental results demonstrate the effectiveness of textual features in rumor detection tasks compared to multimodal models.

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Section: A Rumor Detection Using Unimodal Approachesmentioning

confidence: 99%

Multimodal Arabic Rumors Detection

et al. 2023

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“…The most used form of Arabic is informal Arabic, which includes variations depending on the country and sometimes on the region [30]. Various Arabic language studies were conducted to develop several Arabic NLP (ANLP) applications and automatically analyze text in multiple domains [31].…”

Section: ) Arabic Natural Language Processingmentioning

confidence: 99%

“…In addition, optimizing the area under the curve (AUC) improves the performance of the models. [31] investigated the use of standard ML and DL models to detect Arabic rumors. The study compared seven optimizers in the DL experiments.…”

Section: Arabic Rumor Detectionmentioning

confidence: 99%

Arabic Rumor Detection Using Contextual Deep Bidirectional Language Modeling

et al. 2022

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In today's world, news outlets have changed dramatically; newspapers are obsolete, and radio is no longer in the picture. People look for news online and on social media, such as Twitter and Facebook. Social media contributors share information and trending stories before verifying their truthfulness, thus, spreading rumors. Early identification of rumors from social media has attracted many researchers. However, a relatively smaller number of studies focused on other languages, such as Arabic. In this study, an Arabic rumor detection model is proposed. The model was built using transformer-based deep learning architecture. According to the literature, transformers are neural networks with outstanding performance in natural language processing tasks. Two transformers-based models, AraBERT and MARBERT, were employed, tested, and evaluated using three recently developed Arabic datasets. These models are extensions to the BERT, Bidirectional Encoder Representations from Transformers, a deep learning model that uses transformer architecture to learn the text representations and leverages the attention mechanism. We have also mitigated the challenges introduced by the imbalanced training datasets by employing two sampling techniques. The experimental results of our proposed approaches achieved 0.97 accuracy. This result demonstrated the effectiveness of the proposed method and outperformed other existing Arabic rumor detection methods.

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“…Several studies addressed ATS for English documents [3,4]; however, fewer studies are available for the Arabic language due to its rich morphological structure, the range of dialects, and the scarcity of data and tools [5]. Arabic is the fifth most spoken language globally, with more than 400 million speakers worldwide [6]. The summarization of Arabic text still suffers from low performance and fewer research studies [7].…”

Section: Introductionmentioning

confidence: 99%

Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification

2022

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The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, BLEU and ROUGE. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research.

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Arabic rumor detection: A comparative study

Cited by 15 publications

References 28 publications

Multimodal Arabic Rumors Detection

Multimodal Arabic Rumors Detection

Arabic Rumor Detection Using Contextual Deep Bidirectional Language Modeling

Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification

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