Rumor detection in Arabic tweets using semi-supervised and unsupervised expectation–maximization

Alzanin, Samah M.; Azmi, Aqil M.

doi:10.1016/j.knosys.2019.104945

Cited by 58 publications

(48 citation statements)

References 19 publications

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“…Our study used deep learning-based NLP techniques with RoBERTa (Liu et al 2019), a pre-trained language model developed by Facebook to analyze Twitter APIs. This strategy was pursued after our study found from previous literature that many studies utilize sentiment analysis and machine learning techniques such as supervised or unsupervised learning for the understanding of emotions, but they are usually based on certain contexts and sentiment analysis; e.g., Arabic tweets were analyzed with sentiment analysis to identify rumors (Alzanin & Azmi, 2019). In our study, the novelty was offered by using multiple emotion identification for several months and numerous countries that analyzed the impact of an unexpected exogenous shock event on several countries.…”

Section: Discussion Of This Studymentioning

confidence: 99%

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

et al. 2021

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The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.

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Section: Discussion Of This Studymentioning

confidence: 99%

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

et al. 2021

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“…Some other features proposed by previous studies can be also classified into the user-based category. For instance, Alzanin and Azimi [ 1 ] put forward a feature named “user effect” to measure whether a given user is a broadcaster or a receive. In addition, there was a widespread misunderstanding in previous studies that only users with spammer features will mostly post rumors, e.g., the theory suggested by Sunstein [ 31 ] that ‘ rumor spreaders are persons who want to get attention and popularity ’.…”

Section: (1) User-based Featuresmentioning

confidence: 99%

“…Therefore, there is an urgent need for a method that can classify rumors in an accurate and timely manner. In general, there are three relevant aspects in this regard: spammer detection [ 25 , 40 ], rumor source detection [ 28 , 44 ], and rumor detection [ 1 , 5 , 21 , 34 ]. Related existing studies offered some valuable findings.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Luo et al [ 21 ] proposed a novel post-based augmentation representation for rumor detection, which achieved a high accuracy rate of 80.09% in empirical test. Alzanin and Azmi [ 1 ] put forward a semi-supervised system for detecting rumors in Arabic language social networks, which achieved an F1 score of 80% in the experiment. Existing rumor detection models are frequently established based on a wide range of features corresponding to account information, content of posts, and some other aspects [ 7 , 16 , 30 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

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A two-step rumor detection model based on the supernetwork theory about Weibo

et al. 2021

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Based on the supernetwork theory, a two-step rumor detection model was proposed. The first step was the classification of users on the basis of user-based features. In the second step, non-user-based features, including psychology-based features, content-based features, and parts of supernetwork-based features, were used to detect rumors posted by different types of users. Four machine learning methods, namely, Naive Bayes, Neural Network, Support Vector Machine, and Logistic Regression, were applied to train the classifier. Four real cases and several assessment metrics were employed to verify the effectiveness of the proposed model. Performance of the model regarding early rumor detection was also evaluated by separating the datasets according to the posting time of posts. Results showed that this model exhibited better performance in rumor detection compared to five benchmark models, mainly owing to the application of the supernetwork theory and the two-step mechanism.

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“…Today, in a highly volatile political setting, social media networks continue to play the same critical role in the Arab world. Researchers also utilize this social media data to extract information for various objectives such as opinion mining for the Arabic language [4], event detection [5], [6], and rumor detection [7].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Two Malicious Arabic-Language Twitter Campaigns

Alharthi¹,

Alhothali²,

Moria³

2021

IJACSA

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Fake malicious accounts are one of the primary causes of the deterioration of social network content quality. Numerous such accounts are generated by attackers to achieve multiple nefarious goals, including phishing, spamming, spoofing, and promotion. These practices pose significant challenges regarding the availability of credible data that reflect realworld social media interactions. This has led to the development of various methods and approaches to combat spammers on social media networks. Previous studies, however, have almost exclusively focused on studying and identifying English-language spam profiles, whereas the problem of malicious Arabic-language accounts remains under-addressed in the literature. In this paper, therefore, we conduct a comprehensive investigation of malicious Arabic-language campaigns on Twitter. The study involves analyzing the accounts of these campaigns from several perspectives, including their number, content, social interaction graphs, lifespans, and day-to-day activities. In addition to exposing their spamming tactics, we find that these spam accounts are more successful in avoiding Twitter suspensions that has been previously reported in the literature.

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Rumor detection in Arabic tweets using semi-supervised and unsupervised expectation–maximization

Cited by 58 publications

References 19 publications

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

A two-step rumor detection model based on the supernetwork theory about Weibo

Comprehensive Analysis of Two Malicious Arabic-Language Twitter Campaigns

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