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  • Open Access

    ARTICLE

    An Efficient Character-Level Adversarial Attack Inspired by Textual Variations in Online Social Media Platforms

    Jebran Khan1, Kashif Ahmad2, Kyung-Ah Sohn1,3,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159

    Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language processing (NLP) applications. However, these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication. These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form. The intuition of the proposed scheme is to generate adversarial examples… More >

  • Open Access

    ARTICLE

    DFE-GCN: Dual Feature Enhanced Graph Convolutional Network for Controversy Detection

    Chengfei Hua1,2,3, Wenzhong Yang2,3,*, Liejun Wang2,3, Fuyuan Wei2,3, KeZiErBieKe HaiLaTi2,3, Yuanyuan Liao2,3

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 893-909, 2023, DOI:10.32604/cmc.2023.040862

    Abstract With the development of social media and the prevalence of mobile devices, an increasing number of people tend to use social media platforms to express their opinions and attitudes, leading to many online controversies. These online controversies can severely threaten social stability, making automatic detection of controversies particularly necessary. Most controversy detection methods currently focus on mining features from text semantics and propagation structures. However, these methods have two drawbacks: 1) limited ability to capture structural features and failure to learn deeper structural features, and 2) neglecting the influence of topic information and ineffective utilization of topic features. In light… More >

  • Open Access

    ARTICLE

    Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

    Asif Khan1, Huaping Zhang1,*, Nada Boudjellal2, Arshad Ahmad3, Maqbool Khan3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3345-3361, 2023, DOI:10.32604/cmc.2023.041520

    Abstract Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of election-related tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets pose unique challenges for sentiment… More >

  • Open Access

    ARTICLE

    Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1423-1438, 2023, DOI:10.32604/cmc.2023.035741

    Abstract Social media forums have emerged as the most popular form of communication in the modern technology era, allowing people to discuss and express their opinions. This increases the amount of material being shared on social media sites. There is a wealth of information about the threat that may be found in such open data sources. The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information. Despite the fact that several models for detecting cybersecurity events have been presented, it remains challenging to extract security… More >

  • Open Access

    ARTICLE

    Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

    Wadhah Mohammed M. Aqlan1, Ghassan Ahmed Ali2,*, Khairan Rajab2, Adel Rajab2, Asadullah Shaikh2, Fekry Olayah2, Shehab Abdulhabib Saeed Alzaeemi3,*, Kim Gaik Tay3, Mohd Adib Omar1, Ernest Mangantig4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 665-686, 2023, DOI:10.32604/cmc.2023.039228

    Abstract Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production, resulting in a drop in the size of red blood cells. In severe forms, it can lead to death. This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival. Therefore, controlling thalassemia is extremely important and is made by promoting screening to the general population, particularly among thalassemia carriers. Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like… More >

  • Open Access

    ARTICLE

    Investigating the Cognitive Control of Social Media-Anxious Users Using a Psychological Experimental Approach

    Baoqiang Zhang1,2, Ling Xiang3,4,*

    International Journal of Mental Health Promotion, Vol.25, No.7, pp. 863-871, 2023, DOI:10.32604/ijmhp.2023.027303

    Abstract Social media has become increasingly popular and is now a significant tool for daily communication for many people. The use of social media can cause anxiety and have detrimental impacts on mental health. Cognitive impairment is more likely to affect individuals with anxiety. Investigating the cognitive abilities and mental health of social media users requires the development of new methodologies. This study employed the AX-Continuous Performance Test (AX-CPT) paradigm and the Stroop paradigm to study the cognitive control characteristics of trait anxiety, drawing on psychological experimental methods. Previous studies on whether trait anxiety impairs cognitive control remain controversial, possibly because… More >

  • Open Access

    ARTICLE

    Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images

    Jieyu An*, Wan Mohd Nazmee Wan Zainon, Zhang Hao

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5801-5815, 2023, DOI:10.32604/cmc.2023.038220

    Abstract Targeted multimodal sentiment classification (TMSC) aims to identify the sentiment polarity of a target mentioned in a multimodal post. The majority of current studies on this task focus on mapping the image and the text to a high-dimensional space in order to obtain and fuse implicit representations, ignoring the rich semantic information contained in the images and not taking into account the contribution of the visual modality in the multimodal fusion representation, which can potentially influence the results of TMSC tasks. This paper proposes a general model for Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images (ITMSC) as… More >

  • Open Access

    ARTICLE

    Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

    Khalid M. O. Nahar1,*, Mohammad Alauthman2, Saud Yonbawi3, Ammar Almomani4,5

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5307-5319, 2023, DOI:10.32604/cmc.2023.031848

    Abstract Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as latent Dirichlet allocation. In addition,… More >

  • Open Access

    ARTICLE

    Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Khaled Tarmissi3, Ayman Yafoz4, Amal S. Mehanna5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohamed I. Eldesouki6

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3303-3319, 2023, DOI:10.32604/csse.2023.034823

    Abstract The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and… More >

  • Open Access

    ARTICLE

    Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Abdulkhaleq Q. A. Hassan3, Abdulbaset Gaddah4, Nasser Allheeib5, Suleiman Ali Alsaif6, Badriyya B. Al-onazi7, Heba Mohsen8

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4137-4154, 2023, DOI:10.32604/cmc.2023.035237

    Abstract Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions. The number of social media users has been increasing over the last few years, which have allured researchers’ interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a better way. Irony and sarcasm detection is a complex task in Natural Language Processing (NLP). Irony detection has inferences in advertising, sentiment analysis (SA), and opinion mining. For the last few years, irony-aware SA has gained significant computational… More >

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