Jebran Khan¹, Kashif Ahmad², Kyung-Ah Sohn^1,3,*

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 >

Chengfei Hua^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3

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 >

Asif Khan¹, Huaping Zhang^1,*, 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 >

Farah Mohammad^1,*, Saad Al-Ahmadi², Jalal Al-Muhtadi^1,2

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 >

Wadhah Mohammed M. Aqlan¹, Ghassan Ahmed Ali^2,*, Khairan Rajab², Adel Rajab², Asadullah Shaikh², Fekry Olayah², Shehab Abdulhabib Saeed Alzaeemi^3,*, 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 >

Baoqiang Zhang^1,2, Ling Xiang^3,4,*

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 >

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 >

Khalid M. O. Nahar^1,*, Mohammad Alauthman², Saud Yonbawi³, Ammar Almomani^4,5

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 >

Manar Ahmed Hamza^1,*, 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 >

Manar Ahmed Hamza^1,*, 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 >