Emotion-Aware Fake News Detection on Social Media with BERT Embeddings DOI

Mohammed Al-Alshaqi,

Danda B. Rawat, Chunmei Liu

et al.

Published: Nov. 24, 2023

Fake news dissemination on social media poses a significant threat to the integrity of information and public discourse. This research proposes an emotion-aware fake detection model using BERT embeddings. Leveraging power BERT, our captures contextual relations in text, enabling accurate classification news. Through experimentation with different models, "bert-large-cased" emerges as top-performing variant, achieving remarkable training accuracy 98% F1 score 0.77. Integrating features enhances model's efficacy identifying while minimizing false positives negatives. Our study contributes field detection, offering potent tool for safeguarding from disinformation.

Language: Английский

Fake news detection: state-of-the-art review and advances with attention to Arabic language aspects DOI Creative Commons
Eman Btoush, Keng Hoon Gan,

Saif A. Ahmad Alrababa

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2693 - e2693

Published: March 11, 2025

The proliferation of fake news has become a significant threat, influencing individuals, institutions, and societies at large. This issue been exacerbated by the pervasive integration social media into daily life, directly shaping opinions, trends, even economies nations. Social platforms have struggled to mitigate effects news, relying primarily on traditional methods based human expertise knowledge. Consequently, machine learning (ML) deep (DL) techniques now play critical role in distinguishing necessitating their extensive deployment counter rapid spread misinformation across all languages, particularly Arabic. Detecting Arabic presents unique challenges, including complex grammar, diverse dialects, scarcity annotated datasets, along with lack research field detection compared English. study provides comprehensive review examining its types, domains, characteristics, life cycle, approaches. It further explores recent advancements leveraging ML, DL, transformer-based for detection, special attention delves Arabic-specific pre-processing techniques, methodologies tailored language, datasets employed these studies. Additionally, it outlines future directions aimed developing more effective robust strategies address challenge content.

Language: Английский

Citations

0
Arabic Fake News Detection Using Deep Learning DOI Creative Commons
Nermin Abdelhakim Othman, Doaa S. Elzanfaly, Mostafa Mahmoud M. Elhawary

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 122363 - 122376

Published: Jan. 1, 2024

Language: Английский

Citations

3
Software Subclassification Based on BERTopic-BERT-BiLSTM Model DOI Open Access

Wenjuan Bu,

Hui Shu,

Fei Kang

et al.

Electronics, Journal Year: 2023, Volume and Issue: 12(18), P. 3798 - 3798

Published: Sept. 8, 2023

With the continuous influx of application software onto market, achieving accurate recommendations for users in huge market is urgent. To address this issue, each currently provides its own classification tags. However, several problems still exist, such as lack objectivity, hierarchy, and standardization these classifications, which turn affects accuracy precise recommendations. Accordingly, a customized BERTopic model proposed to cluster description texts automatic tagging updating tags are realized according clusters obtained by topic clustering extracted subject words. At same time, data enhancement method based on c-TF-IDF algorithm solve problem imbalance datasets, then BERT-BiLSTM trained labeled datasets classify dimension function, so realize recommendation users. Based experimental verification two 21 categories SourceForge dataset 19 Chinese App Store subclassed results model, 138 subclasses 262 formed, respectively. In addition, complete tagged text constructed updated automatically. first stage experiment, weighted average accuracy, recall rate, F1 value can reach 0.92, 0.91, second stage, all 0.96. After enhancement, be increased up percentage points.

Language: Английский

Citations

6
A Review of Fake News Detection Techniques for Arabic Language DOI Open Access
Taghreed Alotaibi, Hmood Al-Dossari

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 1, 2024

The growing proliferation of social networks provides users worldwide access to vast amounts information. However, although media have benefitted significantly from the rise various platforms in terms interacting with others, e.g., expressing their opinions, finding products and services, checking reviews, it has also raised critical problems, such as spread fake news. Spreading news not only affects individual citizens but governments countries. This situation necessitates immediate integration artificial intelligence methodologies address alleviate this issue effectively. Researchers field leveraged different techniques mitigate problem. research Arabic language for detection is still its early stages compared other languages, English. review paper intends provide a clear view field. In addition, aims researchers working on solving problems better understanding common features used extraction, machine learning, deep learning algorithms. Moreover, list publicly available datasets provided give an idea characteristics facilitate researcher access. Furthermore, some limitations challenges related rumor are discussed encourage researchers.

Language: Английский

Citations

0
Transformer-based models for combating rumours on microblogging platforms: a review DOI Creative Commons
Rini Anggrainingsih, Ghulam Mubashar Hassan, Amitava Datta

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)

Published: July 20, 2024

Abstract The remarkable success of Transformer-based embeddings in natural language tasks has sparked interest among researchers applying them to classify rumours on social media, particularly microblogging platforms. Unlike traditional word embedding methods, Transformers excel at capturing a word’s contextual meaning by considering words from both the left and right word, resulting superior text representations ideal for like rumour detection This survey aims provide thorough well-organized overview analysis existing research implementing models scope this study is offer comprehensive understanding topic systematically examining organizing literature. We start discussing fundamental reasons significance automating Emphasizing critical role converting textual data into numerical representations, we review current approaches implement Transformer Furthermore, present novel taxonomy that covers wide array techniques employed deployment identifying misinformation Additionally, highlight challenges associated with field propose potential avenues future research. Drawing insights surveyed articles, anticipate promising results will continue emerge as outlined are addressed. hope our efforts stimulate further harnessing capabilities combat spread

Language: Английский

Citations

0
Detection of Arabic and Algerian Fake News DOI Creative Commons
Khaoula Hamadouche, Kheira Zineb Bousmaha,

Mohamed Yasine Bahi Amar

et al.

Applied Computer Systems, Journal Year: 2024, Volume and Issue: 29(2), P. 14 - 21

Published: Dec. 1, 2024

Abstract In an era characterised by the rapid dissemination of information through digital platforms, proliferation fake news has emerged as a pressing global concern. Misinformation, deliberately fabricated or misleading content presented factual news, poses significant threats to public discourse, trust, and decision-making processes. The research highlights significance detection in Arabic language, with specific focus on Algerian dialect. language exhibits great diversity complexity, making false information, all more crucial. spread social media platforms impact individuals society whole. To address this challenge, paper presents TruthGuardian, innovative solution that combines machine learning deep techniques voting system for last decision. This enables fast accurate identification emphasis It provides reliable effective results countering misinformation.

Language: Английский

Citations

0
Emotion-Aware Fake News Detection on Social Media with BERT Embeddings DOI

Mohammed Al-Alshaqi,

Danda B. Rawat, Chunmei Liu

et al.

Published: Nov. 24, 2023

Fake news dissemination on social media poses a significant threat to the integrity of information and public discourse. This research proposes an emotion-aware fake detection model using BERT embeddings. Leveraging power BERT, our captures contextual relations in text, enabling accurate classification news. Through experimentation with different models, "bert-large-cased" emerges as top-performing variant, achieving remarkable training accuracy 98% F1 score 0.77. Integrating features enhances model's efficacy identifying while minimizing false positives negatives. Our study contributes field detection, offering potent tool for safeguarding from disinformation.

Language: Английский

Citations

1