Page 702 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Automated Feature Extraction detection. Their research highlighted the challenges
Continuous Learning and Adaptation associated with bias and generalizability in existing models,
Community Engagement and Feedback Loop emphasizing that while LLMs can provide powerful tools for
Ethical Considerations misinformation detection, careful consideration must be
given to their training data and deployment contexts [2].
By targeting these objectives, the proposed device aims not
only to improve fake news detection but also to contribute This study serves as a reminder that no single solution can
positively to public discourse by fostering a more informed address the complexities of fake news detection
society capable of discerning credible information from comprehensively.
misinformation. As fake news continues to pose significant The application of Graph Neural Networks (GNNs) has
challenges across various domains—politics, health, and emerged as a promising avenue for enhancing fake news
social issues—the development of effective detection detection capabilities. Pilkevych et al. (2024) conducted a
systems becomes increasingly critical for safeguarding truth thorough analysis using GNNs for online media monitoring to
and integrity in information dissemination. identify and evaluate fake news quickly. Their method
utilized knowledge graphs to map relationships and
II. RELATED WORK
The challenge of fake news detection has prompted recognize entities within textual information, focusing on
identifying indicators of harmful psychological influence.
extensive research across various domains, leading to the
development of numerous methodologies and models. This Among the models tested, GraphSAGE achieved remarkable
accuracy scores, demonstrating the potential of GNNs in
section reviews significant contributions in the field, improving the precision and efficiency of misinformation
highlighting advancements in machine learning, deep
learning, and natural language processing (NLP) techniques detection systems [1].
aimed at improving the accuracy and effectiveness of fake In response to the urgent need for timely misinformation
news detection systems. identification, several studies have focused on real-time fake
news detection systems. Cavus et al. (2024) developed a
Recent studies have explored ensemble learning techniques system called FANDC based on cloud computing to handle
to enhance the performance of fake news detection models. fake news detection in online social networks effectively.
Almandouh et al. (2024) investigated a hybrid model Their approach emphasizes scalability and real-time
combining Bidirectional Gated Recurrent Units (Bi-GRU) and processing capabilities, addressing a critical gap in existing
Bidirectional Long Short-Term Memory (Bi-LSTM) networks literature regarding immediate response to misinformation
for Arabic fake news detection. Their findings demonstrated dissemination [4].
that this ensemble approach achieved impressive
performance metrics, including an F1 score of 0.98 and Similarly, Kundu et al. (2024) categorized false information
accuracy rates of 0.98 on the AFND dataset. The study on social media through traditional text categorization
emphasizes the importance of hybrid models in addressing approaches, contributing to advancements in fake news
language-specific challenges in misinformation detection and detection technologies that prioritize real-time analysis [5].
sets a foundation for future research in multilingual contexts These efforts reflect an increasing recognition of the
[1]. necessity for rapid response mechanisms in combating
misinformation.
Additionally, Verma et al. (2024) introduced a novel two-
phase benchmark model called WELFake, which integrates Comparative evaluations of different models have also been
word embedding with linguistic features for fake news instrumental in advancing the field of fake news detection.
detection. This model achieved a peak accuracy of 96.73%, Recent studies have assessed BERT-like models against other
surpassing traditional methods such as BERT and CNN by up architectures to determine their effectiveness in various
to 4.25%. Their approach highlights the effectiveness of contexts. For instance, research comparing traditional
combining advanced embedding techniques with linguistic machine learning classifiers with deep learning models has
analysis to improve detection reliability [1]. provided insights into their relative strengths and
weaknesses in detecting misinformation across diverse
The advent of transformer-based architectures has
datasets [3]. These evaluations are crucial for guiding future
significantly impacted the field of fake news detection. Shu et research directions and informing practitioners about
al. (2024) proposed a methodology utilizing document optimal approaches for specific applications.
embeddings to classify news articles as trustworthy or fake
across multiple datasets. Their evaluation included various III. PROPOSED WORK
machine learning models, including Naïve Bayes, gradient The proposed work focuses on developing an advanced
boosting, and deep learning architectures like LSTM and system for real-time fake news detection, leveraging the
GRU, as well as transformer-based models such as BERT and latest advancements in machine learning and natural
RoBERTa. This comprehensive approach underscores the language processing. This system aims to address the
versatility of transformer models in capturing complex growing challenge of misinformation in the digital age,
patterns within textual data [1]. particularly on social media platforms were news spreads
rapidly. Below are the key components and objectives of the
Furthermore, Ying et al. (2024) explored the potential of
large language models (LLMs) for automating fake news proposed work, informed by recent research findings.

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