Page 711 - 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
improving the precision and efficiency of misinformation system called FANDC based on cloud computing to handle
detection systems [1]. fake news detection in online social networks effectively.
Their approach emphasizes scalability and real-time
In response to the urgent need for timely misinformation
processing capabilities, addressing a critical gap in existing
identification, several studies have focused on real-time fake
literature regarding immediate response to misinformation
news detection systems. Cavus et al. (2024) developed a
dissemination [4].

By targeting these objectives, the proposed device aims not only to improve fake news detection but also to contribute
positively to public discourse by fostering a more informed society capable of discerning credible information from
misinformation. As fake news continues to pose significant challenges across various domains—politics, health, and social
issues—the development of effective detection systems becomes increasingly critical for safeguarding truth and integrity in
information dissemination.
PROPOSED WORK
1. Comprehensive Dataset Creation:
 In addition to collecting existing datasets, the proposed work includes the creation of a new, annotated dataset specifically
for Arabic fake news. This dataset will encompass a wide range of topics and sources to ensure diversity and
representativeness, facilitating better generalization of the models.
2. Advanced Preprocessing Techniques:
 Beyond basic text normalization and tokenization, the work will incorporate advanced preprocessing techniques such as
stemming and lemmatization tailored for the Arabic language. Additionally, handling dialectal variations and slang in
Arabic will be a focus to improve the model's robustness.
3. Exploratory Data Analysis (EDA):
 Conducting thorough EDA to understand the characteristics of the datasets, including word frequency distributions,
common phrases in fake versus real news, and sentiment analysis. This step will help inform feature selection and model
design.
4. Feature Engineering:
 Implementing various feature engineering strategies, such as:
 N-grams: Extracting n-grams (bigrams, trigrams) to capture context.
 Sentiment Features: Analyzing sentiment polarity to identify emotionally charged language often used in fake news.
 Source Credibility Features: Incorporating features related to the credibility of sources, such as historical accuracy and
reputation.
5. Model Selection and Optimization:
 Evaluating a wide range of machine learning algorithms (e.g., Random Forest, SVM, Naïve Bayes) alongside deep learning
architectures (e.g., CNNs, RNNs, LSTMs). Hyperparameter tuning will be conducted using techniques like grid search or
random search to optimize model performance.
6. Ensemble Learning Approaches:
 Exploring ensemble methods that combine multiple classifiers to improve detection accuracy. Techniques such as bagging
and boosting will be examined to leverage the strengths of different models.
7. Real-time Detection System:
 Developing a prototype for a real-time fake news detection system that can be integrated into social media platforms or
news aggregators. This system will utilize the trained models to provide instant feedback on news articles shared online.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 701