# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 1 Introduction  
In recent years, the proliferation of fake news facilitated by the internet and social media has emerged as a pressing societal concern, exerting influence on public opinion, political decisions, and even national security [1; 2]. The escalating volume of online content underscores the urgent need for effective automated fake news detection, where machine learning plays a pivotal role. Despite the availability of numerous machine learning libraries and tools, selecting the optimal algorithm and fine-tuning parameters remains daunting, requiring extensive experimentation, evaluation, and a nuanced understanding of algorithmic principles [3]. Additionally, tasks such as dataset creation, preprocessing, and building machine learning classifiers to discern between fake and real news entail their own challenges and complexities. While several studies analyze different facets of the fake news ecosystem, our work focuses on two key aspects: the efficacy of preprocessing techniques and the evaluation of machine learning algorithms.  
The investigation and analysis of the existing literature related to fake news detection using machine learning (ML) models reveal several areas of uncharted research territories [4; 5; 6; 7]. The first major gap in the current research corpus pertains to comparative analysis studies between different ML approaches, specifically those using term frequency-inverse document frequency (TF-IDF), Empath, and a hybrid model that combines the two [4; 5]. In addition to comparing different ML models, there is a gap in exploring transfer learning applications with the Bernoulli naive Bayes (BNB) classifier in the context of fake news detection [6; 7]. Lastly, applying incremental learning with the multinomial naive Baye(MNB) and BNB classifiers for fake news detection has not received adequate scholarly attention [6; 7].  
Hence, our research contributes novel insights in three main areas: First, we evaluate and compare the accuracy and performance of various machine learning algorithms. Second, we propose a novel hybrid word embedding technique that enhances news label prediction by integrating lexical, linguistic, and morphological features with vectorized text. Finally, we develop a web framework that facilitates a systematic and replicable approach for text classification, emphasizing dataset alignment and balance and the utility of transfer and incremental learning techniques.  
To guide this research and ensure its focus and relevance, several research questions have been identified, each addressing a crucial aspect of the broader objective:  
* _What is the comparative effectiveness of different machine learning models and feature extraction methods in detecting fake news?_
* _What is the potential of transfer and incremental learning in improving the efficiency of fake news detection?_  
These research questions aim to address various technical issues related to using machine learning for fake news detection.  
Our novel hybrid approach, which combines TF-IDF word vectorization and feature extraction, surpassed benchmarks by 4% (\(\pm\)0.5%), offering a potential state-of-the-art solution for future research in fake news detection. This study's contributions lie in its multi-dataset approach, comprehensive performance evaluations, and open-source framework designed for continuous updates in the detection of misinformation.  
The remainder of this paper is structured as follows: Section 2 reviews the related literature, Section 3 presents our methodology, Section 4 details the implementation, Section 5 discusses experimental results, and Section 6 offers concluding remarks.