# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 3 Methodology
### Enhancing Naive Bayes Models with Transfer Learning and Incremental Learning
#### 3.6.2 Incremental Learning Overview  
Incremental learning, also referred to as online learning or out-of-core learning, is a machine learning paradigm that progressively learns from data over time, updating the model with each new piece of data. This approach is particularly effective when dealing with large datasets that cannot be loaded into memory simultaneously [24]. Additionally, it proves advantageous in scenarios involving data streams where data is continuously generated [25].  
Incremental learning differs from batch learning, where the entire dataset trains the model simultaneously [26]. Instead, with incremental learning, the model can be trained and updated as new data arrives, making it more adaptable to changes in data distribution over time, a phenomenon known as concept drift.  
In this research, both naive Bayes models utilized for fake news detection support incremental learning through the partial_fit function. This function allows the model to update its parameters on the fly as new labeled data becomes available, providing a real-time response to the evolving landscape of news dissemination [27].  
Advantages of Incremental Learning: The following highlight the key advantages of incremental learning:  
* Efficient Large-Data Handling: Incremental learning excels at processing vast datasets that exceed memory capacity constraints [28]. Learning iteratively from data in discrete segments effectively addresses challenges posed by large-scale data [24].
* Dynamic Adaptability: A primary advantage of incremental learning is its ability to adapt to changing data patterns over time [20]. This dynamic adaptability becomes particularly crucial when data distribution exhibits temporal variations, ensuring sustained model performance through continuous updates [29].
* Real-time Learning Capability: Incremental learning embodies the capacity to learn instantaneously from newly arriving data [20]. This real-time learning attribute is indispensable when models need to assimilate and respond to fresh information rapidly [25].  
Limitations of Incremental Learning: Below, we discuss some of the limitations of incremental learning:  
* Risk of Forgetting: A notable challenge in incremental learning is the risk of obliterating previously learned information [28]. This phenomenon may occur when newly introduced data significantly differs from or contradicts past data.
* The Stability-Plasticity Dilemma: Achieving a balance between integrating new information (plasticity) and preserving prior knowledge (stability) is critical in incremental learning [29]. Overemphasis on adapting to new data could lead to overfitting recent trends, while underemphasis may render the model inert to novel changes.
* Sensitivity to Noise: Incremental learning, by updating model parameters with every incoming data point, might exhibit heightened sensitivity to noisy or aberrant data points [30]. This sensitivity could potentially undermine model performance by overadjusting to the noise.  
In essence, incremental learning is a delicate balance between accommodating new data while maintaining the integrity of previously acquired knowledge. Each new data point presents an opportunity for learning, but is also a risk of disruption. Therefore, successful incremental learning necessitates careful management of these conflicting pressures [25].  
In this work, transfer learning and incremental learning will be implemented by saving a pre-trained model into a pickle file alongside the vectorization outputs, and then reloading these files for fine-tuning the model using the partial_fit function provided by scikit-learn.