# Are Strong Baselines Enough? False News Detection with Machine Learning
## 2 Materials and Methods
### Previous Research
#### 2.1.1 False News Detection  
[PERSON] (2013) studied satirical news and how to expose it as false news. The contrast between satirical news and false news is worth noting. Satirical news gives text cues to reveal the false nature of the news, whereas false news tries to convince the reader to believe in it. In their research, [PERSON] (2013) proposed an algorithm based on support vector machines (SVM) with five general features: absurdity, humor, grammar, negative affect, and punctuation to predict satirical news. Their research was very successful, as they achieved a 90% precision and 84% recall in satirical news detection. [PERSON]'s [PERSON] (2013) study effectively uses SVM and important linguistic traits to predict satirical news, achieving \begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Description** \\ \hline False news & False or misleading information presented as news, often called “fake news” as well ([PERSON] et al., 2016). This paper uses the term false news, as it has a less polarizing connotation. \\ \hline Machine learning (ML) & The use and development of computer systems that can learn and adapt by using algorithms and statistical data to analyze patterns from the given data ([PERSON] et al., 2016) \\ \hline Artificial intelligence (AI) & The ability of computers to perform tasks that are usually more associated with intelligent beings ([PERSON] et al., 2016) \\ \hline Large language models (LLMs) & An example of generative AI. They can recognize, translate, predict, or generate texts or other forms of content ([PERSON] et al., 2016). A good example of LLMs would be ChatGPT. \\ \hline Deep learning & A subset of ML, is a neural network with three or more layers. The neural networks attempt to simulate the behavior of the human brain ([PERSON] et al., 2016) \\ \hline Natural language processing (NLP) & The application of computational techniques to analyze and synthesize natural language and speech ([PERSON] et al., 2016) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Glossary of different terms used in the research. high precision and recall scores. However, the algorithm's reliance on predetermined cues like absurdity might hinder the algorithm's adaptability to more complex and evolving forms of sati  
Figure 2: A flowchart of the research methodology. [PERSON] [11] used deep learning architectures to detect false news. They highlighted that a problem with the majority of false news detectors is that they only use binary classification methods, making them unable to understand the relationship between two pieces of text. In their research, they tackle this problem through stance detection, in which they use deep neural network architecture to predict how similar the headline is to a news article. Their model proved to be successful, as they were able to detect when a news article was false with stance detection with 94.21% accuracy, which outperformed the existing models at the time by 2.5%. While [PERSON]'s [11] research addresses the limitations of binary classification in false news detection, their proposed method's focus on headline-article similarity may miss other important aspects of misinformation. [PERSON] [24] conducted their research on false news detection with the inclusion of various degrees of \"falseness\". They propose a multi-source multi-class fake news detection framework to tackle this problem. This framework combines automated feature extraction, multi-source fusion, and automated degrees of falseness detection into one single model. Their model could differentiate between the different degrees of falseness from the news that they used. They also integrated multiple sources into false news detection, which could help false news detection as multiple sources give a much better context when detecting false news, as opposed to only using the context given by the news article. However, the model's complexity may present challenges for scalability. [PERSON] [25] studied the potentials and limitations of NLP solutions in false news detection.