# Are Strong Baselines Enough? False News Detection with Machine Learning
## 2 Materials and Methods
### Previous Research
#### 2.1.1 False News Detection
NLP techniques are perhaps the most common way of analyzing false news, and their study proves that NLP techniques are useful in automatic false news detection. [PERSON] [26] additionally studied the task of automatic fact-checking with NLP techniques. However, their research points out more of the fact that automated fact-checking is less reliable when compared to manual fact-checking. [PERSON] et al. (2023)'s solution for this limitation is to develop a hybrid system for automatic fact-checking that would use humans in the process alongside computers. However, in the long run, especially when we consider the possibility of employing large language models to generate false news, using humans in the detection process, although likely still achieving some decent results, would most likely fail to perform as effectively as is required. [PERSON] [10] used ensemble machine learning methods, such as XGBoost, bagging, random forest (RF), extra trees, and gradient boost. The methods they used allowed them to achieve relatively high accuracy scores in classification when using the LIAR dataset [4]. [PERSON] [27] also used ensemble methods with machine learning in their research about false news detection. They also achieved very good results with ensemble methods. [PERSON] [10] and [PERSON]'s [27] research and results conclude that machine learning algorithms work well for false news detection when implemented with ensemble learners. However, [PERSON] [10] and [PERSON]'s [27] studies might overemphasize the general effectiveness of ensemble learners while ignoring potential drawbacks, such as higher processing requirements, the risk of over fitting, and the challenge of sustaining performance over more diverse datasets. [PERSON] [12] researched false news detection for COVID-19-related news. They used classic ML models, deep learning models, and transformer models to conduct their research. They achieved the best results when they developed an ensemble model consisting of all three different transformer models that they used (BERT, ALBERT, and XLNET). With this ensemble model, they were able to receive an accuracy score of 98%. However, the usage of one specific dataset may have contributed to the model's high accuracy, potentially limiting its usefulness in broader scenarios. [PERSON] [28] conducted their research on multimodal (text and image) false news detection. They proposed a multimodal co-attention network-based model to better include text and images together for false news detection. Their model first extracted visual features from images and then textual features from the text, fusing these extracted features that then can be used to detect false news. Their model was able to achieve good accuracy results on the two datasets that they used for their research. On the first dataset that they used, they achieved an accuracy score of 80%, and on the second, they achieved an accuracy score of 89%. [PERSON] [29] recently concluded research on utilizing visual features for false news detection as well. They proposed a multimodal extreme fake news detection (EFND) that gathers context, social context, and visual data to create a multimodal vector. The results they achieved were high, with the accuracy score being 98% and 99% on different datasets. [PERSON]'s [28] model which combined text and images achieved good results, but [PERSON]'s [29] model, which included additional social and contextual data, achieved higher accuracy results. This indicates that multimodal approaches--while still efficient--benefit from additional context to combat their complexity that can impact their ability to generalize and scale effectively.