# Are Strong Baselines Enough? False News Detection with Machine Learning
## 4 Discussion  
The results achieved from these experiments are very promising. This paper was able to conclude that linear models and ensemble methods are the most efficient for automatically detecting false news. The three most efficient methods in this research are passive aggressive classifiers, support vector machines, and random forests. These methods not only achieved good accuracy results in many of the experiments but also maintained a stable performance in terms of macro averages of precision, recall, F1-scores, and AUC scores. The relatively good AUC scores indicate that these classifiers have a strong ability to distinguish between false and true news, showing their overall robustness. However, as observed from a lot of the experiments, the RF method struggles to produce good FPR and FNR results. This paper aimed to compare different types of false news contents and how they differ from each other. In several experiments, classifiers were trained and tested using two different datasets. In all experiments where different datasets were used for training and testing, the accuracy results dropped significantly compared to when the same dataset was used for both training and testing. However, this paper also concluded that the results heavily depend on the specific dataset that was used. For example, in Section 3.3, training using the GossipCop dataset and testing using the PolitiFact dataset were almost 10% higher on all the methods when comparing the results achieved with training using the PolitiFact dataset and testing using the GossipCop dataset. These differences in results could be due to factors such as the size difference between the datasets or the linguistic characteristics of each dataset that make it easier to detect false news in one compared to the other. This is further supported by the observed FPR and FNR scores, which show that the classifiers trained on larger datasets tend to produce fewer false positives and false negatives when tested on smaller datasets, contributing to higher accuracy and AUC scores. This paper also conducted experiments on whether labeling false news according to different degrees of truthfulness would affect the accuracy of the results. In the Section 3.2, where the LIAR dataset [4] was divided into six different labels based on the degree of falsity or truth, it was found that using multiple labels significantly decreased the accuracy results of the classifiers. When the dataset was re-labeled into just two labels, the classifiers consistently achieved accuracy results of over 50%, the highest accuracy being 91%. In contrast, when the dataset was originally provided with six labels, the classifiers only  
Figure 19: ROC curve for the classifying results conducted using the ChatGPT-generated dataset as test data and the LIAR dataset as train data. achieved accuracy results of about 20%. Additionally, the AUC scores show a similar trend, albeit not as drastic. Perhaps the most intriguing part of this research was the novel ChatGPT-generated dataset developed specifically for this paper. While building this dataset, we found out how easy it was to generate false news articles in just a matter of a few minutes. We were able to generate about 10 false articles in roughly an hour. The fact that we succeeded in generating false articles that look completely believable easily and quickly shows that there is a need to expand the topic of automated false news detection into the field of detecting computer-generated texts. In Section 3.4, the focus was on detecting computer-generated false news and understanding how it differs from human-generated false news. The experiments concluded that when using only the ChatGPT-generated dataset, the classifiers were able to achieve similar accuracy results as when using the human-generated datasets. However, when the classifiers were trained using the ChatGPT-generated dataset and tested with the LIAR dataset [4], the achieved accuracy results were around 20% higher than when the training and testing datasets were reversed.