## 5 Discussion  
### Datasets and Models  
The best classifications of datasets DS1, DS2, and DS3, considering the average accuracies obtained using all the models presented in Table 12, were achieved with the LIWC analysis and by combining n-grams and LIWC. The above two methods also generalize best according to testing the combined dataset DS3 individually on the DS1 and DS2 data (Table 13). The climate change dataset (DS1) proved to be more applicable to deception detection compared to the COVID-19 dataset (DS2) and the combined dataset (DS3). Machine learning models achieved the best average results on DS1 using all data processing approaches (Table 14). While examining the possibility of generalization by training models on the combined dataset (DS3) and testing on individual datasets (DS1 and DS2), models also achieved better performance tested on DS1 compared to the DS2 dataset. Consequently, models both trained and tested on the combined set achieved better average results compared to the training and testing on dataset DS2. It appears that DS2 data offers less information that can be used in deception detection compared to other datasets. Given that DS2 (dataset \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**All Models**} \\ \hline  & & **DS3 Models** & **DS3 Models** & **The Average Accuracy** \\  & & **Tested on Dataset** & **Tested on Dataset** & **Obtained by Training and Testing Models** \\  & & **DS1** & **DS2** & **on Datasets DS1, DS2 and DS3** \\ \cline{2-5}  & **LIWC** & 77.94 & 71.08 & 74.51 \\ \hline \multirow{3}{*}{1,1} & n-grams & 70.29 & 64.90 & 67.59 \\ \cline{2-5}  & n-grams + LIWC & 79.04 & 72.92 & 75.98 \\ \hline \multirow{3}{*}{1,2} & n-grams & 72.12 & 67.71 & 69.91 \\ \cline{2-5}  & n-grams + LIWC & 80.19 & 72.61 & 76.40 \\ \hline \multirow{3}{*}{2,2} & n-grams & 71.16 & 69.07 & 70.11 \\ \cline{2-5}  & n-grams + LIWC & 77.85 & 70.63 & 74.24 \\ \hline \multirow{3}{*}{1,3} & n-grams & 73.56 & 66.77 & 70.16 \\ \cline{2-5}  & n-grams + LIWC & 78.66 & 72.81 & 75.73 \\ \hline \multirow{3}{*}{2,3} & n-grams & 75.00 & 65.10 & 70.05 \\ \cline{2-5}  & n-grams + LIWC & 77.50 & 73.23 & 75.37 \\ \hline \multirow{3}{*}{3,3} & n-grams & 67.12 & 58.96 & 63.04 \\ \cline{2-5}  & n-grams + LIWC & 74.81 & 75.21 & 75.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Overview of trends by method for models trained on combined dataset (DS3) and tested individually on DS1 and DS2 data (colored values indicate maximum accuracy obtained using a particular data processing method on each of the given datasets). for COVID-19) is based on answers related to partly more current and potentially more personal experiences, there is a possibility that participants lied more successfully on a topic that is closer to them and more subjective; therefore, the very distinction of lies using the selected data analysis methods became more difficult. In addition, there is a possibility that for the same reason participants were more motivated to lie more convincingly considering the scope of experience they have about COVID-19 and consequently achieved a higher success rate of deception. On the other hand, data collected on the topic of climate change are potentially more objective, which leads to weaker results when trying to deceive. During training and testing of the machine learning models on the DS2 dataset, it was noted that the analysis with LIWC (73.13%) or the combined approach (73.33%) achieves better average results than those obtained exclusively with n-grams (67.34%) (Table 12). The analysis using the combined approach on the same dataset also achieves better average results on all sets of n-grams compared to the analysis exclusively with n-grams (Table 14).