## 6 Conclusions  
Deception detection has turned out to be quite a demanding problem considering the various inherent limitations. The first problem encountered is finding validly labeled true and false data. Given that there is no absolutely reliable method of verifying the veracity of the same information, such a problem was approached in this study with caution, taking into account the amount of motivation and sincerity the people demonstrate when answering the defined survey questions during the data collection procedure. Machine learning models proved to be successful when working on these collected datasets, which once again confirmed the existence of hidden linguistic features of deception present in verbal communication. Natural language processing methods achieved satisfactory results while LIWC analysis and the combined analysis of n-grams and LIWC proved to be the most successful in deception detection. By examining the possibility of generalization, the mentioned methods also achieved better performance compared to the analysis with n-grams, especially on trigrams, where the analysis using a combined approach achieved significantly better prediction compared to the analysis exclusively with n-grams. Such detected generalization is in line with the recent research efforts leading to conclusions that \"deceptive text is more formulaic and less varied than truthful text\", however more research in this area is warranted [20]. However, during the testing of the mutual applicability of the models, the n-gram analysis proved to be a more robust method. While testing the performance of the models obtained based on the DS1 dataset on the DS2 data, the n-gram analysis achieved better results compared to training and testing the models on own data (DS1). On the other hand, the LIWC analysis proved to be the most inflexible when it came to its applicability to other datasets. Machine learning models with LIWC can be used independently in the detection of deception with certain limitations, such as the fact that they are much less applicable to the datasets on which they have not been trained. The combined approach with n-grams and LIWC in most cases allows for even more successful differentiation between truth and lies. Therefore, machine learning models and the approach to deception detection should be carefully selected depending on the specifics of the given problem, such as the need for applicability to other datasets or the possibility of generalization. In addition to the scientific value of this study, the approach has practical advantages. Unlike state-of-the-art deep learning models, which can be computationally intensive and time-consuming, our approach requires less computational resources and is faster. This makes it more accessible and feasible for researchers and practitioners who may not have access to high-performance computing resources or who need to process large amounts of data quickly. Moreover, the efficiency of our approach does not compromise its effectiveness in detecting deception. By combining psycho-linguistic analysis and n-grams, our study achieves high classification accuracy, especially on trigrams, which is comparable to state-of-the-art models. Therefore, our approach not only provides a more practical solution, but also delivers comparable results to more resource-intensive methods. Furthermore, our study highlights the importance of considering the subjectivity of the data when analyzing it for deception, which is a crucial factor in the effectiveness of any machine learning model. By exploring the use of a psycho-linguistic analysis, our study also demonstrates the potential benefits of incorporating linguistic features beyond just n-grams, which could inspire further research in this area. Conceptualization, I.B. and B.B. ; Methodology, I.B. and B.B; Software, B.B. ; Validation, I.B., B.B. and M.B.B. ; Formal Analysis, B.B. ; Investigation, I.B. and B.B. ; Resources, I.B. ; Data Curation, B.B. ; Writing--Original Draft Preparation, B.B. ; Writing--Review & Editing, B.B., I.B.