## 1 Introduction  
In today's world of fast-growing technology and an inexhaustible amount of data, there is a great need to control and verify data validity due to the possibility of fraud. Therefore, the need for a reliable form of detection of such content is not surprising. Some of the ways in which deception manifests itself on the Internet are using identity deception, mimicking data and processes for the purpose of stealing credit card numbers or other private information, charging false invoices for services not performed, hacking sites, offering false excuses and promises, false advertising, spreading propaganda and false information, and other forms of fraud. Therefore, detecting deception, whether in face-to-face interaction or while communicating through a certain medium, is of great importance. The great need to find a reliable method for deception detection is even more emphasized due to the fact that people tell approximately two lies per day [1]. Lying is undoubtedly a skill that is deeply rooted in the human existence, and it has been perfected over the years to a level that is difficult to recognize by even the most experienced professionals. The question is what makes the distinction between truth and lies/deception, especially in the verbal aspect, and if it exists, what is the best way to determine it? Do most of the information lie in non-verbal behavior, or are there certain linguistic patterns that can serve as sufficiently precise indicators of deception? Is there a difference in deception during face-to-face and computer-mediated communication, whether synchronous or asynchronous, verbal or non-verbal?Research so far has led to the conclusion that verbal behavior hides a deep amount of information that can be used in the detection of deception, almost more accurately than in the case of non-verbal analysis. Due to the inapplicability of the polygraph method to deception detection in computer-mediated communication, there is an increasing emphasis on research in methods for analyzing the syntactic and semantic properties of written text and finding indicators of deception in various forms of digital interaction. So far, the most commonly used methods of deception detection in the text are machine learning models. There is a great need for further research into syntactic, semantic, and other properties of natural languages in order to create software that will detect deception with high accuracy. Current research covers deception detection in computer-mediated communication [2; 3]; detection of fake reviews on social platforms [4; 5]; deception detection collected from public trials [6; 7]; the use of crowdsourcing platforms, such as Amazon Mechanical Turk, for generating deception datasets [5; 8]; etc. In their work, [PERSON] and colleagues [5] also analyzed the deep syntax of the data using the principles of probabilistic context-free grammar (PCFG), independently and in combination with the aforementioned methods. In addition to the above, the LIWC tool was also used for deception detection by leveraging insight into the psycho-linguistic characteristics of the analyzed text. In this paper, several different machine learning models were used and their performance in differentiating deceptive and true text was tested. Two sets of data were collected using the crowdsourcing platform Clickworker and the survey tool Qualtrics Survey. For data processing, n-grams, LIWC, and a combination of the two approaches were used. A selection of essential features for each model was carried out over the LIWC dimensions using the WEKA tool in order to obtain subsets of features with which the models provide the highest accuracy. Since two distinct datasets on two topics were created, they were used to train their own separate models. These models were then tested on their own data, but they were also cross-tested on the data they were not trained with. Furthermore, both datasets were combined to create a joint dataset on deceptive text, which was then used to test both models.