## 2 Theoretical Background
### Related Work on Deception Detection
The answer to the question of which machine learning model best differentiates the true from false data is not easy to come by given that each model works specifically concerning the specific problem and dataset on which it is trained and tested, which is also often conceptualized by the specific context by the researchers in the area [19]. [PERSON] and colleagues in their research [2] deal with the deception detection in asynchronous computer-mediated communication by examining four machine learning models on data collected through two experimental studies. The research is based on the detection of deception from the point of view of interaction, not individual analysis. A similar study was conducted in order to understand changes in the linguistic behavior of people participating in a deceptive or truthful discussion during synchronous computer-mediated communication [3]. The research was conducted by dividing respondents into groups of two people who were given the task of talking to each other via e-mail and, thus, getting to know each other. All respondents were given several topics to discuss in such a way that one respondent (sender) was randomly selected from each group and assigned a task to deceive the person (recipient) by giving a false opinion on two of the five given topics. Likewise, the importance of motivation in deception was examined in such a way that the senders were randomly assigned the additional task of being highly or lowly motivated while lying. The data were analyzed using the LIWC software in order to extract statistically significant features related to false or true communication and to test the hypotheses based on LSM and IDT. Based on LSM and IDT, changes in the behavior of the interlocutor (recipient) were also analyzed in order to examine whether deception can be detected from changes in his behavior. It was also investigated the extent to which motivation changes the linguistic style of a person who lies/deceives in synchronous computer-mediated communication. The results show that senders statistically use more words during deception, more references to other people and less to themselves, and more emotional words. Motivated senders avoid causal terms like \"because\", \"hence\", and \"effect\" while unmotivated ones use more simple negations. According to LSM and IDT theories, recipients use more words and ask more questions during deception, especially when the sender is not motivated [3]. Previous research focused on detecting deception using and combining a few machine learning algorithms and models, mostly Naive Bayes and SVM and to a certain extent analysis on a small range of n-grams. This paper extends the state of the art by employinga wider set of machine learning algorithms and models and uses wider combinations of n-grams. Specifically, prior research was exclusively done on bigrams while this study uses n-grams ranging from unigrams to trigrams. Such an extended set of models, algorithms, and n-grams was further combined with a specific analysis using LIWC, examining its operation on different datasets. The study contributes to the discussion on models' applicability in terms of generalization, where n-grams were found to be the most robust method for deception detection and the analysis using LIWC the least flexible. The best opportunity for generalization were noted with the use of combinations of n-grams and analysis using LIWC. The literature review presented in this chapter is summarized in a table form as part of Appendix A.