## 2 Theoretical Background
### Related Work on Deception Detection  
A notable work in deception detection during public trials included analyzing verbal and non-verbal behavior of suspects and witnesses [6]. Videos of witnesses and suspects were collected during testimonies on public trials and were used to build a reliable machine learning model that can distinguish truth from lies by analyzing verbal and non-verbal characteristics and thus provide assistance in making key decisions in the judiciary. The models based on n-grams were tested individually on different sets of verbal and non-verbal features and in combination. A further analysis of a subset of features revealed that the model gives better results when it is trained on non-verbal features, mostly by analyzing facial expressions, which are then followed by unigrams. Human performance in deception detection was tested by analyzing text, sound, noiseless video, and video with sound, which achieved worse results compared to the used machine learning models. In another study, three different approaches were used when analyzing data on false and true positive hotel reviews [4]. Truthful reviews were collected from the TripAdvisor platform while fake reviews were generated using the Amazon Mechanical Turk platform, containing opinions created with the intention of deceiving another person. The text was analyzed using POS and n-gram techniques and psycho-linguistic analysis using LIWC software. The models were tested individually and in combination, and only those with the best performance were selected. Data processing led to the conclusion that bigrams generally give the best results while the combination of bigrams and psycho-linguistic analysis gives a slightly better result. In addition, all tested methods outperform humans on the same dataset. In the same paper, by comparing the features obtained using POS analysis, LIWC, and a combination of analysis with n-grams and LIWC, it was noted that people hardly fake spatial information. Through the analysis of POS, the authors came to the conclusion that some of the language structures that are most often used in honest (informative) reviews are nouns, adjectives, prepositions, conjunctions, and verbs while fake (imaginary) reviews mostly use verbs, adverbs, and pronouns. Verbs and adverbs are common features in both types of reviews, but with an important difference: in the informative text, the past participle is mostly used while in the imaginary text, a much wider range of different verb tenses is used. Somewhat better results on the same TripAdvisor hotel reviews dataset were achieved with a deep syntax analysis (that is, by using features derived from the analysis of parsed trees obtained from probabilistic context-free grammar (PCFG)) [5]. The mentioned approach is combined with shallow syntax (i.e., POS tags) but still gives the best results in combination with n-grams, proving that the analysis of deep syntax offers information not present in the learned POS features and that it can serve as a more reliable method when detecting deception. The models were tested on 4 different datasets, from the domain of fake reviews from the TripAdvisor and Yelp platforms and sets of essays collected through Amazon Mechanical Turk on the topics \"abortion\", \"best friend\", and \"death penalty\". The same set of essays was analyzed using LIWC software in the work of [PERSON] & [PERSON] [8], showing slightly less favorable results. In fake essays, references to other people (\"you\", \"others\", \"people\") and words related to certainty are mostly present while in true essays the person confidently connects with the statements made using more references on oneself (\"I\", \"friends\", \"self\") and presents several attitudes based on belief (\"think\", \"feel\", believe\") [8]. In addition to TripAdvisor, the analysis of fake reviews was also conducted on social platforms, such as Twitter [18].