## 2 Theoretical Background
### Tools and Methods in Deception Detection
#### 2.3.3 Linguistic Inquiry and Word Count (LIWC)  
LIWC (Linguistic Inquiry and Word Count) is a software for text analysis designed for the purpose of studying natural language. It consists of two key components: the word processing component and the LIWC dictionary. The dictionary forms the core of the application itself, as it connects psychosocial with linguistic constructs and consists of over 12,000 words, root words, and phrases. Groups of words from the LIWC vocabulary that specify a particular domain are referred to as \"categories\" or \"dimensions\". Each LIWC entry can belong to several LIWC categories and are mostly arranged hierarchically. Originally, the categories were cognitive and emotional while with the increasing understanding of the psychology of verbal behavior, the number and depth of categories increased [15]. LIWC receives text records in various formats of input, which it then sequentially analyzes and compares with the dictionary. The software counts the words in a given text and calculates the percentage of total words represented in all LIWC subcategories. The latest version of the LIWC-22 offers some improvements over previous versions. The dictionary has been upgraded to handle numbers, punctuation marks, short phrases, and regular expressions in order to extend the use of LIWC (for example, to the analysis of content from social networks (Facebook, Twitter, Instagram, Snapchat) where such linguistic style is often present). In LIWC-22, the psychometric abilities of the dictionary were improved, and several new categories were added [16]. The construction of the LIWC began with the intention of analyzing verbal speech to extract psychological processes described through the use of style words and the content of what is written or spoken (content words). It was soon concluded that these are quite different categories with different psychometric properties. Style words, which are also called function words, \"make up only 0.05% of the total set of words in the English language, and are contained in a total of 55% of all words that we hear, speak or read\". They represent the way people communicate and offer a greater insight into the psychosocial aspect of speech compared to content words, which describe only the content of communication [17]. Function words have proven to be very successful in the analysis of the emotional and biological state, status, sincerity, and individual differences; therefore, the emphasis is placed precisely on their deeper analysis in order to give insight into psychological processes that other methods of text analysis simply cannot detect. On the other hand, LIWC is a probabilistic system that does not take context into account linguistic constructs such as irony, sarcasm, and idioms; therefore, absolute conclusions about human psychology by using only LIWC analysis cannot be drawn [17]. Research proves LIWC to be successful in detecting deception. \"Deceptive statements compared with truthful ones are moderately descriptive, distanced from self, and more negative\" [17]. Such a description is not surprising considering that more information carries a greater risk of uncovering the truth. By analyzing deception in synchronous computer-mediated communication, it was shown how the linguistic style of the sender (who lies) and the receiver (who is unaware of the deception) changes. Both respondents were using more words overall (especially sensory) and fewer 1 st person pronouns during deception compared to honest interaction [2; 3]. Thus, it is obvious that linguistic style hides patterns that are specific to true and false communication, which can to some extent be successfully detected using the LIWC approach. Since LIWC software lacks context analysis, it is recommended to combine it with other natural language processing methods. Based on previous research, LIWC, together with an analysis combined with n-grams, achieved satisfactory results [4; 5].