5.2.1.
Introduction
In this paragraph, some features are studied that cannot be modeled per key event. The literature implies that lying is a cognitive intensive process and it is shown that deceivers display longer pauses and lower response lengths consequently (Vrij, 2008). In this chapter, these characteristics are treated and modeled into features to be reviewed. Figure 14 shows the char codes (on the y-axis) plotted against the timestamps of all key events of a message. Each circle represents a key press. The arrows point to different places in the message where deletion, typing or a pause occurs. These figures are insightful to generate an idea on what happened while the participant was writing the message, but visually analyzing the 120 messages did not yield a new insight.
The deletion keys also show a certain pattern. For small corrections, the key is used one time. In a lot of cases however, the key is used frequently to delete a large amount of text. It would be useful to recognize this behaviour and to find out if the frequency and length with which these deletion moments occur is influenced by the intent of the message. Furthermore, the typing speed or pause rate may indicate the thoughts a user has put in while writing a message. Writing a deceptive message could supposedly cost more time as the participant has to come up with arguments. Or maybe will be done faster as he is proficient with lying. That is what will be analysed in the coming paragraphs.
5.2.2.
Quantitative message properties
In this paragraph, some quantitative properties between the deceptive and truthful messages will be analysed. The properties are message length, time to write the total message and the deletion rate. These aspects are derived from the literature on deception, which states that often these traits also change for real-life deceivers (Vrij, 2008).
First, the message length is analysed. Deceivers are said to produce shorter answers. This might be reflected in the number of key events per message. In Figure 15, the number of key events are plotted per message type and participant (x-axis). The figure indicates that there is no quantitative pattern visible that discriminates truthful from deceptive messages. The number of key events per message seem distributed randomly. It is shown that each participant has messages of equal size. The number of key events does not visually indicate cues that discriminate deception from truthfulness.
Figure 15 - Key events per message of each participant
Key events do not reflect the length of the message properly. The number of key events may increase as a user presses the shift or delete, but this does not change the size of the semantical message consisting of alphabetic characters. The true number of alphabetic characters of each message can be plotted against the number of key events that was needed to form this message, to see the differences between the messages per intent. In Figure 16, the true message length is plotted against the number of key events. The plot does not show a difference between the two types of messages, as both the truthful and deceptive messages are equally distributed throughout the range.
Now the literature on deception also showed that deceivers need more time to formulate an answer (Vrij, 2008). In Figure 17, the writing time in seconds is plotted for each message per user. The figure shows that writing time does not obviously discriminates messages by intent per person. The messages are distributed rather homogeneously which will not favour a classification job based on this metric.
Figure 17 - Plot of the writing time in seconds for each message per user
Lastly, the number of deletions can be plotted against the total number of key events. Deletions may have a predictive force when it comes to deception, as participants may be more hesitant about their writing when they are deceptive. In Figure 18, the number of deletions is plotted against the key events of the corresponding message. It is shown in the plot that the truthful and deceptive messages are spread homogenously. This makes deletion per length of message unfit for classification.
Figure 18 - Number of deletions against the total number of key events of a message’
To conclude, the features considered in this sub-chapter do not show visible differences that are suitable to be used for classification.
5.2.3.
Pauses between words
After writing each word, the participant may take a pause. During these pauses, the participant may do several things. He may continue writing, take some time to think, look for spelling errors, or check the contents of his written words. The pauses during these breaks might be indicators to deception. In order to analyse these pauses, each part in the text where two words are separated is analysed. This separation is typically characterized by a space. In Figure 19, the PDFs of the messages of participant 2 are plotted. The plot does not show obvious differences between the two messages, with a difference value of 0.3744.
Figure 19 - Pauses between words per message type of participant 2
5.2.4.
Statistical differences
The quantitative features, as displayed in chapter 5.2.2, show no visible difference between the two types of messages. The pause rate however, did show some differences. In this sub-chapter, the statistical properties of the pauses between words of the two message types for each participant will be calculated. For the statistical test, a Mann-Whitney U-test will be used. The results are shown in Table 4. A p-value of 0.05 is chosen. The number of participants with statistical significant differences in the pause rate between words is 1.
Power Pause rate (p-value) 1 16650 0,87 2 7497 0,92 3 18785 0,20 4 8512 0,56 5 17391 0,36 6 23618 0,33 7 3176 0,66 8 5895 0,36 9 13448 0,06 10 11020 0,22 11 19292 0,00* 12 10034 0,85 13 12740 0,66 14 19623 0,76 15 11250 0,93 16 17922 0,33 17 13659 0,27 18 441 0,50 19 6825 0,25 20 29507 0,17 21 5792 0,13 22 18236 0,17 23 7473 0,10 24 3328 0,25 25 16888 0,76 26 14453 0,92 27 1922 0,90 28 22038 0,79 29 8954 0,89 30 528 0,08