# Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
## 4 Experiments with Data from the SNS Pesedia
### Plan of the Experiments  
We used the messages that had or generated replies in the network for our experiment and used the text data and keystroke dynamics data of the user writing those messages. The goal of the experimentation was to compute the propagation of the detected value in the messages in the network, by comparing this value to the most present value detected in the replies of the messages. This propagation was measured for the different analyses that we had available, which were sentiment, stress, and combined analysis using text, and then again sentiment, stress, and combined but using keystroke dynamics data. We also computed the propagation for a combined analysis that used both combined sentiment and stress analysis on text data, and combined sentiment and stress analysis on keystroke dynamics data. Figure 3: Example of messages posted and replies with different polarities. We performed the training of the ANNs that would conduct the different analyses using different partitions of the training dataset mentioned in Section 3 each time. We performed five different partitions of the dataset. In this way, we obtained five ANNs trained to detect sentiment and five trained to detect stress levels on text data, and again five ANNs to detect sentiment and five to detect stress levels with keystroke dynamics data, using different partitions of the data for training each one. This process was done for being able to analyze not only differences between analyzers, but also for exploring the differences of ANNs trained with different data. For this purpose, we proceeded to perform experiments with the data not used for training (we separated the messages that were used for training from the dataset for conducting these experiments), comparing the ANNs that detected sentiments with the ones that detected stress labels on text data, and the same with the ones that operate with keystroke dynamics data. That resulted in a total of 25 experiments on text and 25 on keystroke dynamics data, since we compared every pair of text ANNs and every pair of keystroke dynamics ANNs. Finally, we launched several experiments with the setup presented in this section, changing the threshold of class inference in the ANNs from 0.5 to 0.9. This means that if the probability of a class given as output by the ANN model is greater or equal than the threshold, the result of the analysis is this concrete class; otherwise, it is the other class. We show in Figure 4 the results of propagation for the analyses on text data, and the combined analyses of text and keystroke dynamics data with thresholds for class detection unaltered. Figure 5 shows the results of propagation of analyses on keystroke dynamics data and the combined analyses with thresholds unaltered again. Then, for analyzing the effect of altering the threshold on the ANNs, we show the following figures, where the threshold is set to 0.7 (we do not show the other possibilities such as threshold 0.9 since 0.7 was shown experimentally to be the only case where there are bigger differences with the base case with no changes). We show in Figure 6 the results of the analyses on text data and combined analyses (text and keystroke), while changing the threshold of the ANNs that work with keystroke dynamics data to 0.7. We show the results for the analyses on keystroke dynamics data and the combined analyses applying this same process in Figure 7. Altering the threshold in ANNs that work with text data to 0.7, we repeated this process and obtained the Figures 8 and 9. Finally, we show in Figures 10 and 11 the results altering the threshold to 0.7 in the ANNs that perform sentiment analysis, and in Figures 12 and 13 the results altering the threshold to 0.7 in the ANNs that perform stress analysis. In the figures, the legend represents the following forms of the metric presented in Section 4.1:  
* PCOMB_or_text: PDV of the \"or\" version of combined sentiment and stress analyzers on text. * PSEN_text: PDV of the sentiment analyzer on text.