# Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
## 4 Experiments with Data from the SNS Pesedia
### Results  
As Table 2 shows, in the experiments with both the ANNs trained with text data (embeddings of texts) and the ones trained with data of keystroke dynamics, there is a propagation of the detected state of the user to the replies of the message being analyzed. We can appreciate that there are several  
Figure 12: Results of the experiments with the sentiment, stress, and combined analyses on text, and with the combined analyses on text and keystroke data with threshold set to 0.7 in the ANNs that perform stress analysis. Figure 13: Results of the experiments with the sentiment, stress, and combined analyses on keystroke data, and with the combined analyses on text and keystroke data with threshold set to 0.7 in the ANNs that perform stress analysis. differences between experiments with different partitions of the data used on the training and in the experimentation, even when the general trend is to find a propagation of the state. There were cases that provided a network that was very likely to give as output one of the two classes (negative or positive sentiment for sentiment analysis, or in the case of stress analysis high or low stress level). This was because the data in the partition of the dataset used for the experiment were unbalanced in favor of one of the classes, which resulted in high propagation in the later experiment (since the ANN was very likely to output one of the two classes, the comparison between the analyses of the messages and the replies matched most of the time). Additionally, some cases give poor propagation, since the data in that partition could not provide an ANN that would give satisfying results of accuracy. The average propagated values for the different analyses are shown in Table 2. In the experiments without altering the thresholds for class detection, the best-performing analyses in terms of propagation for combined analyses of sentiment and stress that use fusion of text and keystroke data (combining the output of the respective analyzers) were PCOMB_TEXT_AND_KSD_and (0.8951 average propagation) and PCOMB_TEXT_AND_KSD_or (0.9153 average propagation), the latter being better than the former. For combined analyses of sentiment and stress that do not use data fusion, the best-performing analyses were PCOMB_and_text (0.9196 average propagation) and PCOMB_and_ksd (0.9811 average propagation), the latter being better than the former again. As shown in Table 2 the non-fusion analyses also shown higher average propagation than the analyses using text and keystroke dynamics data. PSEN_ksd and PSTR_ksd are the two non-combined and non-fusion analyses with the highest propagation (0.9193 and 0.9235 average propagation respectively) and PSTR_ksd also has higher propagation than PCOMB_and_text but lower than PCOMB_and_ksd. Finally, PCOMB_or_ksd also shown a high result of propagation, but lower than the best performing analyses already discussed in this section. The worst performing analyses were PCOMB_TEXT_OR_KSD_or, PCOMB_TEXT_OR_KSD_and, PCOMB_or_text, and PSEN_text (0.7033, 0.5333, 0.6823, and 0.6868 average propagation respectively), PCOMB_TEXT_OR_KSD_or best. Regarding the experimentation altering the threshold for class detection for the ANNs, it can be seen that as a general trend, every analysis affected obtains a better propagation of the state detected upon setting the threshold to a more strict point (e.g., 0.7 like in the results shown on Figures 6-13). On the one hand, when altering the threshold of the ANNs that work with keystroke dynamics data, setting it to 0.7, it is shown that PCOMB_TEXT_AND_KSD_or (best performing fusion analysis) is able to perform with a very similar propagation than PCOMB_and_text (which is the best analysis using only text data, while analyses using keystroke data were close to total propagation because of making the threshold strict for this data).