# Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
## 4 Experiments with Data from the SNS Pesedia
### Reformulation of the Advisor Agent  
Since there has been an addition of new analyzers in the system and experiments performed for knowing which analyzers work best at detecting states in the user that propagate more and therefore might be more informative for the advisor agent, this agent has been redesigned and its decision making process for generating feedback updated. The selection of the analyses used in the advisor agent is done because the experiments have shown that as a general trend they are the best at detecting a state that propagates more in the network, and they are shown in the following. As has been stated in the previous section, the advisor agent accomplishes two different tasks: the calculation of the combined analyses from the data given by the four analyzer agents, and the generation of feedback to the users interacting in the SNS that generated the message being analyzed.  
In accordance with the experiments performed, the first task of the advisor agent has been determined to be the calculation of three different combined analyses, selected from the results of the experiments that aimed to discover which analysis is more informative at detecting a state of the user that propagates more in the SNS. The three combined analyses computed in this agent are the following: PCOMB_and_text, PCOMB_and_ksd, and PCOMB_TEXT_AND_KSD_or. The two combined analyses that use only one data source (PCOMB_and_text and PCOMB_and_ksd) use the \"and\" version of combined analysis. Finally, the combined analysis of text and keystroke dynamics use an \"or\" version of combined analysis, taking the output of the text combined analysis and keystroke dynamics combined analysis as input. This selection of analyses is done based on the results of the experiments, as has been shown in Figures 4-13.  
The second task of this agent is to generate feedback to the user, based on the data generated on the analysis. The rules used for this process are based on the following:  
1. As the default case, if there are no issues detecting input, or with data availability or neutral detections, then the warning is generated when a negative message is detected by the text and keystroke dynamics data combined analysis.
2. The tokenizer sometimes is not able to detect any token in the input text; in this case, the feedback to warn the user is generated when the sentiment and stress combined analysis on keystroke dynamics data assigns a negative label to the message.
3. One source of data is not available, text or keystroke dynamics, so the warning is generated if the combined analysis on the available data detects a negative message.
4. The class probability detected is in the range of 0.5-0.525 for both sentiment and stress analysis on one kind of data, meaning that the ANN models for this data source detect a neutral state. In this case, the warning is generated if the combined analysis on the other data source detects a negative message outside of the mentioned range of class probability. If both data sources generated an output with a class probability inside the mentioned range, then the combined analysis of text and keystroke dynamics data is used for the generation of the warning, doing so if a negative message is detected by this combined analysis.