# Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
## 5 Conclusions and Future Work  
In the present work, we introduced new agents capable of performing sentiment and stress analysis on keystroke dynamics data into a MAS presented in a previous work, in order to improve the capacity of the MAS when predicting user states that could generate a problem or make risks arise from the social interaction. The MAS guides users on a SNS or another online social environment by analyzing the data of a user that posts a message and giving feedback to the user if this message is deemed negative by the MAS agents analyses. We also propose different versions of combined analysis that use both sentiment analysis and stress analysis on text data and on keystroke dynamics data, to be used in the advisor agent of the MAS for the task of generating feedback to the users. For discovering what analyses are more informative to be used in the generation of warnings or feedback, and thus improve the system ability to prevent negative outcomes and risks in a social online environment, we integrated our MAS into a private SNS called Pesedia and performed experiments with real users during a period of one month. During this period, we gathered the dataset of text and keystroke dynamics data used for training the machine learning models. We performed a laboratory experiment with the data from the dataset not used for training that aimed at discovering which analyses are able to predict a state of the user that propagates more to the replies of the message analyzed in the network. In this way, we would be able to know what analyses can be considered more informative to be taken into account for warning the users, in terms of their ability to detect a state that may potentially propagate more in the network. We also launched experiments with different setups in the threshold for class detection in the ANNs that perform the analysis. Regarding the experimentation with the different analyses for discovering which one detects a state that propagates more to the replies, we found that the best analyses are the \"or\" version of combined text and keystroke analysis that uses \"and\" combinations of sentiment and stress analysis, and the \"and\" versions of text combined analysis and keystroke dynamics combined analysis. Analyses on only keystroke dynamics also shown high results of propagation but the combined version of sentiment and stress on keystroke dynamics was better than the non-combined analyses. Moreover, when setting a high threshold for class detection (making the detection process more strict, by selecting one class only with a 70% of probability or more), in the ANNs that perform analysis on keystroke dynamics data and in the ANNs that perform sentiment analysis, the best analysis that combines text and keystroke data approaches the best single data type analysis. This is not the case when altering the threshold for the ANNs that perform analysis on text data and that perform stress analysis. The proposed approach, as shown by the experiments, can help users navigating in an online social environment be aware that the information they post has a chance of generating negativity or high stress in the network, and thus can help prevent risks. Nevertheless, the targeted users in our proposed system are people of a young age, and so we built and trained models with data from people of ages between 12 and 15 years old; therefore, it is not granted that the performance of the system implemented will be the same when used in online social environments with older or more experienced users. Despite this limitation, in [7] we built text data analyzers (which also included combined analysis) that were able to predict sentiment and stress states of the users that propagated to the replies of messages in the SNS twitter.com, which has a wide range of users; therefore, it has been shown that analyzers of text data can be used for this purpose. Moreover, data privacy is a limitation for the proposed system, since it relies on the analyses on user data to generate feedback and potentially prevent risks.