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Using Keystroke Dynamics in a Multi-Agent System for User Guiding in Online Social Networks
Applied Sciences ( IF 2.5 ) Pub Date : 2020-05-28 , DOI: 10.3390/app10113754
Guillem Aguado , Vicente Julián , Ana García-Fornes , Agustín Espinosa

Nowadays there is a strong integration of online social platforms and applications with our daily life. Such interactions can make risks arise and compromise the information we share, thereby leading to privacy issues. In this work, a proposal that makes use of a software agent that performs sentiment analysis and another performing stress analysis on keystroke dynamics data has been designed and implemented. The proposal consists of a set of new agents that have been integrated into a multi-agent system (MAS) for guiding users interacting in online social environments, which has agents for sentiment and stress analysis on text. We propose a combined analysis using the different agents. The MAS analyzes the states of the users when they are interacting, and warns them if the messages they write are deemed negative. In this way, we aim to prevent potential negative outcomes on social network sites (SNSs). We performed experiments in the laboratory with our private SNS Pesedia over a period of one month, so we gathered data about text messages and keystroke dynamics data, and used the datasets to train the artificial neural networks (ANNs) of the agents. A set of experiments was performed for discovering which analysis is able to detect a state of the user that propagates more in the SNS, so it may be more informative for the MAS. Our study will help develop future intelligent systems that utilize user data in online social environments for guiding or helping them in their social experience.

中文翻译:

在多Agent系统中使用击键动力学在在线社交网络中进行用户指导

如今,在线社交平台和应用程序已与我们的日常生活紧密结合。这种互动可能会引起风险并损害我们共享的信息,从而导致隐私问题。在这项工作中,已经设计并实施了一项提案,该提案利用了对击键动态数据进行情感分析和另一种压力分析的软件代理。该提案包括一组新的代理,这些代理已集成到多代理系统(MAS)中,用于指导用户在在线社交环境中进行交互,该代理具有用于对文本进行情感和压力分析的代理。我们建议使用不同的代理进行组合分析。当用户交互时,MAS会分析用户的状态,并在用户编写的消息被视为否定时向用户发出警告。通过这种方式,我们旨在防止社交网站(SNS)上的潜在负面结果。我们在实验室中与我们的私人SNS Pesedia进行了为期一个月的实验,因此我们收集了有关短信和按键动态数据的数据,并使用这些数据集训练了代理商的人工神经网络(ANN)。执行了一组实验,以发现哪种分析能够检测到在SNS中传播更多的用户状态,因此对于MAS可能更有用。我们的研究将帮助开发未来的智能系统,该系统利用在线社交环境中的用户数据来指导或帮助他们进行社交体验。因此,我们收集了有关文本消息的数据和击键动态数据,并使用这些数据集来训练代理的人工神经网络(ANN)。执行了一组实验,以发现哪种分析能够检测到在SNS中传播更多的用户状态,因此对于MAS可能更有用。我们的研究将帮助开发未来的智能系统,该系统利用在线社交环境中的用户数据来指导或帮助他们进行社交体验。因此,我们收集了有关文本消息的数据和击键动态数据,并使用这些数据集来训练代理的人工神经网络(ANN)。执行了一组实验,以发现哪种分析能够检测到在SNS中传播更多的用户状态,因此对于MAS可能更有用。我们的研究将帮助开发未来的智能系统,该系统利用在线社交环境中的用户数据来指导或帮助他们进行社交体验。
更新日期:2020-05-28
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