Abstract
This paper presents a theoretical model for interpreting the underlying meaning in virtual dialogue from computer-mediated communication (CMC). The objective is to develop a model for processing dialogues and understanding the meaning of online users’ social interactions based on available information behavior. The methodology proposed in this paper is built on a demonstrated observation that humans – in analogy to “sensors” in social networks – can detect unusual or unexpected changes in humans’ trustworthiness based on observed virtual behaviors. Even with limited resources such as email, blogs, online conversations, etc., humans “sensors” can infer meaning based on observed behaviors, and assign attributes to certain words or actions. The idiosyncratic nature of human observations can be arbitrated by an attribution mechanism that provides the basis for a systematic approach to measuring trustworthiness. In this paper, we discuss a particular trust scenario called the Leader’s Dilemma with the objective of identifying how anomalous online behavior can be interpreted as untrustworthy. We adopt the dyadic attribution model to analyze how a human disposition can be systematically uncovered based on words and actions, as evidenced by information behavior. This model is better suited for computational analysis of attribution engines. The novel goal of this research is to design a sensor system with the ability to attribute meaning to virtual interactions as supported by computer-mediated technologies.
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Ho, S.M., Timmarajus, S.S., Burmester, M., Liu, X. (2014). Dyadic Attribution: A Theoretical Model for Interpreting Online Words and Actions. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2014. Lecture Notes in Computer Science, vol 8393. Springer, Cham. https://doi.org/10.1007/978-3-319-05579-4_34
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DOI: https://doi.org/10.1007/978-3-319-05579-4_34
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