The social values of wireless technology in the institutional context

Despite growing attention, social values, compared to economic aspects, of information technology (IT) capture substantially less attention in the mainstream IT literature. In the context of mobile technology, social values might be as critical to help justify technology investment as the predominant economics perspective in the existing IT literature. As wireless networks and relevant mobile technologies continue to penetrate the global society and business world, an emerging social phenomenon rapidly reshapes how organizations interact with the technology and reposition themselves in their specific institutional context where organizations often develop networked alliance to compete against one another. This study thus seeks to shed light on how organizations make sense of the social aspects of wireless network implementation. Preliminary understanding derived from two higher education organizations' experiences is summarized. Implications for future research endeavor are suggested.

Detecting expressions of blame or praise in text

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.