As an increasing important source of information and knowledge, social questioning and answering sites (social Q&A) have attracted significant attention from industry and academia, as they address the challenge of evaluating and predicting the quality of answers on such sites. However, few previous studies examined the answer quality by considering knowledge domains or topics as a potential factor. To fill this gap, a model consisting of 24 textual and non-textual features of answers was developed in this study to evaluate and predict answer quality for social Q&A, and the model was applied to identify and compare useful features for predicting high-quality answers across four knowledge domains, including science, technology, art, and recreation. The findings indicate that review and user features are the most powerful indicators of high-quality answers regardless of knowledge domains, while the usefulness of textual features (length, structure, and writing style) varies across different knowledge domains. In the future, the findings could be applied to automatically assessing answer quality and quality control in social Q&A.
KeywordsSocial question and answer sites, answer quality, quality assessment.
Language as a symbolic medium plays an important role in virtual communications. Words communicated online as action cues can provide indications of an actor's behavioral intent. This paper describes an ongoing investigation into the impact of a deceptive insider on group dynamics in virtual teamcollaboration. An experiment using an online game environment was conducted in 2014. Our findings support the hypothesis that language-action cues of group interactions will change significantly after an insider has been compromised and makes efforts to deceive. Furthermore, the language used in group dynamic interaction will tend to employ more cognition, inclusivity and exclusivity words when interacting with each other and with the focal insider. Future work will employ finely tuned complex Linguistic Inquiry and Word Count dictionaries to identify additional language-action cues for deception.
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