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Titlebook: HCI in Business; Second International Fiona Fui-Hoon Nah,Chuan-Hoo Tan Conference proceedings 2015 Springer International Publishing Switze

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楼主: panache
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The Beginner‘s Guide to Data Science therefore examine the content change in terms of quantity change and content stability by showing the semantic convergence. We found out that the quantity of content change is significant in the promoted articles, which complies with Wikipedia’s stated criteria.
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Social Media Analytics with Facebook - The Case of Higher Education Institutionsasis to evaluate and to develop their Facebook activities. We conclude that many HEIs lack an integrated Social Media Analytics strategy, under-utilize the real-time capabilities of Social Media Analytics and would benefit from Social Media Analytics.
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What Do Patients of Different Medical Illness Say About Their Doctors Online? An Analysis of Online cteristics of online physician reviews, this study took a step further to identify the relationship between patients of different medical and how they evaluated a medical encounter. In this paper, findings were reported and implications in improving physician-patient communication and patients’ empowerment were discussed.
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Understanding the ‘Quality Motion’ of Wikipedia Articles Through Semantic Convergence Analysis therefore examine the content change in terms of quantity change and content stability by showing the semantic convergence. We found out that the quantity of content change is significant in the promoted articles, which complies with Wikipedia’s stated criteria.
发表于 2025-3-24 11:31:15 | 显示全部楼层
A Study of the Uses of Instant Messaging in the Government Officesive impact on workplace IM uses and . has a positive influence on the relationship between workplace IM uses and the associated communication satisfaction. Based on our findings, implications for research and practices are discussed.
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The Diagnostic Melancholia Scale (DMS),utperforms the other machine learning methods. The best precision scores of predicting three stance categories (i.e., ., . and .) are 94.62 %, 88.86 % and 10.47 %, respectively, which concludes that the proposed approach can capture the sentiment of documents from lack-of-label, skewed data.
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