Abstract-Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest. While efforts have been made toward automated unrest prediction, we focus on filtering the vast volume of tweets to identify tweets relevant to unrest, which can be provided to downstream users for further analysis. We train a supervised classifier that is able to label Arabic language tweets as relevant to unrest with high reliability. We examine the relationship between training data size and performance and investigate ways to optimize the model building process while minimizing cost. We also explore how confidence thresholds can be set to achieve desired levels of performance.

In this article, the authors describe the process through which faculty and staff members at Georgia Southern University (GSU) have collaborated to revamp our First-Year Seminar. In 2006, a university task force reviewed results of GSU self-study data as well as best practices at other institutions to make recommendations for improvements to the first-year experience for our students. The task force made several recommendations that included significantly modifying our First-Year Seminar to include a focus on information literacy and to set a more academic tone. A set of online tutorials was developed to support the learning outcomes related to information literacy within the new seminar. The benefits and challenges of implementing this new seminar are discussed.

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Scaffolding self-regulated learning and metacognition: Implications for the design of computer-based scaffolds.

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