No abstract

SummaryThe messages in microblogs, like Twitter, present a wealth of information around the hot topics and public opinions. Topic detection and tracking enables a better understanding of the incoming information from social networks. Many of the studies performed in this field consider a set of predefined number of topics (clusters) which remain constant during the detection process. Such approaches are not efficient when data is dynamic and cumulative. In addition, nonparametric evolutionary topic models do not have an accurate performance on short texts due to the data deficiency. In this article, we propose an evolutionary clustering model based on the Bayesian nonparametric Dirichlet process mixture model, which automatically determines the number of clusters. To improve the algorithm performance when dealing with short social media texts, temporal information of tweets and other social network parameters are used in a weighted combination to augment the text similarity in detecting topics. Model evaluation is performed on the data gathered from the Twitter during a 2.5‐month period. Results reveal that the social network information can promote the topic detection performance. The proposed model outperforms previous study in terms of topic coherence and clustering performance, leading to a better clustering on short texts.

This paper aims to present a Web 2.0-based collaborative Learning framework based on the social constructivist theory promoting lifelong learning. The Web 2.0 provides conditions of collaborative learning, multiple modes and methods of learning and multiple perspective to achieve learning goals. This practical framework under the guidelines of design science research methodology allows learners to actively engage in the learning process, construct own knowledge and use to solve real-world problems collaboratively. Experts reviewed and confirmed the validity of the framework. Three successive iterations, working with 42 students in formal learning and Facebook members in informal learning were conducted to demonstrate and validate how learners started developing critical thinking, selfregulation and use of knowledge. The framework contributes to the practice of lifelong learning with emphasis on developing sustainability competencies that enable learners to gain more skills in applying different problem-solving frameworks to complex daily life problems.