Online services provide a range of opportunities for understanding human behavior through the large aggregate data sets that there operation collects. Social network services have become a viable source of information for users. Studying the characteristics of such popular message is important for a number of tasks such as, breaking news detection, personalized message recommendation, others. We formulate the task into the classification problem and study two of its variants by investigating a wide spectrum of features based on the contents of messages, temporal information, metadata of messages and users, as well as structural properties of the user's social graph on large scale dataset. Students' informal conversations on social media shed light into their educational experiences opinions, feelings, and concerns about the learning process. Data from such instrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity of students' experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques. We focused on engineering students' Twitter posts to understand issues and problems in their educational experiences.

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Social Media Classification Educations Computer And Educations