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Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing. 

Drug misuse and overdose has plagued the United States over the past decades and has severely impacted several communities and families. Often, it is difficult for drug users to get the assistance they need and thus many usage cases remain undetected until it is too late. With the booming age of social media, many users often prefer to discuss their emotions through virtual environments where they can also meet others dealing with similar problems. The widespread use of social media sites creates interesting new opportunities to apply NLP techniques to analyze content and potentially help those drug users (e.g., early detection and intervention). To tap into such opportunities, we study categorization of tweets about drug usage into fine-grained categories. To facilitate the study of the proposed new problem, we create a new dataset and use this data to study the effectiveness of multiple representative categorization methods. We further analyze errors made by these methods and explore new features to improve them. We find that a new feature based on tweet tone is quite useful in improving classification scores. We further explore possible downstream applications based on this classification system and provide a set of preliminary findings.