Mass Collaboration on the Web: Textual Content Analysis by Means of Natural Language Processing

Abstract

This chapter describes perspectives for utilizing natural language processing (NLP) to analyze artifacts arising from mass collaboration on the web. In recent years, the amount of user-generated content on the web has grown drastically. This content is typically noisy, un-or at best semi-structured, so that traditional analysis tools cannot properly handle it. To discover linguistic structures in this data, manual analysis is not feasible due to the large quantities of data. In this chapter, we explain and analyze web-based resources of mass collaboration, namely wikis, web forums, debate platforms and blog comments. We introduce recent advances and ongoing efforts to analyze textual content in two of these resources with the help of NLP. This includes an approach to discover flows of knowledge in online mass collaboration as well as methods to mine argumentative structures in natural language text. Finally, we outline application scenarios of the previously discussed techniques and resources within the domain of education.

An Organizational and Governance Model to Support Mass Collaborative Learning Initiatives

“…Nevertheless, in the last decade, numerous studies have been published on MCL environments and a number of these studies focus mostly on specific models (e.g., network models), techniques (e.g., network analysis techniques), and approaches (e.g., network-based approaches) to support the supervision of online learning communities (e.g., MCL) [63]. On the other side, to cope with the current information explosion and information overload (which is a common and challenging issue in the MCL environment), the authors of [12] tried to take the advantage of the application of natural language processing (NLP). Another study [64] proposed firstorder probabilistic reasoning techniques (that benefit from machine-learning techniques) to estimate the quality of knowledge (e.g., consistency, relevance, and scalability) created and shared by mass collaborative efforts.…”

“…The fact is that MCL as an emerging approach is viewed from different perspectives (e.g., computer science, computational linguistics, network science, psychology, pedagogy, economics, knowledge management, and collaborative learning), and different researchers have different viewpoints about this new research field [12]. On the other side, the interdisciplinary nature of MCL coupled with the complexity of such a system would necessitate further investigation and broader collaboration among researchers toward acquiring a clearer and deeper understanding of the concept and related issues.…”

“…Over the last years, MC has made a profound impact on societies in the areas where it was applied [3]. In the context of education and learning, for example, the Massive Open Online Courses (MOOCs) initiative in recent years has provided free online courses available for anyone to enroll, with no limit on attendance [12]. Citizendium [18] is another example in which volunteer contributors take part in an open wiki project to dedicate themselves to creating a free, comprehensive, and reliable repository of structured knowledge under gentle expert oversight.…”

Meta-Governance Framework to Guide the Establishment of Mass Collaborative Learning Communities

“…Another important issue in the context of community assessment is dealing with big data. MCL (due to the size of the community and the number of learners) faces the challenge of handling the huge amount of content generated, uploaded, and shared [17,38]. On top of that, there is abundant knowledge, information, and data with different degrees of quality (e.g., non-relevant or of low quality), each coming from a different location [8].…”

“…On top of that, there is abundant knowledge, information, and data with different degrees of quality (e.g., non-relevant or of low quality), each coming from a different location [8]. To effectively cope with this problem, the literature proposes different potential techniques (e.g., natural language processing [38] and deep learning [39]) or technologies (e.g., smart systems and software) [40], but it remains a huge challenge.…”