Adaptive Moderation of User-Generated Content on Web

Roochi Elaheh Momeni
2014 unpublished
User-generated content on the Web, and particularly in social media platforms, facilitates the augmentation of additional information with digital resources and delivers valuable information. However, some user-generated content (UGC) is not useful due to the varying intentions of authors of content and perspectives of viewers. This raises the challenge of how to maximize its value for platform viewers. Currently, the majority of available approaches tends to train an assessment and ranking
more » ... oach for maximizing various values such as usefulness, relevancy, or credibility for a platform's viewers. However, most of these approaches rely on particular sources of ground truth and do not enable moderation requesters to make adaptive assessments of a particular value. Accordingly, there is insufficient consideration of approaches which are adaptive for individual users. Many of the available approaches do not enable individual requesters to adapt a moderation to their requirements. In the attempt to overcome this challenge, this thesis aims to provide researchers and Web data curators with a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused on automated moderation of user-generated content. Accordingly, an adaptive moderation framework is proposed. It is a semi-supervised approach which semantically enriches and clusters content along multiple explicit semantic facets (e.g., subjectivity, informative, and topics) and enables users to explore different facets and select combinations of facets in order to extract and rank content that matches their interests. The development of this framework is the result of the following investigations. First, a systematic review of approaches for assessing and ranking of UGC has been conducted, producing results which have been obtained by gathering and comparing existing approaches. These are grouped in three categories: Community-based assessment and ranking of UGC, Single-user assessment and ra [...]
doi:10.25365/thesis.33946 fatcat:v6e5r4g7zbggnfhapxgdyujsti