User-generated comments in online social media have recently been gaining increasing attention as a viable source of general-purpose descriptive annotations for digital objects like photos or videos. Because users have different levels of expertise, however, the quality of their comments can vary from very useful to entirely useless. Our aim is to provide automated support for the curation of useful user-generated comments from public collections of digital objects. After constructing a crowd-sourced gold standard of useful and not useful comments, we use standard machine learning methods to develop a usefulness classifier, exploring the impact of surface-level, syntactic, semantic, and topic-based features in addition to extra-linguistic attributes of the author and his or her social media activity. We then adapt an existing model of prevalence detection that uses the learned classifier to investigate patterns in the commenting culture of two popular social media platforms. We find that the prevalence of useful comments is platform-specific and is further influenced by the entity type of the media object being commented on (person, place, event), its time period (e.g., year of an event), and the degree of polarization among commenters.

This article surveys the use of empirical, machine-learning methods for a particular natural language-understanding task-information extraction. The author presents a generic architecture for information-extraction systems and then surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture.

This article surveys the use of empirical, machine-learning methods for a particular natural language-understanding task-information extraction. The author presents a generic architecture for information-extraction systems and then surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture.