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  • × author_ss:"Gorrell, G."
  1. Gorrell, G.; Eaglestone, B.; Ford, N.; Holdridge, P.; Madden, A.: Towards "metacognitively aware" IR systems : an initial user study (2009) 0.00 
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    Abstract
    Purpose - The purpose of this paper is to describe: a new taxonomy of metacognitive skills designed to support the study of metacognition in the context of web searching; a data collection instrument based on the taxonomy; and the results of testing the instrument on a sample of university students and staff. Design/methodology/approach - The taxonomy is based on a review of the literature, and is extended to cover web searching. This forms the basis for the design of the data collection instrument, which is tested with 405 students and staff of Sheffield University. Findings - Subjects regard the range of metacognitive skills focused on as broadly similar. However, a number of significant differences in reported metacognition usage relating to age, gender and discipline. Practical implications - These findings contribute to the long-term aims of the research which are to: develop a model of the actual and potential role of metacognition in web searching, and identify strategic "metacognitive interventions" that can be built into an intelligent information retrieval system, driven by the model, capable of enhancing retrieval effectiveness by compensating for metacognitive weaknesses on the part of the searcher. Originality/value - The value of the paper lies in: the consideration of metacognition in the context of web searching, the presentation of an extensible taxonomy of metacognitive skills, development and testing of a prototype metacognitive inventory, finding of significant differences in reported metacognition usage according to age, gender and discipline, and reflection of the implications of the results for future research into web searching.
    Type
    a
  2. Gorrell, G.; Bontcheva, K.: Classifying Twitter favorites : Like, bookmark, or Thanks? (2016) 0.00 
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    Abstract
    Since its foundation in 2006, Twitter has enjoyed a meteoric rise in popularity, currently boasting over 500 million users. Its short text nature means that the service is open to a variety of different usage patterns, which have evolved rapidly in terms of user base and utilization. Prior work has categorized Twitter users, as well as studied the use of lists and re-tweets and how these can be used to infer user profiles and interests. The focus of this article is on studying why and how Twitter users mark tweets as "favorites"-a functionality with currently poorly understood usage, but strong relevance for personalization and information access applications. Firstly, manual analysis and classification are carried out on a randomly chosen set of favorited tweets, which reveal different approaches to using this functionality (i.e., bookmarks, thanks, like, conversational, and self-promotion). Secondly, an automatic favorites classification approach is proposed, based on the categories established in the previous step. Our machine learning experiments demonstrate a high degree of success in matching human judgments in classifying favorites according to usage type. In conclusion, we discuss the purposes to which these data could be put, in the context of identifying users' patterns of interests.
    Type
    a

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