Raluca Budiu

SparTag.us: a low cost tagging system for foraging of web content

Tagging systems such as del.icio.us and Diigo have become important ways for users to organize information gathered from the Web. However, despite their popularity among early adopters, tagging still incurs a relatively high interaction cost for the general users. We introduce a new tagging system called SparTag.us, which uses an intuitive Click2Tag technique to provide in situ, low cost tagging of web content. SparTag.us also lets users highlight text snippets and automatically collects tagged or highlighted paragraphs into a system-created notebook, which can be later browsed and searched. We report several user studies aimed at evaluating Click2Tag and SparTag.us.

Comprehending anaphoric metaphors.

In this study we investigate the comprehension of various kinds of anaphoric metaphors in context. We describe an experiment that manipulated the metaphoricity of simple noun + verb + ending sentences by using either a metaphoric noun or a metaphoric verb or both. Our results show that metaphoric nouns affect sentence comprehension to a greater extent than do metaphoric verbs. Thus, even though there were no sentence-reading time differences between metaphoric and literal targets, metaphoric nouns were read more slowly than were literal nouns, and they also affected the reading time of the following verb. Moreover, in trials involving metaphoric-noun targets, participants read the endings of the targets faster and made more mistakes in answering posttrial questions than they did in literal-noun trials. We argue that these results suggest a comprehension deficit for anaphoric noun metaphors even when they are preceded by a context.

Navigation in degree of interest trees

We present an experiment that compares how people perform search tasks in a degree-of-interest browser and in a Windows-Explorer-like browser. Our results show that, whereas users do attend to more information in the DOI browser, they do not complete the task faster than in an Explorer-like browser. However, in both types of browser, users are faster to complete high information scent search tasks than low information scent tasks. We present an ACT-R computational model of the search task in the DOI browser. The model describes how a visual search strategy may combine with semantic aspects of processing, as captured by information scent. We also describe a way of automatically estimating information scent in an ontological hierarchy by querying a large corpus (in our case, Googles corpus).