Abstract. In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial effort. Users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages (tweets) and present different methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a large-scale evaluation of faceted search strategies, show significant improvements over keyword search and reveal significant benefits of those strategies that (i) further enrich the semantics of tweets by exploiting links posted in tweets, and that (ii) support users in selecting facet value pairs by adapting the faceted search interface to the specific needs and preferences of a user.

Paid crowdsourcing platforms have evolved into remarkable marketplaces where requesters can tap into human intelligence to serve a multitude of purposes, and the workforce can benefit through monetary returns for investing their efforts. In this work, we focus on individual crowd worker competencies. By drawing from self-assessment theories in psychology, we show that crowd workers often lack awareness about their true level of competence. Due to this, although workers intend to maintain a high reputation, they tend to participate in tasks that are beyond their competence. We reveal the diversity of individual worker competencies, and make a case for competence-based pre-selection in crowdsourcing marketplaces. We show the implications of flawed self-assessments on real-world microtasks, and propose a novel worker pre-selection method that considers accuracy of worker self-assessments. We evaluated our method in a sentiment analysis task and observed an improvement in the accuracy by over 15%, when compared to traditional performance-based worker pre-selection. Similarly, our proposed method resulted in an improvement in accuracy of nearly 6% in an image validation task. Our results show that requesters in crowdsourcing platforms can benefit by considering worker self-assessments in addition to their performance for pre-selection.

Abstract. Grouping is an attractive interaction metaphor for users to create reference collections of Web resources they are interested in. Each grouping activity has a certain semantics: things which were previously unrelated are now connected with others via the group. We present the GroupMe! application which allows users to group and arrange multimedia Web resources they are interested in. GroupMe! has an easy-to-use interface for gathering and grouping of resources, and allows users to tag everything they like. The semantics of any user interaction is captured, transformed and stored as adequate RDF descriptions. As an example application of this automatically derived RDF content, we show the enhancement of search for tagged Web resources, which evaluates the grouping information to deduce additional contextual information about the resources. GroupMe! is available via http://www.groupme.org.

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