Cognitive models in information retrieval—an evaluative review
Journal of Documentation
Elements of information theory
Elements of information theory
Relevance and contributing information types of searched documents in task performance
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Modeling cognitive processes in information seeking: from popper to pask
Journal of the American Society for Information Science and Technology - Special issue: Part II: Information seeking research
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Relevance judgment: What do information users consider beyond topicality?
Journal of the American Society for Information Science and Technology - Research Articles
Improving personalized web search using result diversification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Exploring the role of the reader in the activity of blogging
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding the efficiency of social tagging systems using information theory
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Query expansion using gaze-based feedback on the subdocument level
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Leveraging social context for searching social media
Proceedings of the 2008 ACM workshop on Search in social media
Proceedings of the Second ACM International Conference on Web Search and Data Mining
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Using twitter to recommend real-time topical news
Proceedings of the third ACM conference on Recommender systems
Ranking mechanisms in twitter-like forums
Proceedings of the third ACM international conference on Web search and data mining
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
DivRank: the interplay of prestige and diversity in information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Eddi: interactive topic-based browsing of social status streams
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Unfolding the event landscape on twitter: classification and exploration of user categories
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Learning to rank social update streams
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Personalized network updates: increasing social interactions and contributions in social networks
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Media-based social interaction patterns: a case study in an online civic mobilization
Proceedings of the 2012 international workshop on Socially-aware multimedia
ProfileRank: finding relevant content and influential users based on information diffusion
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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As users turn to large scale social media systems like Twitter for topic-based content exploration, they quickly face the issue that there may be hundreds of thousands of items matching any given topic they might query. Given the scale of the potential result sets, how does one identify the 'best' or 'right' set of items? We explore a solution that aligns characteristics of the information space, including specific content attributes and the information diversity of the results set, with measurements of human information processing, including engagement and recognition memory. Using Twitter as a test bed, we propose a greedy iterative clustering technique for selecting a set of items on a given topic that matches a specified level of diversity. In a user study, we show that our proposed method yields sets of items that were, on balance, more engaging, better remembered, and rated as more interesting and informative compared to baseline techniques. Additionally, diversity indeed seemed to be important to participants in the study in the consumption of content. However as a rather surprising result, we also observe that content was perceived to be more relevant when it was highly homogeneous or highly heterogeneous. In this light, implications for the selection and evaluation of topic-centric item sets in social media contexts are discussed.