Viewing morphology as an inference process
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
WordNet: a lexical database for English
Communications of the ACM
Analysis of a very large web search engine query log
ACM SIGIR Forum
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Information re-retrieval: repeat queries in Yahoo's logs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
"I know what you did last summer": query logs and user privacy
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Entropy of search logs: how hard is search? with personalization? with backoff?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Finding question-answer pairs from online forums
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Towards a model of understanding social search
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Information Seeking Can Be Social
Computer
Online expansion of rare queries for sponsored search
Proceedings of the 18th international conference on World wide web
What do people ask their social networks, and why?: a survey study of status message q&a behavior
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Questions are content: a taxonomy of questions in a microblogging environment
Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 47
#TwitterSearch: a comparison of microblog search and web search
Proceedings of the fourth ACM international conference on Web search and data mining
Question identification on twitter
Proceedings of the 20th ACM international conference on Information and knowledge management
Asking questions of targeted strangers on social networks
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Bursty event detection from collaborative tags
World Wide Web
Claude E. Shannon: a retrospective on his life, work, and impact
IEEE Transactions on Information Theory
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Conventional studies of online information seeking behavior usually focus on the use of search engines or question answering (Q&A) websites. Recently, the fast growth of online social platforms such as Twitter and Facebook has made it possible for people to utilize them for information seeking by asking questions to their friends or followers. We anticipate a better understanding of Web users' information needs by investigating research questions about these questions. How are they distinctive from daily tweeted conversations? How are they related to search queries? Can users' information needs on one platform predict those on the other? In this study, we take the initiative to extract and analyze information needs from billions of online conversations collected from Twitter. With an automatic text classifier, we can accurately detect real questions in tweets (i.e., tweets conveying real information needs). We then present a comprehensive analysis of the large-scale collection of information needs we extracted. We found that questions being asked on Twitter are substantially different from the topics being tweeted in general. Information needs detected on Twitter have a considerable power of predicting the trends of Google queries. Many interesting signals emerge through longitudinal analysis of the volume, spikes, and entropy of questions on Twitter, which provide insights to the understanding of the impact of real world events and user behavioral patterns in social platforms.