Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A general optimization framework for smoothing language models on graph structures
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Conversational tagging in twitter
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Social action tracking via noise tolerant time-varying factor graphs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Semi-supervised recognition of sarcastic sentences in Twitter and Amazon
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Context comparison of bursty events in web search and online media
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Survey on social tagging techniques
ACM SIGKDD Explorations Newsletter
A new perspective on Twitter hashtag use: diffusion of innovation theory
Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 47
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
Speak little and well: recommending conversations in online social streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
"Voluntweeters": self-organizing by digital volunteers in times of crisis
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Rumor has it: identifying misinformation in microblogs
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
On recommending hashtags in twitter networks
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
NE-Rank: A Novel Graph-Based Keyphrase Extraction in Twitter
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Improving LDA topic models for microblogs via tweet pooling and automatic labeling
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Learning to annotate tweets with crowd wisdom
Proceedings of the 22nd international conference on World Wide Web companion
Recommendation for online social feeds by exploiting user response behavior
Proceedings of the 22nd international conference on World Wide Web companion
Meaning as collective use: predicting semantic hashtag categories on twitter
Proceedings of the 22nd international conference on World Wide Web companion
Information sharing on Twitter during the 2011 catastrophic earthquake
Proceedings of the 22nd international conference on World Wide Web companion
Diffusion of innovations revisited: from social network to innovation network
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Exploiting entities in social media
Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrieval
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Researchers and social observers have both believed that hashtags, as a new type of organizational objects of information, play a dual role in online microblogging communities (e.g., Twitter). On one hand, a hashtag serves as a bookmark of content, which links tweets with similar topics; on the other hand, a hashtag serves as the symbol of a community membership, which bridges a virtual community of users. Are the real users aware of this dual role of hashtags? Is the dual role affecting their behavior of adopting a hashtag? Is hashtag adoption predictable? We take the initiative to investigate and quantify the effects of the dual role on hashtag adoption. We propose comprehensive measures to quantify the major factors of how a user selects content tags as well as joins communities. Experiments using large scale Twitter datasets prove the effectiveness of the dual role, where both the content measures and the community measures significantly correlate to hashtag adoption on Twitter. With these measures as features, a machine learning model can effectively predict the future adoption of hashtags that a user has never used before.