Geographic information from georeferenced social media data
SIGSPATIAL Special
Trend-based and reputation-versed personalized news network
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Identifying content for planned events across social media sites
Proceedings of the fifth ACM international conference on Web search and data mining
Unfolding the event landscape on twitter: classification and exploration of user categories
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Expert Systems with Applications: An International Journal
Dynamical classes of collective attention in twitter
Proceedings of the 21st international conference on World Wide Web
Mining whining in support forums with frictionary
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Trending Twitter topics in English: An international comparison
Journal of the American Society for Information Science and Technology
Expert Systems with Applications: An International Journal
Spotting trends: the wisdom of the few
Proceedings of the sixth ACM conference on Recommender systems
Modeling topic trends on the social web using temporal signatures
Proceedings of the twelfth international workshop on Web information and data management
Identifying event-related bursts via social media activities
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Trend makers and trend spotters in a mobile application
Proceedings of the 2013 conference on Computer supported cooperative work
Curating and contextualizing Twitter stories to assist with social newsgathering
Proceedings of the 2013 international conference on Intelligent user interfaces
Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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
Automatic selection of social media responses to news
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Real time discussion retrieval from twitter
Proceedings of the 22nd international conference on World Wide Web companion
Exploring the impact of communication on innovation
International Journal of Business Information Systems
Identifying dynamics and collective behaviors in microblogging traces
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
Analysis and forecasting of trending topics in online media streams
Proceedings of the 21st ACM international conference on Multimedia
Journal of Information Science
Traveling trends: social butterflies or frequent fliers?
Proceedings of the first ACM conference on Online social networks
Are Some Tweets More Interesting Than Others? #HardQuestion
Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval
Hi-index | 0.00 |
Twitter, Facebook, and other related systems that we call social awareness streams are rapidly changing the information and communication dynamics of our society. These systems, where hundreds of millions of users share short messages in real time, expose the aggregate interests and attention of global and local communities. In particular, emerging temporal trends in these systems, especially those related to a single geographic area, are a significant and revealing source of information for, and about, a local community. This study makes two essential contributions for interpreting emerging temporal trends in these information systems. First, based on a large dataset of Twitter messages from one geographic area, we develop a taxonomy of the trends present in the data. Second, we identify important dimensions according to which trends can be categorized, as well as the key distinguishing features of trends that can be derived from their associated messages. We quantitatively examine the computed features for different categories of trends, and establish that significant differences can be detected across categories. Our study advances the understanding of trends on Twitter and other social awareness streams, which will enable powerful applications and activities, including user-driven real-time information services for local communities. © 2011 Wiley Periodicals, Inc.