%0 Conference Paper %1 zhang2012beyond %A Zhang, Haipeng %A Korayem, Mohammed %A You, Erkang %A Crandall, David J. %B Proceedings of the Fifth ACM International Conference on Web Search and Data Mining %C New York, NY, USA %D 2012 %I ACM %K geo inthesis spikes visualization similarity tag diss spatial temporal %P 33--42 %R 10.1145/2124295.2124302 %T Beyond Co-occurrence: Discovering and Visualizing Tag Relationships from Geo-spatial and Temporal Similarities %U http://doi.acm.org/10.1145/2124295.2124302 %X Studying relationships between keyword tags on social sharing websites has become a popular topic of research, both to improve tag suggestion systems and to discover connections between the concepts that the tags represent. Existing approaches have largely relied on tag co-occurrences. In this paper, we show how to find connections between tags by comparing their distributions over time and space, discovering tags with similar geographic and temporal patterns of use. Geo-spatial, temporal and geo-temporal distributions of tags are extracted and represented as vectors which can then be compared and clustered. Using a dataset of tens of millions of geo-tagged Flickr photos, we show that we can cluster Flickr photo tags based on their geographic and temporal patterns, and we evaluate the results both qualitatively and quantitatively using a panel of human judges. We also develop visualizations of temporal and geographic tag distributions, and show that they help humans recognize semantic relationships between tags. This approach to finding and visualizing similar tags is potentially useful for exploring any data having geographic and temporal annotations. %@ 978-1-4503-0747-5

@inproceedings{zhang2012beyond, abstract = {Studying relationships between keyword tags on social sharing websites has become a popular topic of research, both to improve tag suggestion systems and to discover connections between the concepts that the tags represent. Existing approaches have largely relied on tag co-occurrences. In this paper, we show how to find connections between tags by comparing their distributions over time and space, discovering tags with similar geographic and temporal patterns of use. Geo-spatial, temporal and geo-temporal distributions of tags are extracted and represented as vectors which can then be compared and clustered. Using a dataset of tens of millions of geo-tagged Flickr photos, we show that we can cluster Flickr photo tags based on their geographic and temporal patterns, and we evaluate the results both qualitatively and quantitatively using a panel of human judges. We also develop visualizations of temporal and geographic tag distributions, and show that they help humans recognize semantic relationships between tags. This approach to finding and visualizing similar tags is potentially useful for exploring any data having geographic and temporal annotations.}, acmid = {2124302}, added-at = {2017-02-03T14:19:52.000+0100}, address = {New York, NY, USA}, author = {Zhang, Haipeng and Korayem, Mohammed and You, Erkang and Crandall, David J.}, biburl = {https://www.bibsonomy.org/bibtex/21747dd94a19b75c436efb169e5e1bc9c/becker}, booktitle = {Proceedings of the Fifth ACM International Conference on Web Search and Data Mining}, doi = {10.1145/2124295.2124302}, interhash = {4e799cb5dffd825f7ac72c27970ea46f}, intrahash = {1747dd94a19b75c436efb169e5e1bc9c}, isbn = {978-1-4503-0747-5}, keywords = {geo inthesis spikes visualization similarity tag diss spatial temporal}, location = {Seattle, Washington, USA}, numpages = {10}, pages = {33--42}, publisher = {ACM}, series = {WSDM '12}, timestamp = {2022-05-07T08:05:32.000+0200}, title = {Beyond Co-occurrence: Discovering and Visualizing Tag Relationships from Geo-spatial and Temporal Similarities}, url = {http://doi.acm.org/10.1145/2124295.2124302}, year = 2012 }