%0 Conference Paper %1 citeulike:13565852 %A Xu, Jiejun %A Lu, Tsai C. %B Proceedings of the 20th International Conference on Intelligent User Interfaces %C New York, NY, USA %D 2015 %I ACM %K crowdsourcing information-visualzation microblogging xwpaws %P 62--66 %R 10.1145/2678025.2701385 %T Seeing the Big Picture from Microblogs: Harnessing Social Signals for Visual Event Summarization %U http://dx.doi.org/10.1145/2678025.2701385 %X We propose an approach to automatically select a set of representative images to generate a concise visual summary of a real-world event from the Tumblr microblogging platform. Central to our approach is a unified graph model with heterogeneous nodes and edges to capture the interrelationship among various entities (e.g., users, posts, images, and tags) in online social media. With the graph representation, we then cast the summarization problem as a graph-based ranking problem by identifying the most representative images regarding to an event. The intuition behind our work is that not only can we crowdsource social media users as sensors to capture and share data, but we can also use them as filters to identify the most useful information through analyzing their interaction in the microblogging network. In addition, we propose a greedy algorithm to encourage diversity among top ranked results for the generation of temporal highlights of targeted events. Our approach is flexible to support different query tasks and is adaptable to additional graph entities and relationships. %@ 978-1-4503-3306-1

@inproceedings{citeulike:13565852, abstract = {{We propose an approach to automatically select a set of representative images to generate a concise visual summary of a real-world event from the Tumblr microblogging platform. Central to our approach is a unified graph model with heterogeneous nodes and edges to capture the interrelationship among various entities (e.g., users, posts, images, and tags) in online social media. With the graph representation, we then cast the summarization problem as a graph-based ranking problem by identifying the most representative images regarding to an event. The intuition behind our work is that not only can we crowdsource social media users as sensors to capture and share data, but we can also use them as filters to identify the most useful information through analyzing their interaction in the microblogging network. In addition, we propose a greedy algorithm to encourage diversity among top ranked results for the generation of temporal highlights of targeted events. Our approach is flexible to support different query tasks and is adaptable to additional graph entities and relationships.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Xu, Jiejun and Lu, Tsai C.}, biburl = {https://www.bibsonomy.org/bibtex/22d2565110dea139247d845b1dab38f4b/aho}, booktitle = {Proceedings of the 20th International Conference on Intelligent User Interfaces}, citeulike-article-id = {13565852}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2701385}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/2678025.2701385}, doi = {10.1145/2678025.2701385}, interhash = {a7d1e385ce6fb6812aab19e2a87fe6bf}, intrahash = {2d2565110dea139247d845b1dab38f4b}, isbn = {978-1-4503-3306-1}, keywords = {crowdsourcing information-visualzation microblogging xwpaws}, location = {Atlanta, Georgia, USA}, pages = {62--66}, posted-at = {2015-03-30 17:22:17}, priority = {2}, publisher = {ACM}, series = {IUI '15}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Seeing the Big Picture from Microblogs: Harnessing Social Signals for Visual Event Summarization}}, url = {http://dx.doi.org/10.1145/2678025.2701385}, year = 2015 }