TY - GEN
T1 - Visual analysis of news streams with article threads
AU - Krstajic, Miloš
AU - Bertini, Enrico
AU - Mansmann, Florian
AU - Keim, Daniel A.
PY - 2010
Y1 - 2010
N2 - The analysis of large quantities of news is an emerging area in the field of data analysis and visualization. International agencies collect thousands of news every day from a large number of sources and making sense of them is becoming increasingly complex due to the rate of the incoming news, as well as the inherent complexity of analyzing large quantities of evolving text corpora. Current visual techniques that deal with temporal evolution of such complex datasets, together with research efforts in related domains like text mining and topic detection and tracking, represent early attempts to understand, gain insight and make sense of these data. Despite these initial propositions, there is still a lack of techniques dealing directly with the problem of visualizing news streams in a "on-line" fashion, that is, in a way that the evolution of news can be monitored in real-time by the operator. In this paper we propose a purely visual technique that permits to see the evolution of news in real-time. The technique permits to show the stream of news as they enter into the system as well as a series of important threads which are computed on the fly. By merging single articles into threads, the technique permits to offload the visualization and retain only the most relevant information. The proposed technique is applied to the visualization of news streams generated by a news aggregation system that monitors over 4000 sites from 1600 key news portals world-wide and retrieves over 80000 reports per day in 43 languages.
AB - The analysis of large quantities of news is an emerging area in the field of data analysis and visualization. International agencies collect thousands of news every day from a large number of sources and making sense of them is becoming increasingly complex due to the rate of the incoming news, as well as the inherent complexity of analyzing large quantities of evolving text corpora. Current visual techniques that deal with temporal evolution of such complex datasets, together with research efforts in related domains like text mining and topic detection and tracking, represent early attempts to understand, gain insight and make sense of these data. Despite these initial propositions, there is still a lack of techniques dealing directly with the problem of visualizing news streams in a "on-line" fashion, that is, in a way that the evolution of news can be monitored in real-time by the operator. In this paper we propose a purely visual technique that permits to see the evolution of news in real-time. The technique permits to show the stream of news as they enter into the system as well as a series of important threads which are computed on the fly. By merging single articles into threads, the technique permits to offload the visualization and retain only the most relevant information. The proposed technique is applied to the visualization of news streams generated by a news aggregation system that monitors over 4000 sites from 1600 key news portals world-wide and retrieves over 80000 reports per day in 43 languages.
KW - Data streaming
KW - News analysis
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=77956242707&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956242707&partnerID=8YFLogxK
U2 - 10.1145/1833280.1833286
DO - 10.1145/1833280.1833286
M3 - Conference contribution
AN - SCOPUS:77956242707
SN - 9781450302265
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 39
EP - 46
BT - Proc. of the 1st International Workshop on Novel Data Stream Pattern Mining Techniques, StreamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
T2 - 1st International Workshop on Novel Data Stream Pattern Mining Techniques, StreamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Y2 - 25 July 2010 through 25 July 2010
ER -