TY - GEN
T1 - Graph-based methods for clustering topics of interest in Twitter
AU - Hromic, Hugo
AU - Prangnawarat, Narumol
AU - Hulpuş, Ioana
AU - Karnstedt, Marcel
AU - Hayes, Conor
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Online Social Media provides real-time information about events and news in the physical world. A challenging problem is then to identify in a timely manner the few relevant bits of information in these massive and fast-paced streams. Most of the current topic clustering and event detection methods focus on user generated content, hence they are sensible to language, writing style and are usually expensive to compute. Instead, our approach focuses on mining the structure of the graph generated by the interactions between users. Our hypothesis is that bursts in user interest for particular topics and events are reflected by corresponding changes in the structure of the discussion dynamics. We show that our method is capable of effectively identifying event topics in Twitter ground truth data, while offering better overall performance than a purely content-based method based on LDA topic models.
AB - Online Social Media provides real-time information about events and news in the physical world. A challenging problem is then to identify in a timely manner the few relevant bits of information in these massive and fast-paced streams. Most of the current topic clustering and event detection methods focus on user generated content, hence they are sensible to language, writing style and are usually expensive to compute. Instead, our approach focuses on mining the structure of the graph generated by the interactions between users. Our hypothesis is that bursts in user interest for particular topics and events are reflected by corresponding changes in the structure of the discussion dynamics. We show that our method is capable of effectively identifying event topics in Twitter ground truth data, while offering better overall performance than a purely content-based method based on LDA topic models.
UR - http://www.scopus.com/inward/record.url?scp=84937510010&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19890-3_61
DO - 10.1007/978-3-319-19890-3_61
M3 - Conference Publication
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 701
EP - 704
BT - Engineering the Web in the Big Data Era - 15th International Conference, ICWE 2015, Proceedings
A2 - Frasincar, Flavius
A2 - Houben, Geert-Jan
A2 - Cimiano, Philipp
A2 - Schwabe, Daniel
PB - Springer-Verlag
T2 - 15th International Conference on Web Engineering, ICWE 2015
Y2 - 23 June 2015 through 26 June 2015
ER -