TY - GEN
T1 - An optimization approach for sub-event detection and summarization in twitter
AU - Meladianos, Polykarpos
AU - Xypolopoulos, Christos
AU - Nikolentzos, Giannis
AU - Vazirgiannis, Michalis
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In this paper, we present a system that generates real-time summaries of events using only posts collected from Twitter. The system both identifies important moments within the event and generates a corresponding textual description. First, the set of tweets posted in a short time interval is represented as a weighted graph-of-words. To identify important moments within an event, the system detects rapid changes in the graphs’ edge weights using a convex optimization formulation. The system then extracts a few tweets that best describe the chain of interesting occurrences in the event using a greedy algorithm that maximizes a nondecreasing submodular function. Through extensive experiments on real-world sporting events, we show that the proposed system can effectively capture the sub-events, and that it clearly outperforms the dominant sub-event detection method.
AB - In this paper, we present a system that generates real-time summaries of events using only posts collected from Twitter. The system both identifies important moments within the event and generates a corresponding textual description. First, the set of tweets posted in a short time interval is represented as a weighted graph-of-words. To identify important moments within an event, the system detects rapid changes in the graphs’ edge weights using a convex optimization formulation. The system then extracts a few tweets that best describe the chain of interesting occurrences in the event using a greedy algorithm that maximizes a nondecreasing submodular function. Through extensive experiments on real-world sporting events, we show that the proposed system can effectively capture the sub-events, and that it clearly outperforms the dominant sub-event detection method.
U2 - 10.1007/978-3-319-76941-7_36
DO - 10.1007/978-3-319-76941-7_36
M3 - Conference contribution
AN - SCOPUS:85044453632
SN - 9783319769400
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 481
EP - 493
BT - Advances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings
A2 - Azzopardi, Leif
A2 - Pasi, Gabriella
A2 - Hanbury, Allan
A2 - Piwowarski, Benjamin
PB - Springer Verlag
T2 - 40th European Conference on Information Retrieval, ECIR 2018
Y2 - 26 March 2018 through 29 March 2018
ER -