=Paper=
{{Paper
|id=Vol-2078/keynote
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2078/keynote.pdf
|volume=Vol-2078
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==None==
https://ceur-ws.org/Vol-2078/keynote.pdf
Graph-Based Event Detection in Streams: The
Twitter Case
Michalis Vazirgiannis
LIX, Ecole Polytechnique, France
mvazirg@lix.polytechnique.fr
Abstract
Due to its instantaneous nature, Twitter has been established as a major com-
munication medium. Among others, people use the service to report latest news
and to comment about real-world events. Users show particular interest in social
events such as large parties, political campaigns, and sporting events, but also
for emergency events such as natural disasters and terrorist attacks. Automated
and real-time event detection in this case is an interesting challenge. We present
our work on this topic capitalizing on modeling the stream as an evolving graph
of words, and then detecting events based on their evolution patterns. To iden-
tify important moments, the system detects rapid changes in the graphs edge
weights using a convex optimization formulation. Then we need to summarize
the event in the best feasible way. We present a method that generates real-time
summaries of events using only posts collected from Twitter. 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 non-decreasing sub-modular
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.
Biography
Dr. Vazirgiannis is a Professor at LIX, Ecole Polytechnique in France and leads
the Data Science and Mining group. He holds a degree in Physics and a PhD
in Informatics from Athens University (Greece), and a Master degree in AI
from HerioWatt Univ Edinburgh. He has conducted research in GMD-IPSI, Max
Planck MPI (Germany), and in INRIA/FUTURS (Paris). He has been teach-
ing in AUEB (Greece), Ecole Polytechnique, Telecom-Paristech, ENS (France),
Tsinghua, Jiaotong Shanghai (China) and in Deusto University (Spain). His cur-
rent research interests are on machine learning and combinatorial methods for
Graph analysis (including community detection, graph clustering and embed-
dings, influence maximization), text mining including Graph of Words, word
embeddings with applications to web advertising and marketing, and event de-
tection and summarization. He has active cooperation with industrial partners in
the area of data analytics and machine learning for large scale data repositories in
BroDyn 2018: 1st Workshop on Analysis of Broad Dynamic Topics over Social Media @ ECIR 2018, Grenoble, France 9
different application domains. He has supervised fifteen completed PhD disserta-
tions. He has published three books and more than 160 papers in international
refereed journals and conferences. He has organized large scale conferences in
the area of Data Mining and Machine Learning (such as ECML/PKDD) while
he participates in the senior PC of AI and ML conferences, e.g., AAAI and
IJCAI. He has received the ERCIM and the Marie Curie EU fellowships, the
Tencent “Rhino-Bird International Academic Expert Award” in 2017 and since
2015 he leads the AXA Data Science1 chair. More information can be found at:
http://www.lix.polytechnique.fr/dascim.
1
http://www.lix.polytechnique.fr/dascim/dascis
10