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Real-time Adaptive Crawler for Tracking Unfolding Events on Twitter
Asmelash Teka Hadgu
author
Sallam Abualhaija
author
Claudia Niederée
author
2019
Iscram
Valencia, Spain
English
When a major event such as a crisis situation occurs, people post messages on social media sites such as Twitter, in
order to exchange information or to share emotions. These posts can provide useful information to raise situation
awareness and support decision making, e.g., by aid organizations. In this paper, we propose a novel method for
social media crawling, which exploits a Bayesian inference framework to keep track of keyword changes over time
and uses a counter-stream to gauge the inclusion of noise and irrelevant information. In addition, we present a
framework to evaluate real-time adaptive social search algorithms in a reproducible manner, which relies on a
semi-automated approach for ground-truth construction. We show that our method outperforms previous methods
for very large scale events.
social search
real-time adaptive search
event tracking
crsis communication
exported from refbase (http://idl.iscram.org/show.php?record=1985), last updated on Fri, 22 Nov 2019 12:03:17 +0100
text
http://idl.iscram.org/files/asmelashtekahadgu/2019/1985_AsmelashTekaHadgu_etal2019.pdf
AsmelashTekaHadgu_etal2019
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management
Iscram 2019
Franco
Z
editor
González
J
J
editor
Canós
J
H
editor
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
2019
Iscram
Valencia, Spain
conference publication
978-84-09-10498-7
2411-3387
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