This paper presents our efforts to detect Concept Drifts (changes in data generation processes), using the Cross-Recurrence Quantification Analysis, on time series produced by social network systems. Experiments were performed on the TSViz project (http://www.tsviz.com.br), which collects online tweets associated with predefined hashtags and processes them to generate different time series: one to measure the amount of information contained in textual short messages and another to quantify the positiveness and negativeness of users’ sentiments, etc. In that context, this work proposed and evaluated a Concept Drift approach to point out when generating processes change along time, indicating the detection of relevant textual changes in terms of the amount of information and sentiments. As a main contribution, results show that our approach indicates when the most important social events happen, which were confirmed by official news.
References
1.
K. T.
Alligood
, T. D.
Sauer
, and J. A.
Yorke
, Chaos: An Introduction to Dynamical Systems
(Springer-Verlag
, 1997
).2.
C. M.
Bishop
, Pattern Recognition and Machine Learning (Information Science and Statistics)
(Springer-Verlag New York, Inc.
, Secaucus, NJ
, 2006
).3.
M.
Bouazizi
and T.
Ohtsuki
, “Opinion mining in twitter how to make use of sarcasm to enhance sentiment analysis
,” in IEEE/ACM ASONAM
(ACM
, 2015
), pp. 1594
–1597
.4.
F. G.
da Costa
, R. A.
Rios
, and R. F.
de Mello
, “Using dynamical systems tools to detect concept drift in data streams
,” Expert Syst. Appl.
60
, 39
–50
(2016
). 5.
L.
de Carvalho Pagliosa
and R. F.
de Mello
, “Applying a kernel function on time-dependent data to provide supervised-learning guarantees
,” Expert Syst. Appl.
71
, 216
–229
(2017
). 6.
J. P.
Eckmann
, O. S.
Kamphorst
, and D.
Ruelle
, “Recurrence plots of dynamical systems
,” Europhys. Lett.
4
, 973
–977
(1987
). 7.
A. M.
Fraser
and H. L.
Swinney
, “Independent coordinates for strange attractors from mutual information
,” Phys. Rev. A
33
, 1134
–1140
(1986
). 8.
J.
Gama
, I.
Žliobaitė
, A.
Bifet
, M.
Pechenizkiy
, and A.
Bouchachia
, “A survey on concept drift adaptation
,” ACM Comput. Surv.
46
(4
), 44:1
–44:37
(2014
). 9.
W.
Gao
and F.
Sebastiani
, “Tweet sentiment: From classification to quantification
,” in IEEE/ACM ASONAM
(ACM
, 2015
), pp. 97
–104
.10.
A.
Gupta
, P.
Kumaraguru
, C.
Castillo
, and P.
Meier
, “TweetCred: A real-time web-based system for assessing credibility of content on twitter
,” in CoRR
(2014
).11.
H.
Kantz
and T.
Schreiber
, Nonlinear Time Series Analysis
(Cambridge University Press
, Cambridge
, 1997
), Vol. 7.12.
M. B.
Kennel
, R.
Brown
, and H. D. I.
Abarbanel
, “Determining embedding dimension for phase-space reconstruction using a geometrical construction
,” Phys. Rev. A
45
, 3403
–3411
(1992
). 13.
S.
Kumar
, H.
Liu
, S.
Mehta
, and L.
Venkata Subramaniam
, “Exploring a scalable solution to identifying events in noisy twitter streams
,” in IEEE/ACM ASONAM
(ACM
, 2015
), pp. 496
–499
.14.
H.
Lamba
, M. M.
Malik
, and J.
Pfeffer
, “A tempest in a teacup? Analyzing firestorms on twitter
,” in IEEE/ACM ASONAM
(ACM
, 2015
), pp. 17
–24
.15.
M.
Li
, X.
Chen
, X.
Li
, B.
Ma
, and P. M. B.
Vitanyi
, “The similarity metric
,” Trans. Info. Theory
50
(12
), 3250
–3264
(2004
). 16.
N.
Marwan
, M.
Romano
, M.
Thiel
, and J.
Kurths
, “Recurrence plots for the analysis of complex systems
,” Phys. Rep.
438
(5–6
), 237
–329
(2007
). 17.
18.
A. N.
Pettitt
, “A non-parametric approach to the change-point problem
,” Appl. Stat.
28
(2
), 126
–135
(1979
). 19.
R. A.
Rios
, C. S.
Lopes
, F. H. G.
Sikansi
, P. A.
Pagliosa
, and R. F.
de Mello
, “Analyzing the public opinion on the Brazilian political and corruption issues
,” in BRACIS 2017
(IEEE
, 2017
), pp. 13
–18
.20.
R. A.
Rios
, P. A.
Pagliosa
, R. P.
Ishii
, and R. F.
de Mello
, “Tsviz: A data stream architecture to online collect, analyze, and visualize tweets
,” in SAC'17
(ACM
, New York, NY
, 2017
), pp. 1031
–1036
.21.
J.
Serrà
, X.
Serra
, and R. G
Andrzejak
, “Cross recurrence quantification for cover song identification
,” New J. Phys.
11
, 093017
(2009
). 22.
C. E.
Shannon
, “A mathematical theory of communication
,” The Bell System Technical Journal
27
(3
), 379
–423
(1948
). 23.
F.
Takens
, “Detecting strange attractors in turbulence
,” in Dynamical Systems and Turbulence, Warwick (1980)
, (Springer, Berlin, Heidelberg
, 1981
), Vol. 898, pp. 366
–381
.24.
J. P.
Zbilut
, A.
Giuliani
, and C. L.
Webber
, “Detecting deterministic signals in exceptionally noisy environments using cross-recurrence quantification
,” Phys. Lett. A
246
(1
), 122
–128
(1998
). 25.
J. P.
Zbilut
and C. L.
Webber, Jr.
, “Embeddings and delays as derived from quantification of recurrence plots
,” Phys. Lett. A
171
(3–4
), 199
–203
(1992
). 26.
J.
Zou
, F.
Fekri
, and S. W.
McLaughlin
, “Mining streaming tweets for real-time event credibility prediction in twitter
,” in IEEE/ACM ASONAM
(ACM
, 2015
), pp. 1586
–1589
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