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Big Data 2019 American Journal of Computer Science and Information Technology
ISSN: 2349-3917 Page 21
March 04-05, 2019
Barcelona, Spain
8th Edition of International Conference on
Big Data &
Data Science
The crime phenomenon of modern society is more complex
and diverse than in the past. There are many ways to
predict and analyze crime phenomena. The current era of the
fourth industrial revolution is experiencing innovative changes
as cutting-edge information and communications technology
are incorporated into all areas of the economy and society;
for example, artificial intelligence (AI), the Internet of Things,
big data, and mobile technology. Criminologists (crime-data
scientists) play a very important role in this process. They
create or assemble high-quality data that can be used to train
machine-learning systems, find machine-learning algorithms
that are suitable for the data, and perform modeling. The
discussions of politics, economy, and culture posted on social
media outlets represent the opinions of the era. The method
of collecting and analyzing the unstructured data from online
channels, including the Social Network Service, can interpret
the actual phenomenon in our society. The current study uses
structured and social big data to predict crime and preemptively
respond to it. The results of this study provides a detailed
description of the entire research process, which consisted
of gathering big data, analyzing it, and making observations
to develop a crime-prediction model that uses actual big data.
The study also contains an in-depth discussion of several
processes: text mining, which extracts useful information
from online documents; opinion mining, which analyzes the
emotions contained in documents; machine learning for crime
prediction and visualization analysis. Machine learning will be
applied to finally suggest a prediction model. The results of the
analysis and policy implication will be discussed.
Recent Publications
1. Song J, Song T M and Lee J (2018) Stay alert:
Forecasting the risks of sexting in Korea using social
big data. Computer in Human Behavior 81:294-301.
2. Song J, Song T M, Seo D-C, Jin D-L and Kim J S (2017)
Social Big Data Analysis of Information Spread and
Perceived Infection Risk During the 2015 Middle East
Respiratory Syndrome Outbreak in South Korea. Cyber
psychology, Behavior, and Social Networking 20(1):22-
29.
3. Song J, Song T M, Seo D C and Jin J (2016) Data
mining of web-based documents on social networking
sites that included suicide-related words among
Korean adolescents. Journal of Adolescent Health
59(6):668-673.
4. Juyoung Song and Taemin Song (2018) Crime
prediction using big data. Bullsbook Publishing Co.
Seoul, Korea.
5. Taemin Song and Juyoung Song (2016) Social Big Data
Research Methodology with R, Hannarae Publishing
Co, Seoul, Korea.
Biography
Juyoung Song is an Assistant Professor of Criminal Justice and Criminolo-
gy at Pennsylvania State University. She has completed her Bachelors and
Master’s degrees in the College of Lawat Hanyang University in Seoul, South
Korea, and her Doctorate degree in Criminal Justice at Michigan State Uni-
versity. Her Career appointments have included an Assistant Professor at
the University of West Georgia, and an Associate Researcher at the Korean
Institute of Criminology. She has presented at numerous national and inter-
national conferences about “Big Data” and published several articles on big
data analysis. She has recently published five books about big data analysis
in Korean and is currently working on, “Crime Prediction Using Big Data in
English.”
Juyoung Song
Pennsylvania State University, USA
Juyoung Song, Am J Compt Sci Inform Technol 2019, Volume 7
DOI: 10.21767/2349-3917-C1-008
Crime prediction using social big data and
machine learning