Masters Theses
Keywords and Phrases
Crime; Sentiment analysis; Twitter
Abstract
"In this research, we show online social networks can be used to study crime detection problems. Crime is defined as an act harmful not only to the individual involved, but also to the community as a whole. It is also a forbidden act that is punishable by law. Crimes are social nuisances that place heavy financial burdens on society. Here we look at use of data mining followed by sentiment analysis on online social networks, to help detect the crime patterns. Twitter is an online social networking and microblogging service that enables users to post brief text updates, also referred to as "tweets". These updates can convey important information about the author. A filter was designed to extract tweets from cities deemed to be either the most dangerous or the safest in the United States (US). A geographic analysis revealed a correlation between these tweets and the crimes that occurred in the corresponding cities. Over 100,000 crime-related tweets were collected over a period of 20 days. Sentiment analysis techniques were conducted on these tweets to analyze the crime intensity of a particular location. This type of study will help reveal the crime rate of a location in real-time. Although the results of this test helped in detecting crime patterns, the sentiment analysis techniques did not always guarantee the proper results. We conclude with applications of this type of study and how it can be improved by applying media to text processing techniques"--Abstract, page iii.
Advisor(s)
Chellappan, Sriram
Committee Member(s)
Jiang, Wei
Yin, Zhaozheng
Department(s)
Computer Science
Degree Name
M.S. in Computer Science
Publisher
Missouri University of Science and Technology
Publication Date
Fall 2014
Pagination
viii, 35 pages
Note about bibliography
Includes bibliographical references (pages 33-34).
Rights
© 2014 Raja Ashok Bolla, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Online social networks -- Political aspectsSocial media -- Political aspectsCrime preventionCriminal investigation
Thesis Number
T 10575
Electronic OCLC #
902730358
Recommended Citation
Bolla, Raja Ashok, "Crime pattern detection using online social media" (2014). Masters Theses. 7321.
https://scholarsmine.mst.edu/masters_theses/7321
