skip to main content
10.1145/3347146.3359082acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
demonstration

Witnessing Crime through Tweets: A Crime Investigation Tool based on Social Media

Published: 05 November 2019 Publication History

Abstract

The vast and growing amount of publicly available real-time information from social network services such as Twitter could provide many benefits for improving public health and safety, especially towards the area of crime prevention. While prior studies have leveraged such data to help in the prediction of criminal incidents, we have developed a crime investigation tool which utilizes Twitter data to aid in crime analysis. The tool provides contextual information about crime incidents by visualizing the spatial and time-based characteristics of a crime and its context using data from nearby tweets and from the criminal history of a target place. In addition, sentiment analysis is also carried out with the identified tweets to further examine the negative characteristics of the spatial areas related to the different crimes in question. A demonstration prototype of this tool was developed as a web application for the area of San Francisco.

References

[1]
Harshavardhan Achrekar, Avinash Gandhe, Ross Lazarus, Ssu-Hsin Yu, and Benyuan Liu. 2011. Predicting flu trends using twitter data. In Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on. IEEE, 702--707.
[2]
Harald Bosch, Dennis Thom, Michael Wörner, Steffen Koch, Edwin Püttmann, Dominik Jäckle, and Thomas Ertl. 2011. Scatterblogs: Geo-spatial document analysis. In IEEE Conference on Visual Analytics Science and Technology. 309--310.
[3]
Spencer Chainey, Lisa Tompson, and Sebastian Uhlig. 2008. The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal 21 (2008), 4--28.
[4]
Brian Heredia, Taghi M Khoshgoftaar, Joseph Prusa, and Michael Crawford. 2016. Cross-domain sentiment analysis: an empirical investigation. In Information Reuse and Integration (IRI), 2016 IEEE 17th International Conference on. IEEE, 160--165.
[5]
Rui Li, Kin Hou Lei, Ravi Khadiwala, and Kevin Chen-Chuan Chang. 2012. Tedas: A twitter-based event detection and analysis system. In 2012 IEEE 28th International Conference on Data Engineering. IEEE, 1273--1276.
[6]
Alan M MacEachren, Anuj Jaiswal, Anthony C Robinson, Scott Pezanowski, Alexander Savelyev, Prasenjit Mitra, Xiao Zhang, and Justine Blanford. 2011. Sense-place2: Geotwitter analytics support for situational awareness. In IEEE conference on visual analytics science and technology. IEEE, 181--190.
[7]
Xiaofeng Wang, Matthew S Gerber, and Donald E Brown. 2012. Automatic Crime Prediction Using Events Extracted from Twitter Posts. SBP 12 (2012), 231--238.
[8]
James Q Wilson and George L Kelling. 1982. Broken windows. Atlantic monthly 249, 3 (1982), 29--38.

Cited By

View all
  • (2023)Legal Events Classification in Online Social Community Posts2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C60940.2023.00044(350-355)Online publication date: 22-Oct-2023
  • (2023)A Comprehensive Review on Crime Patterns and Trends Analysis using Machine Learning2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)10.1109/ICAISS58487.2023.10250664(732-736)Online publication date: 23-Aug-2023
  • (2023)Measuring social media impact on Impulse Buying BehaviorCogent Business & Management10.1080/23311975.2023.226237110:3Online publication date: Oct-2023
  • Show More Cited By

Index Terms

  1. Witnessing Crime through Tweets: A Crime Investigation Tool based on Social Media

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2019
    648 pages
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 05 November 2019

    Check for updates

    Author Tags

    1. Crime Investigation
    2. Social media
    3. Spatio-Temporal Visualization

    Qualifiers

    • Demonstration
    • Research
    • Refereed limited

    Funding Sources

    • Swiss National Science Foundation
    • Kakenhi
    • MIC SCOPE

    Conference

    SIGSPATIAL '19
    Sponsor:

    Acceptance Rates

    SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;
    Overall Acceptance Rate 257 of 1,238 submissions, 21%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)51
    • Downloads (Last 6 weeks)7
    Reflects downloads up to 06 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Legal Events Classification in Online Social Community Posts2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C60940.2023.00044(350-355)Online publication date: 22-Oct-2023
    • (2023)A Comprehensive Review on Crime Patterns and Trends Analysis using Machine Learning2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)10.1109/ICAISS58487.2023.10250664(732-736)Online publication date: 23-Aug-2023
    • (2023)Measuring social media impact on Impulse Buying BehaviorCogent Business & Management10.1080/23311975.2023.226237110:3Online publication date: Oct-2023
    • (2023)Smart vehicles networks: BERT self-attention mechanisms for cyber-physical system securityInternational Journal of System Assurance Engineering and Management10.1007/s13198-023-02065-1Online publication date: 27-Jul-2023
    • (2022)Crime Prediction and Monitoring in Porto, Portugal, Using Machine Learning, Spatial and Text AnalyticsISPRS International Journal of Geo-Information10.3390/ijgi1107040011:7(400)Online publication date: 14-Jul-2022
    • (2022)Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A SurveyACM Computing Surveys10.1145/352449855:4(1-38)Online publication date: 21-Nov-2022
    • (2021)The FGLOCTweet Corpus: An English tweet-based corpus for fine-grained location-detection tasksResearch in Corpus Linguistics10.32714/ricl.10.01.0610:1(117-133)Online publication date: 2021
    • (2021)Review of Learning-Based Techniques of Sentiment Analysis for Security PurposesInnovations in Smart Cities Applications Volume 410.1007/978-3-030-66840-2_8(96-109)Online publication date: 13-Feb-2021
    • (2020)An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web DocumentWireless Communications & Mobile Computing10.1155/2020/88790542020Online publication date: 1-Jan-2020
    • (2020)Machine Learning Approach for Sentiment Analysis in Crime Information Retrieval2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)10.1109/IC2IE50715.2020.9274607(96-100)Online publication date: 15-Sep-2020
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media