CRIME DETECTION FOR ID BLOCK IN SOCIAL MEDIA
DOI:
https://doi.org/10.61841/nyhahe24Keywords:
crime detection for id block in social mediaAbstract
This project shows that online social networks can be used to learn some issues related to detection. It is portrayed as a demonstration that is hurtful not exclusively to the individual in question yet in addition to the whole network. Violations are social disturbances that put an overwhelming weight on society. The use of data accompanied by online social network analysis to detect trends of detection. Twitter is informal real-time long-range communication and also a smaller blogging platform that allows clients to post brief updates of content, commonly known as "tweets.". Those updates will pass on significant data about the creator. A channel was developed to remove tweets and viewed as either the best or the most risky in the U.S. A regional study showed a connection between those data and the crimes in the respective places. More than 100,000 tweets accordingly to the crime gathered for the past 20 days. Methods of analysis were performed on these messages to determine the severity of a crime committed by a specific place. The research type is aid in revealing the rate of a city in real-time. Although the findings from this study helped forecast crime patterns, the methods of evaluating emotions did not always guarantee the right results. They end by applying media with implementations of this form of study to text processing techniques and how they can be improved. The current user location is also identified when we connect to the projects.
Downloads
References
1. D. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread of influence through a social network,”
in Proc. 9th ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, 2003
2. J. Goldenberg, B. Libai, and E. Muller, “Talk of the network: A complex systems look at the underlying
process of word-of-mouth." Marketing Letters, vol. 12, no. 3, pp. 211-223, 2001.
3. H. W. Hethcote, “The mathematics of infectious diseases,” SIAM Review, vol. 42, no. 4, pp. 599–653,
2000.
4. M. E. J. Newman, “The structure and function of complex networks,” SIAM Review, vol. 45, no. 2, pp.
167–256, 2003.
5. S. Wang, X. Hu, P. S. Yu, and Z. Li, “MMRate: inferring multi-aspect diffusion networks with multipattern cascades,” in Proc. 2014, pp. 1246–1255
6. Y. Tang, Y. Shi, and X. Xiao, “Influence maximization in near-linear time: a martingale approach,” in
Proc. ACM Int. Conf. Special Interest Group Manage. Data, 2015.
7. L. Weng, A. Flammini, A. Vespignani, and F. Menczer, “Competition among memes in a world with
limited attention,” Scientific Reports, vol. 2, 2012
8. S. A. Myers and J. Leskovec, “Clash of the contagions: Cooperation and competition in information
diffusion,” in Proc. 12th IEEE Int. Conf. Data Mining, 2012
9. X. Rong and Q. Mei, “Diffusion of innovations revisited: from social network to innovation network,” in
Proc. 22th ACM Int. Conf. Inf. Knowl. Manage.,2013.
10. Y. Bi, W. Wu, and Y. Zhu, “Csi: Charged system influence model for human behavior prediction,” in
Proc. 13th IEEE Int. Conf. Data Mining, 2013
11. M. Coscia, “Competition and success in the meme pool: a case study on quickmeme.com,” in Proc. Int. AAAI Conf. Weblogs Soc. Media, 2013.
12. Valera and M. Gomez-Rodriguez, “Modeling adoption and usage of competing products,” in Proc. 15th IEEE Int. Conf. Data Mining, 2015.
13. N. Pathak, A. Banerjee, and J. Srivastava, “A generalized linear threshold model for multiple cascades,” in Proc. 10th IEEE Int. Conf. Data Mining, 2010.
14. B. A. Prakash, A. Beutel, R. Rosenfeld, and C. Faloutsos, “Winner takes all: competing viruses or ideas on fair-play networks,” in Proc. 21rd Int. Conf. World Wide Web, 2012.
15. B. Karrer and M. E. J. Newman, “Competing epidemics on complex networks,” Physical Review E, vol. 84(3): 036106, 2011.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
