Rumor Remove Order Strategy on Social Networks
Authors: 
Yuanda Wang, Haibo Wang, Shigang Chen, Ye XiaAuthors Info & Claims
Pages 128 - 137
Abstract
Rumors are defined as widely spread talk with no reliable source to back it up. In modern society, the rumors are widely spreading on the social network. The spread of rumors poses great challenges for the society. A ”fake news” story can rile up your emotions and change your mood. Some rumors can even cause social panic and economic losses. As such, the influence of rumors can be far-reaching and long-lasting. Efficient and intelligent rumor control strategies are necessary to constrain the spread of rumors. Existing rumor control strategies are designed for controlling a single rumor. However, there are usually many rumors existing on social networks and only limited rumors can be removed at a time due to the limited detection capacity and CPU performance. Consequently, when dealing with multiple rumors, we should remove rumors in a certain order. We argue that the order of removing rumors matters as different rumors possess different properties, e.g., acceptance rate, propagation speed, etc. Unfortunately, to the best of our knowledge, there is no prior work on removing multiple rumors and the order of removing rumors. To this end, this paper proposes two novel rumor control strategies to remove the multiple rumors. We also extends the classical Susceptible Infected Recovered (SIR) model to simulate the dynamics of rumor propagation in a more practical manner. We evaluate the performance of strategies. The experiments show that our proposed rumor control strategies obviously outperform than benchmark strategy.
References
[1]
R. Amutha and D. Vimal Kumar. 2020. Semi-Supervised Clustering Algorithm for Rumor Minimization and Propagation with Classification in Social Networks. 2020 International Conference on Inventive Computation Technologies (ICICT) (Feb. 2020).
[2]
Ceren Budak, Divyakant Agrawal, and Amr El Abbadi. 2011. Limiting the spread of misinformation in social networks. Proceedings of the 20th international conference on World wide web (March 2011), 665 – 674.
[3]
Xin Chen, Qingqin Nong, Yan Feng, Yongchang Cao, Suning Gong, Qizhi Fang, and Ker-I Ko. 2017. Centralized and decentralized rumor blocking problems. Journal of Combinatorial Optimization volume (2017).
[4]
Sun-Ho Choi, Hyowon Seo, and Minha Yoo. 2020. A multi-stage SIR model for rumor spreading. American Institute of Mathematical Sciences(2020).
[5]
M.L. Goldstein, S.A. Morris, and G.G. Yen. 2004. Problems with fitting to the power-law distribution. THE EUROPEAN PHYSICAL JOURNAL B(2004), 255–258.
[6]
Xinran He, Guojie Song, Wei Chen, and Qingye Jiang. [n.d.]. Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model. Proceedings of the 2012 SIAM International Conference on Data Mining ([n. d.]).
[7]
Z. He, Z. Cai, and X. Wang. 2015. Modeling Propagation Dynamics and Developing Optimized Countermeasures for Rumor Spreading in Online Social Networks. (2015), 205–214.
[8]
Kundan Kandhway and Joy Kuri. 2014. Optimal control of information epidemics modeled as Maki Thompson rumors. Communications in Nonlinear Science and Numerical Simulation 19 (December 2014), 4135–4147.
[9]
Songsong Li, Yuqing Zhu, Deying Li, Donghyun Kim, and Hejiao Huang. [n.d.]. Rumor Restriction in Online Social Networks. 2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC) ([n. d.]).
[10]
Bess Lovejoy. Online. 9 False Rumors With Real-Life Consequences. https://www.mentalfloss.com/article/72892/9-false-rumors-real-life-consequences(Online).
[11]
O. Odegbile, S. Chen, and Y. Wang. 2019. Dependable Policy Enforcement in Traditional Non-SDN Networks. 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) (2019), 545–554.
[12]
Mathematicla Association of America. Online. The SIR Model for Spread of Disease - The Differential Equation Model. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model(Online).
[13]
Nathaniel Percy. Online. Social media threat, rumors cause panic at football game at Diamond Bar High School. https://www.sgvtribune.com/2018/08/18/social-media-threat-rumors-cause-panic-at-football-game-at-diamond-bar-high-school/(Online).
[14]
Devavrat Shah and Tauhid Zaman. 2010. Detecting sources of computer viruses in networks: theory and experiment. Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems (June 2010), 203–214.
[15]
Santhoshkumar Srinivasan and Dhinesh Babu L.D.2020. A Neuro-Fuzzy Approach to Detect Rumors in Online Social Networks. International Journal of Web Services Research (IJWSR) (2020).
[16]
Li Tan, Zihao Ma, Juan Cao, and Xinyue Lv. 2020. Rumor detection based on topic classification and multi-scale feature fusion. Journal of Physics: Conference Series 1601 (jul 2020), 032032. https://doi.org/10.1088%2F1742-6596%2F1601%2F3%2F032032
[17]
Guangmo Tong, Student Member, Weili Wu, Ling Guo, Deying Li, Cong Liu, Bin Liu, and Ding-Zhu Du. 2020. An Efficient Randomized Algorithm for Rumor Blocking in Online Social Networks. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 7, 2 (April-June 2020).
[18]
Ya-Qi Wang, Xiao-Yuan Yang, Yi-Liang Han, and Xu-An Wang. 2013. Rumor Spreading Model with Trust Mechanism in Complex Social Networks. Communications in Theoretical Physics 59, 4 (apr 2013), 510–516. https://doi.org/10.1088%2F0253-6102%2F59%2F4%2F21
[19]
Zhaoxu Wang, Wenxiang Dong, Wenyi Zhang, and Cheewei Tan. 2014. Rumor source detection with multiple observations: fundamental limits and algorithms. ACM SIGMETRICS Performance Evaluation Review (June 2014).
[20]
Zhaoxu Wang, Wenxiang Dong, Wenyi Zhang, and Chee Wei Tan. 2014. Rumor source detection with multiple observations: fundamental limits and algorithms. ACM SIGMETRICS Performance Evaluation Review (June 2014).
[21]
Zhihong Wang and Yi Guo. 2020. Rumor events detection enhanced by encoding sentimental information into time series division and word representations. Neurocomputing (2020).
[22]
Brian E. Weeks and R. Kelly Garrett. 2014. Electoral Consequences of Political Rumors: Motivated Reasoning, Candidate Rumors, and Vote Choice during the 2008 U.S. Presidential Election. International Journal of Public Opinion Research 26, 4 (03 2014), 401–422. https://doi.org/10.1093/ijpor/edu005 arXiv:https://academic.oup.com/ijpor/article-pdf/26/4/401/2191815/edu005.pdf
[23]
Wikipedia. Online. Runge-Kutta methods. https://en.wikipedia.org/wiki/Runge-Kutta_methods(Online).
[24]
Qingqing Wu, Xianguan Zhao, Lihua Zhou, Yao Wang, and Yudi Yang. 2019. Minimizing the influence of dynamic rumors based on community structure. International Journal of Crowd Science 3 (September 2019).
[25]
Ruidong Yan, Deying Li, Weili Wu, Ding-Zhu Du, and Yongcai Wang. 2019. Minimizing Influence of Rumors by Blockers on Social Networks: Algorithms and Analysis. IEEE Transactions on Network Science and Engineering (2019), 1–1.
[26]
Zhao Zhang, Wen Xu, Weili Wu, and Ding-Zhu Du. 2015. A novel approach for detecting multiple rumor sources in networks with partial observations. Springer Science+Business Media (August 2015).
[27]
Laijun Zhao, Hongxin Cui, Xiaoyan Qiu, Xiaoli Wang, and Jiajia Wang. 2013. SIR rumor spreading model in the new media age. Physica A: Statistical Mechanics and its Applications 392 (2013), 995–1003.
[28]
Laijun Zhao, Jiajia Wang, Yucheng Chen, Qin Wang, Jingjing Cheng, and Hongxin Cui. 2012. SIHR rumor spreading model in social networks. Physica A: Statistical Mechanics and its Applications 391 (April 2012), 2444–2453.
[29]
L. Zheng and C. W. Tan. 2015. A probabilistic characterization of the rumor graph boundary in rumor source detection. (2015), 765–769.
[30]
Liang Zheng and Chee Wei Tan. 2015. A probabilistic characterization of the rumor graph boundary in rumor source detection. 2015 IEEE International Conference on Digital Signal Processing (DSP) (July 2015).
[31]
Kai Zhu and Lei Ying. 2014. A robust information source estimator with sparse observations. Computational Social Networks volume(2014).
Recommendations
A study of rumor control strategies on social networks
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge managementIn this paper we study and evaluate rumor-like methods for combating the spread of rumors on a social network. We model rumor spread as a diffusion process on a network and suggest the use of an "anti-rumor" process similar to the rumor process. We ...
Rumor Gauge: Predicting the Veracity of Rumors on Twitter
Special Issue on KDD 2016 and Regular PapersThe spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated ...
A Bio-Inspired Defensive Rumor Confinement Strategy in Online Social Networks
Online social networks (OSNs) are used to connect people and propagate information around the globe. Along with information propagation, rumors also penetrate across the OSNs in a massive order. Controlling the rumor propagation is utmost important to ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2021
162 pages
ISBN:9781450389549
DOI:10.1145/3471287
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 September 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
ICISDM 2021
ICISDM 2021: 2021 the 5th International Conference on Information System and Data Mining
May 27 - 29, 2021
CA, Silicon Valley, USA
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 190Total Downloads
- Downloads (Last 12 months)76
- Downloads (Last 6 weeks)12
Reflects downloads up to 13 Dec 2024
Other Metrics
Citations
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in