Towards a reputation model applied to geosocial networks: a case study on crowd4city
Authors: 
Ana Gabrielle Ramos Falcão, Cláudio de Souza Baptista, Maxwell Guimarães de Oliveira, Júlio Henrique Rocha, Tiago Henrique da Silva Leite, José Eustáquio Rangel de QueirozAuthors Info & Claims
Ana Gabrielle Ramos Falcão, Cláudio de Souza Baptista, Maxwell Guimarães de Oliveira, Júlio Henrique Rocha, Tiago Henrique da Silva Leite, José Eustáquio Rangel de QueirozAuthors Info & ClaimsPages 1756 - 1763
Abstract
Geosocial networks gather large amounts of voluntarily generated information that can be explored among many different contexts, including urban areas. In this sense, we developed the Crowd4City system, which put together city authorities and citizens focusing on the improvement of their urban spaces. A challenge in this context is concerning the information reliability on crowdsourced data, by automatically identifying misinformation or spam from malicious users. For such, this paper proposes a reputation model to be applied to geosocial network users aiming at ensuring a better information reliability. A case study evaluates Crowd4City proposal in a real scenario. The promising results enhance information reliability in the geosocial network.
References
[1]
Aftab Alam, Shah Khusro, Irfan Ullah, and Muhammad Shuaib Karim. 2017. Confluence of social network, social question and answering community, and user reputation model for information seeking and experts generation. Journal of Information Science 43, 2 (2017), 260--274.
[2]
B. Baykurt. 2012. Redefining Citizenship and Civic Engagement: political values embodied in FixMyStreet.com. AoIR Selected Papers of Internet Research 1 (2012).
[3]
J. Carter, E. Bitting, and A. A. Ghorbani. 2002. Reputation formalization for an information-sharing multi-agent system. Computational Intelligence 18, 4 (2002), 515--534.
[4]
C. Castelfranchi and R. Falcone. 1998. Principles of trust for MAS: cognitive anatomy, social importance, and quantification. In Proceedings of the IEEE Intern. Conf. on Multi Agent Systems. 72--79.
[5]
A. Crooks et al. 2015. Crowdsourcing urban form and function. International Journal of Geographical Information Science 29, 5 (2015), 720--741.
[6]
V. Furtado et al. 2010. Collective Intelligence in Law Enforcement - The Wiki-Crimes System. Information Sciences 180, 1 (2010), 4--17.
[7]
M. F. Goodchild. 2007. Citizens as voluntary sensors: spatial data infrastructure in the world of Web 2.0. International Journal of Spatial Data Infrastructures Research 2 (2007), 24--32.
[8]
B. Guo, A. Chin, Z. Yu, R. Huang, and D. Zhang. 2015. An Introduction to the Special Issue on Participatory Sensing and Crowd Intelligence. ACM Trans. Intell. Syst. Technol. 6, 3, Article 36 (2015), 4 pages.
[9]
B. Guo, Z. Yu, X. Zhou, and D. Zhang. 2014. From participatory sensing to Mobile Crowd Sensing. In Proceedings of the IEEE Intern. Conf. on Pervasive Computing and Communication Workshops. 593--598.
[10]
Pei-Yun Hsueh, Prem Melville, and Vikas Sindhwani. 2009. Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria. In NAACL HLT Workshop on Active Learning for Natural Language Processing. 27--35.
[11]
A. Jøsang and W. Quattrociocchi. 2009. Advanced Features in Bayesian Reputation Systems. In Proceedings of the Intern. Conf. on Trust, Reputation, and Security: Theories and Practice. Springer, 105--114.
[12]
R. Jurca and B. Faltings. 2003. Towards Incentive-compatible Reputation Management. In Proceedings of the Intern. Conf. on Trust, Reputation, and Security: Theories and Practice. Springer-Verlag, 138--147. http://dl.acm.org/citation.cfm?id=1762128.1762139
[13]
R. Montes, A. M. Sanchez, P. Villar, and F. Herrera. 2017. A decision making model to evaluate the reputation in social networks using HFLTS. In Proceedings of the IEEE Intern. Conf. on Fuzzy Systems. 1--6.
[14]
M. Naphade, G. Banavar, C. Harrison, J. Paraszczak, and R. Morris. 2011. Smarter Cities and Their Innovation Challenges. Computer 44, 6 (2011), 32--39.
[15]
J. Nielsen. 2012. How many test users in a usability study? (2012). Retrieved from http://www.nngroup.com.
[16]
M. G. Oliveira, C. de S. Baptista, C. E. C. Campelo, and M. Bertolotto. 2017. A gold-standard social media corpus for urban issues. In Proceedings of the ACM Symp. on Appl. Comp. (SAC). 1011--1016.
[17]
F. Petroni, L. Querzoni, R. Beraldi, and M. Paolucci. 2016. Exploiting User Feedback for Online Filtering in Event-based Systems. In Proceedings of the ACM Symp. on Appl. Comp. (SAC). 2021--2026.
[18]
D. Rafailidis. 2016. Modeling Trust and Distrust Information in Recommender Systems via Joint Matrix Factorization with Signed Graphs. In Proceedings of the ACM Symp. on Appl. Comp. (SAC). 1060--1065.
[19]
S. Rahimiaghdam, P. Karagoz, and A. Mutlu. 2016. Personalized Time-aware Outdoor Activity Recommendation System. In Proceedings of the ACM Symp. on Appl. Comp. (SAC). 1121--1126.
[20]
J. Sang, T. Mei, and C. Xu. 2015. Activity Sensor: Check-In Usage Mining for Local Recommendation. ACM Trans. Intell. Syst. Technol. 6, 3, Article 41 (2015), 24 pages.
[21]
W. T. L. Teacy, Nicholas R. J., A. Rogers, and M. Luck. 2008. A Hierarchical Bayesian Trust Model based on Reputation and Group Behaviour. In Proceedings of the European Workshop on Multi-Agent Systems. https://eprints.soton.ac.uk/266836/
[22]
C. R. Vicente, D. Freni, C. Bettini, and C. S. Jensen. 2011. Location-Related Privacy in Geo-Social Networks. IEEE Internet Computing 15, 3 (2011), 20--27.
[23]
Timo Wandhofer, Catherine van Eeckhaute, Stephen Taylor, and Miriam Fernandez. 2012. WeGov analysis tools to connect policy makers with citizens online. In Proceedings of the tGovernment Workshop. United Kingdom.
[24]
C. Xia et al. 2014. CityBeat: Real-time Social Media Visualization of Hyper-local City Data. In Proceedings of the ACM Intern. Conf. on World Wide Web. 167--170.
[25]
B. Zhang, Z. Song, C. Harold Liu, J. Ma, and W. Wang. 2015. An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints. ACM Trans. Intell. Syst. Technol. 6, 3, Article 42 (2015), 19 pages.
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- Towards a reputation model applied to geosocial networks: a case study on crowd4city
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2327 pages
ISBN:9781450351911
DOI:10.1145/3167132
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- Roger L. Wainwright,
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Published: 09 April 2018
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