• Zurina Saaya
  • Tham Weng Hong



Rule-based, Trust matrix, Social media.


Social media has emerged as a popular platform for users to share information about real-world events, particularly during disaster emergencies. However, disaster managers often having problem to gather an accurate information of the current situation since not all observations are made by reliable users. In this project, we address this problem by developing a trust matrix framework to identify the trustworthiness of the information shared on social media. Specifically, this project is focusing on flood disaster management and information shared on Twitter platform. The first objective of this project is to create text corpus for statistical and keyword analysis of a flood event shared on social media. Second objective is to develop a trust matrix for the flood events from social media. Third objective is to evaluate trust matrix for flood event using crowd-sourced data. The evaluation of trust matrix is done using real flood event dataset which are gathered between June and July 2018. To establishing a ground-truth for trust matrix framework, each data is mapped with actual flood event from news portal.


W. X. Zhao, J. Jiang, J. Weng, J. He, E. P. Lim, H. Yan, X. Li, “Comparing twitter and traditional media using topic models,” Proceedings of the European Conference on Information Retrieval, Springer, Berlin, Heidelberg, April 2011, pp. 338-349.

B. Krishnamurthy, P. Gill, M. Arlitt, “A few chirps about twitter,” Proceedings of the First ACM Workshop on Online Social Networks, August 2008, pp. 19-24.

X. Guan, C. Chen, “Using social media data to understand and assess disasters,” Natural Hazards, vol. 74, no. 2, pp. 837-850, 2014.

M. Sloman, T. Grandison, “A survey of trust in internet applications,” IEEE Communications Surveys & Tutorials, vol. 3, issue 4, pp. 2-16, Fourth Quarter 2000.

A. Jøsang, R. Ismail, C. Boyd, “A survey of trust and reputation systems for online service provision,” Decision support systems, vol. 43, issue 2, pp. 618-644, 2007.

A. Gupta, H. Lamba, P. Kumaraguru, A. Joshi, “Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy,” Proceedings of the 22nd ACM International Conference on World Wide Web, May 2013, pp. 729-736.

T. Sakaki, M. Okazaki, Y. Matsuo, “Earthquake shakes Twitter users: real-time event detection by social sensors,” Proceedings of the 19th ACM International Conference on World Wide Web, April 2010, pp. 851-860.

T. Bodnar, C. Tucker, K. Hopkinson, S.G. Bilén, “Increasing the veracity of event detection on social media networks through user trust modeling,” Proceedings of the 2014 IEEE International Conference on Big Data, October 2014, pp. 636-643.

C. Lee, H. Kwak, H. Park, S. Moon, “Finding influentials based on the temporal order of information adoption in Twitter,” Proceedings of the 19th ACM International Conference on World Wide Web, April 2010, pp. 1137-1138.

B. Suh, L. Hong, P. Pirolli, E. H. Chi, “Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network,” Proceedings of the 2010 IEEE Second International Conference on Social Computing, August 2010, pp. 177-184.

D. Eilander, P. Trambauer, J. Wagemaker, A. Van Loenen, “Harvesting social media for generation of near real-time flood maps,” Procedia Engineering, vol. 154, pp. 176-183, 2016.

C. Grosan, A. Abraham, “Rule-based expert systems,” Intelligent Systems. Intelligent Systems Reference Library, vol 17. Springer, Berlin, Heidelberg, 2011, vol. 17, pp. 149-185.

M.R. Frank, L. Mitchell, P.S. Dodds, C.M. Danforth, “Happiness and the patterns of life: A study of geolocated tweets,” Scientific reports, 3, 2625, 2013.

S.A. Wood, A.D. Guerry, J.M. Silver, M. Lacayo, “Using social media to quantify nature-based tourism and recreation,” Scientific reports, 3, 2976, 2013.

C. Chew, G. Eysenbach, “Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak,” PloS one, vol. 5, issue 11, e14118, 2010.

R. Chunara, J.R. Andrews, J.S. Brownstein, “Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak,” The American Journal of Tropical Medicine and Hygiene, vol. 86, issue 1, pp. 39-45, 2012.

X. Lu, C. Brelsford, “Network structure and community evolution on twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami,” Scientific reports, 4, 6773, 2014.

S. Muralidharan, L. Rasmussen, D. Patterson, J.H. Shin, “Hope for Haiti: An analysis of Facebook and Twitter usage during the earthquake relief efforts,” Public Relations Review, vol. 37, issue 2, pp. 175-177, 2011.

D. Sun, S. Li, W. Zheng, A. Croitoru, A. Stefanidis, M. Goldberg, “Mapping floods due to Hurricane Sandy using NPP VIIRS and ATMS data and geotagged Flickr imagery,” International Journal of Digital Earth, vol. 9, issue 5, pp. 427-441, 2016.

B. Jongman, J. Wagemaker, B. R. Romero, E.C. de Perez, “Early flood detection for rapid humanitarian response: harnessing near real-time satellite and Twitter signals,” ISPRS International Journal of Geo-Information, vol. 4, issue 4, pp. 2246-2266, 2015.

J. Fohringer, D. Dransch, H. Kreibich, K. Schröter, “Social media as an information source for rapid flood inundation mapping,” Natural Hazards and Earth System Sciences, vol. 15, issue 12, pp. 2725-2738, 2015.

R. I. Ogie, R. J. Clarke, H. Forehead, P. Perez, “Crowdsourced social media data for disaster management: Lessons from the PetaJakarta. org project,” Computers, Environment and Urban Systems, vol. 73, pp. 108-117, 2019.

S. Vosoughi, D. Roy, S. Aral, “The spread of true and false news online,” Science, vol. 359(6380), pp. 1146-1151, 2018.




How to Cite

Saaya, Z., & Hong, T. W. (2019). THE DEVELOPMENT OF TRUST MATRIX FOR RECOGNIZING RELIABLE CONTENT IN SOCIAL MEDIA. International Journal of Computing, 18(1), 60-66.