Approximation algorithms for directed Steiner problems
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Why and Where: A Characterization of Data Provenance
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Lineage retrieval for scientific data processing: a survey
ACM Computing Surveys (CSUR)
A survey of data provenance in e-science
ACM SIGMOD Record
Mining Taverna's semantic web of provenance
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Finding effectors in social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Rumors in a Network: Who's the Culprit?
IEEE Transactions on Information Theory
Finding provenance data in social media
Finding provenance data in social media
Spotting Culprits in Epidemics: How Many and Which Ones?
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
A tool for collecting provenance data in social media
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A tool for assisting provenance search in social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A tool for assisting provenance search in social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Social media propagates breaking news and disinformation alike fast and on an unsurpassed scale. Because of its democratizing nature, social media users can easily produce, receive, and propagate a piece of information without necessarily providing traceable information. Thus, there are no means for a user to verify the provenance (aka sources or originators) of information. The disinformation can cause tragic consequences to society and individuals. This work aims to take advantage of characteristics of social media to provide a solution to the problem of lacking traceable information. Such knowledge can provide additional context to received information such that a user can assess how much value, trust, and validity should be placed in it. In this paper, we are studying a novel research problem that facilitates the seeking of the provenance of information for a few known recipients (less than 1% of the total recipients) by recovering the paths it has taken from its originators. The proposed methodology exploits easily computable node centralities of a large social media network. The experimental results with Facebook and Twitter datasets show that the proposed mechanism is effective in correctly identifying the additional recipients and seeking the provenance of information.