Rumor detection on Twitter using extracted patterns from conversational tree

Yavary, Arefeh; Sajedi, Hedieh

doi:10.1109/icwr.2018.8387241

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…In addition to some of the studies mentioned above, RDF subgraph queries are also a class of graph search that can be applied to many scenarios through different forms of optimization. For example, solving dynamic vehicle path problems [6] and also rumor detection in different social network analyses [7]. Moreover, when the method can be applied to network searches, it can help to find the nodes that cause Doppler shifts [8].…”

Section: Related Workmentioning

confidence: 99%

RDF Subgraph Query Based on Common Subgraph in Distributed Environment

Huang

Lai

et al. 2023

Wireless Communications and Mobile Computing

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With the gradual development of the network, RDF graphs have become more and more complex as the scale of data increases; how to perform more effective query for massive RDF graphs is a hot topic of continuous research. The traditional methods of graph query and graph traversal produce great redundancy of intermediate results, and processing subgraph collection queries in stand-alone mode cannot perform efficient matching when the amount of data is extremely large. Moreover, when processing subgraph collection queries, it is necessary to iterate the query graph multiple times in the query of the common subgraph, and the execution efficiency is not high. In response to the above problems, a distributed query strategy of RDF subgraph set based on composite relation tree is proposed. Firstly, a corresponding composite relationship is established for RDF subgraph set, then the composite relation graph is clipped, and the redundant nodes and edges of the composite relation graph are deleted to obtain the composite relation tree. Finally, using the composite relation tree, a MapReduce-based RDF subgraph set query method is proposed, which can use parallel in the computing environment, the distributed query batch processing is performed on the RDF subgraph set, and the query result of the RDF subgraph set is obtained by traversing the composite relation tree. The experimental results show that the algorithm proposed in this paper can improve the query efficiency of RDF subgraph set.

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Section: Related Workmentioning

confidence: 99%

RDF Subgraph Query Based on Common Subgraph in Distributed Environment

Huang

Lai

et al. 2023

Wireless Communications and Mobile Computing

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“…Social media network like Twitter is being progressively used by professionals, organizations, and individuals as a primary source of information to find out the current affairs [10][11][12]. In spite of the rising potential of Twitter as a primary source of information, its tendency is to spread misinformation that is rumors and its impact attracted several researchers [13,14]. Researchers studied, analyzed, and developed the ways to detect and classify rumors so that the end-users can get accurate and verified information.…”

Section: Related Workmentioning

confidence: 99%

Feature-Based Supervised Classifier to Detect Rumor in Social Media

Joshi

Bhilare

2020

Computational Methods and Data Engineering

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Social media is the most important and powerful platform for sharing information, ideas, and news almost immediately. With this, it also attracted antisocial elements for spreading and distributing rumors that is unverified information. Malicious and intended misinformation spread on social media has a severe effect on societies, people and individuals, especially in case of real-life emergencies such as terror strikes, riots, earthquakes, floods, war, etc. Thus, to minimize the harmful impact of rumor on society, it will be better to detect it as early as possible. The objective of this research and analysis is to develop a modified rumor detection model targeted for the proliferation of any malicious rumors related to any significant events. It is achieved through a binomial supervised classifier. The classifier uses a combination of explicit and implicit features to detect rumors. Our enhanced model significantly achieved it with 85.68% accuracy.

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