Rumour Detection Models & Tools for Social Networking Sites
Mohammed Mahmood Ali1, Mohammad S. Qaseem2, Ateeq ur Rahman3

1Mohammed Mahmood Ali*, CSE Department, Osmania University, Hyderabad, India.
2Mohammed S. Qaseem, CSE Department, Nawab Shah alam College of Engineering, JNTUH, Hyderabad, India.
3Ateeq ur rahman, CSE Department, Shadan College of Engineering and Technology, JNTUH, Hyderabad, India.
Manuscript received on November 26, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3291-3296 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3465129219/2019©BEIESP | DOI: 10.35940/ijeat.B3465.129219
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Abstract: Efficient utilization of social networking sites (SNS) had reduced communication delays, at the same time increased rum our messages. Subsequently, mischievous people started sharing of rum ours via social networking sites for gaining personal benefits. This falsified information (i.e., rum our) creates misconception among the people of society influencing socio-economic losses by disrupting the routine businesses of private and government sectors. Communication of rum our information requires rigorous surveillance, before they become viral through social media platforms. Detecting these rum our words in an early stage from messaging applications needs to be predicted using robust Rum our Detection Models (RDM) and succinct tools. RDM are effectively used in detecting the rum ours from social media platforms (Twitter, Link edln, Instagram, WhatsApp, Weibo sena and others) with the help of bag of words and machine learning approaches to a limited extent. RDM fails in detecting the emerging rum ours that contains linguistic words of a specific language during the chatting session. This survey compares the various RDM strategies and Tools that were proposed earlier for identifying the rum our words in social media platforms. It is found that many of earlier RDM make use of Deep learning approaches, Machine learning, Artificial Intelligence, Fuzzy logic technique, Graph theory and Data mining techniques. Finally, an improved RDM model is proposed in Figure 2, efficiency of this proposed RDM models is improved by embedding of Pre-defined rumour rules, WordNet Ontology and NLP/machine learning approach giving the precision rate of 83.33% when compared with other state-of-art systems.
Keywords: Social Networking Sites (SNS), Rumour Detection models (RDM), Pre-defined rules, WordNet Ontology.