Isolating Rumors Using Sentiment Analysis

Authors

  • V. Sivasangari TIFAC-CORE in Cyber Security Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
  • Ashok Kumar Mohan TIFAC-CORE in Cyber Security Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
  • K. Suthendran Kalasalingam Academy of Research and Education, Krishnankoil 626 126, Tamilnadu, India
  • M. Sethumadhavan TIFAC-CORE in Cyber Security Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

DOI:

https://doi.org/10.13052/2245-1439.7113

Keywords:

Social Media Analysis, Twitter Scraper, Sentiment Classifier, Rumor, Non-rumor, #RIP

Abstract

In recent days, social media has become a platform to spread false facts all the way through internet. One of the growing data analytic engine from web informal organization says, twitter has become the prime source for spreading fake news facilitating numerous perpetrators around the globe. It has turned into a competent, speedy cum effortless hotspot for news-fans to just click and forward junk data. Individuals are opting to use twitter for searching information regarding crisis circumstances and everyday occasions. In twitter, the spread of fraudulent or inaccurate information during the emergency situations will affect the individuals and public in numerous ways, but the original news is more reliable when it is declared by the news channels. So, the importance of proposed framework is categorized in three steps; Initially, Twitter Scraper is applied to scrape the vast volume of tweets and metadata from the collected set of tweets for the study on former Chief Minister (state of Tamil Nadu) death case controversy during 2016. Then the threshold value based on negative polarity of common tweets for the scraped data is calculated, once the tweeted texts are different from the threshold condition it will be automatically tagged as ‘rumor’ (negative) or else tagged as ‘non-rumor’ (positive) using sentiment classifier. Finally, the proposed model on VADER based sentiment analysis identifies the false facts. It is obtained as a result of the sample tweets regularly training and testing it on whole datasets.

 

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Author Biographies

V. Sivasangari, TIFAC-CORE in Cyber Security Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

V. Sivasangari is pursuing her M.Tech. in Cyber Security at TIFAC-CORE in Cyber Security, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. Her current area of research is Rumor Prediction in Social Media Networks.

Ashok Kumar Mohan, TIFAC-CORE in Cyber Security Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

Ashok Kumar Mohan, M.Tech. specialized in Cyber Security, is a Research Associate at TIFAC-CORE in Cyber Security, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. He is currently a Ph.D. scholar doing his research in the area of Cyber Forensics funded by Ministry of Electronics & Information Technology (Government of India) under Visvesvaraya PhD scheme for Electronics and IT. He is currently pursuing his research over the cyber security core vicinity in Metadata Forensics, Wireless Security Auditing, Rumor Prediction in Social Media Networks and Slack Space Analysis of NTFS File Systems. He is also the Certified EC-Council Instructor (CEI) for ethical hacking and penetration testing certification courses at the research centre.

K. Suthendran, Kalasalingam Academy of Research and Education, Krishnankoil 626 126, Tamilnadu, India

Suthendran Kannan, received his B.E. Electronics and Communication Engineering from Madurai Kamaraj University in 2002; his M.E. Communication Systems from Anna University in 2006 and his Ph.D. Electronics and Communication Engineering from Kalasalingam University in 2015. He was a Research and Development Engineer at Matrix view Technologies Private Limited, Chennai for a couple of years. He is now the Head, Cyber Forensics Research Laboratory and Associate Professor in Information Technology, Kalasalingam Academy of Research and Education. His current research interests include Cyber Security, Communication System, Signal Processing, Image Processing, etc.

M. Sethumadhavan, TIFAC-CORE in Cyber Security Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

M. Sethumadhavan, received his Ph.D. (Number Theory) from Calicut Regional Engineering College. Currently, he is working as a Professor in the Department of Mathematics and Computer Science, Amrita Vishwa Vidyapeetham University, Coimbatore. His current research interests include: Post Quantum Crytography, Block Chain and Boolean functions.

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https://stackoverflow.com/questions/8713241/whats-the-facebooks-graph-api-call-limit

https://stackoverflow.com/questions/1285666/twitter-api-limitt

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Published

2018-01-05

How to Cite

1.
Sivasangari V, Mohan AK, Suthendran K, Sethumadhavan M. Isolating Rumors Using Sentiment Analysis. JCSANDM [Internet]. 2018 Jan. 5 [cited 2024 Apr. 25];7(1-2):181-200. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/5293

Issue

2018: Vol 7 Iss 1-2

Section

Articles