Rumor Detection on Twitter Using a Supervised Machine Learning Framework

Rumor Detection on Twitter Using a Supervised Machine Learning Framework

Hardeo Kumar Thakur (Department of Computer Engineering, Netaji Subhas Institute of Technology, New Delhi, India), Anand Gupta (Department of Computer Engineering, Netaji Subhas Institute of Technology, New Delhi, India), Ayushi Bhardwaj (Netaji Subhas Institute of Technology, New Delhi, India) and Devanshi Verma (Netaji Subhas Institute of Technology, New Delhi, India)
Copyright: © 2018 |Pages: 13
DOI: 10.4018/IJIRR.2018070101

Abstract

This article describes how a rumor can be defined as a circulating unverified story or a doubtful truth. Rumor initiators seek social networks vulnerable to illimitable spread, therefore, online social media becomes their stage. Hence, this misinformation imposes colossal damage to individuals, organizations, and the government, etc. Existing work, analyzing temporal and linguistic characteristics of rumors seems to give ample time for rumor propagation. Meanwhile, with the huge outburst of data on social media, studying these characteristics for each tweet becomes spatially complex. Therefore, in this article, a two-fold supervised machine-learning framework is proposed that detects rumors by filtering and then analyzing their linguistic properties. This method attempts to automate filtering by training multiple classification algorithms with accuracy higher than 81.079%. Finally, using textual characteristics on the filtered data, rumors are detected. The effectiveness of the proposed framework is shown through extensive experiments on over 10,000 tweets.
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Introduction

When it comes to people and connectivity online social networking sites like Twitter seem to have set their web wider than any other. Studies reveal that the swift information propagation potential and extensive reach to the masses has made twitter the top source of breaking news in most parts of the world (Rosenstiel, Sonderman, Loker, Ivancin & Kjarval, 2015). Amidst the news statements, misinformation or rumor also finds its route into the society and its widespread is one of the biggest challenges facing social media (The guardian, 2011). Rumors are ubiquitous and with vast public involvement, they have the capability to impose real damage to individuals, organizations, and the government. Viral rumors about individuals that condemn them for their actions may lead to hate campaigns and eventually harm their reputation. This may affect individual’s self-esteem and confidence level. Rumors accelerate the dynamic nature of share markets and consequently elaborate their effect on organizations (Time,2013). Sometimes misinformation about the outburst of a disease (Time,2014) might affect the tourism of a country and likewise other government sectors. Analysis of rumors led to its aspect of public participation through various perspectives, for example, political belief (Shin, Jian, Driscoll & Bar,2016), influence on markets (Cruz & Gomes, 2013; Yiwen, Guizhong & Zongping, 2000) and crisis management (Onook, Manish & Raghav, 2013). For analysis of misinformation, its detection is the preliminary step.

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