IIIT Hyderabad Publications |
|||||||||
|
Controversy and Stance Detection to Mitigate Spread of MisinformationAuthor: ALLAPARTHI SRI TEJA Date: 2019-06-21 Report no: IIIT/TH/2019/77 Advisor:Vikram Pudi AbstractGiven the huge impact of the Internet, online content along with social networks play an important role in forming or changing the opinions of people. Unlike traditional journalism where only certain news organizations can publish content, online journalism has given chance even for individuals to publish. This has its own advantages like individual empowerment, decentralization of power etc...but has given a chance to a lot of malicious entities to spread misinformation for their own benefit. It provides the mechanism to publish news events as they happen. As soon as the information is uploaded to the web, it is available around the globe. In these times where blogging is considered as journalism and thousands of websites are being built daily, it is unfortunate that the authenticity of the news on a topic can be undermined. People are more inclined to believe that the information is true if they encounter it before. As reported by many organizations in recent history, this even has an influence on major events like the outcome of elections. Hence, there is a great need for an automated intelligent system which can classify what stories people are reading to so that they can verify before believing or sharing such news stories. Such a system can be a bulwark to the dissemination of fake, false, conspiratorial and misleading news. The three specific sub-problems we dealt with in this thesis are: (1) Fine-grained classification of a news articles based on content, (2) Controversy detection based on the reactions like comments or likes and shares of an article on social networks, and (3) Stance detection in the wild using maximally informative sentences extracted from many articles discussing the same topic. As mentioned, given that the usage of online social media is increasing, many political parties and news sources are using this as a platform for spreading news. This helps us to collect more data about peoples opinions on a topic and utilize this data as the key to understanding some aspects regarding the controversy. In recent times, social media has been playing a role in how individuals process information and form political opinions. So, there has been a need to offer the user a view that differs from what they are mostly exposed to, for gaining the overall picture on a topic before coming to a conclusion or an opinion. One aspect that is the focus of these studies is how the social media user interaction can be used to predict a controversy. We investigate the topics related to US 2016 elections and present the results of our quantitative analysis that aims to classify the topic as being controversial or not. We propose a deep hierarchical attention neural architecture combining sentiment and semantic embeddings for fine-grained classification of news stories. Full thesis: pdf Centre for Data Engineering |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |