Papers by Sakshini Hangloo
2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT)
In the past decade, the social networks platforms and micro-blogging sites such as Facebook, Twit... more In the past decade, the social networks platforms and micro-blogging sites such as Facebook, Twitter, Instagram and Sina Weibo have become an integral part of our day-today activities and is widely used all over the world by billions of users to share their views and circulate information in the form of messages, pictures, and videos. These are even used by government agencies to spread important information through their verified Facebook accounts and official Twitter handles, as it can reach a huge population within a limited time window. However, many deceptive activities like propaganda and rumour can mislead users on a daily basis. In this COVID times the fake news and rumours are very prevalent and are shared in a huge number which has created chaos in this tough time. And hence, the need of Fake News Detection it the present scenario is inevitable. In this paper, we survey the recent literature about different approaches to detect fake news over the Internet. In particular, we firstly discuss about fake news and the various terms related to it that have been considered in the literature. Secondly, we highlight the various publicly available datasets and various online tools that are available and cam debunk Fake News in real time. Thirdly, we describe fake news detection methods based on two broader areas i.e., it's content and the social context. Finally, we provide a comparison of various techniques that are used to debunk fake news.
Multimedia Systems
The growth in the use of social media platforms such as Facebook and Twitter over the past decade... more The growth in the use of social media platforms such as Facebook and Twitter over the past decade has significantly facilitated and improved the way people communicate with each other. However, the information that is available and shared online is not always credible. These platforms provide a fertile ground for the rapid propagation of breaking news along with other misleading information. The enormous amounts of fake news present online have the potential to trigger serious problems at an individual level and in society at large. Detecting whether the given information is fake or not is a challenging problem and the traits of social media makes the task even more complicated as it eases the generation and spread of content to the masses leading to an enormous volume of content to analyze. The multimedia nature of fake news on online platforms has not been explored fully. This survey presents a comprehensive overview of the state-of-the-art techniques for combating fake news on online media with the prime focus on deep learning (DL) techniques keeping multimodality under consideration. Apart from this, various DL frameworks, pre-trained models, and transfer learning approaches are also underlined. As till date, there are only limited multimodal datasets that are available for this task, the paper highlights various data collection strategies that can be used along with a comparative analysis of available multimodal fake news datasets. The paper also highlights and discusses various open areas and challenges in this direction.
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Papers by Sakshini Hangloo