Visible to the public Natural Language Processing based Online Fake News Detection Challenges – A Detailed Review

TitleNatural Language Processing based Online Fake News Detection Challenges – A Detailed Review
Publication TypeConference Paper
Year of Publication2020
AuthorsHirlekar, V. V., Kumar, A.
Conference Name2020 5th International Conference on Communication and Electronics Systems (ICCES)
Date Publishedjun
Keywordsfake news content, fake news detection, fake news identification, feature extraction, Human Behavior, learning (artificial intelligence), machine learning, natural calamities, natural language processing, online fake news detection challenges, online social media, online social network, pubcrawl, Resiliency, Scalability, sentiment analysis, social media usage, social movements, social networking (online), world events
AbstractOnline social media plays an important role during real world events such as natural calamities, elections, social movements etc. Since the social media usage has increased, fake news has grown. The social media is often used by modifying true news or creating fake news to spread misinformation. The creation and distribution of fake news poses major threats in several respects from a national security point of view. Hence Fake news identification becomes an essential goal for enhancing the trustworthiness of the information shared on online social network. Over the period of time many researcher has used different methods, algorithms, tools and techniques to identify fake news content from online social networks. The aim of this paper is to review and examine these methodologies, different tools, browser extensions and analyze the degree of output in question. In addition, this paper discuss the general approach of fake news detection as well as taxonomy of feature extraction which plays an important role to achieve maximum accuracy with the help of different Machine Learning and Natural Language Processing algorithms.
Citation Keyhirlekar_natural_2020