@InCollection{Verma2020, author = {Pawan Kumar Verma and Prateek Agrawal}, booktitle = {Data Management, Analytics and Innovation}, publisher = {Springer Singapore}, title = {{Study and Detection of Fake News: P2C2-Based Machine Learning Approach}}, year = {2020}, month = {sep}, pages = {261--278}, abstract = {News is the most important and sensitive piece of information which affects the society nowadays. In the current scenario, there are two ways to propagate news all over the world; first one is the traditional way, i.e., newspaper and second is electronic media like social media websites. Electronic media is the most popular medium these days because it helps to propagate news to huge audience in few seconds. Besides these benefits of electronic media, it has one disadvantage also, i.e., “spreading the Fake News”. Fake news is the most common problem these days. Even big companies like Twitter, Facebook, etc. are facing fake news problems. Several researchers are working in these big companies to solve this problem. Fake news can be defined as the news story that is not true. In some specific words, we can say that news is fake if any news agency declares a piece of news deliberately written as false and it is also verifiably as false. This paper focuses on some key characteristics of fake news and how it is affecting the society nowadays. It also includes various key viewpoints which are useful to categorize whether the news is fake or not. At last, this paper discussed some key challenges and future directions that help in increasing accuracy in detection of fake news on the basis of P2C2 (Propagation, Pattern, Comprehension & Credibility) approach having two phases: Detection and Verification. This paper helps readers in two ways (i) Newcomer can easily get the basic knowledge and impact of fake news; (ii) They can get knowledge of different perspectives of fake news which are helpful in the detection process.}, doi = {10.1007/978-981-15-5619-7_18}, keywords = {Credibility-based content classification, Comprehension content study on social media}, url = {https://link.springer.com/chapter/10.1007/978-981-15-5619-7_18} }