## 1 Introduction  
Fake news detection has always been a problem because of its long-term repercussions and consequences. Its root can be traced back to the 17 th century in propaganda, which became misinformation in the cold war [1]. In modern days, this problem has become grave due to the emergence of social media platforms. Specifically, in the past few years, social media channels, such as Facebook, Twitter, and Instagram, have emerged as platforms for quick dissemination and retrieval of information. Figure 1 shows a snapshot of some fake news in recent years. According to various studies [2], almost 50% of the population of developed nations depend on social media for news. The importance of social media cannot be denied, and it has emerged as an effective medium at the time of crises in regard to the role it plays in breaking news, for example [3]. However, one drawback of the convenience provided by social media is the quick dissemination of fake news. In contrast to conventional mediums such as print media or television, the content of social media can be modified by users, thereby enriching the content with their opinions or biases. This can alter the meaning or context of the news altogether [5]. According to various studies, social media is a fertile ground for quick sharing of information without fact checking [1]. Fake news can be defined as the creation or modification of news content by social media user to deliberately or non-deliberately change its apparent meaning or context, contaminating it with their opinion or biases, where the intent may be to jeopardize or harm a person, organization, or society, monetarily or morally. Examples of fake news are sarcasm, memes, fake advertisements, fake political statements, and rumors [3]. A fakester is a term used for a person responsible for spreading fake news. News can have various degrees based on its credibility, i.e., true, half-true, and false [5]. Fake news can be transmitted in the form of images, video, and text. The life cycle of fake news has been described in [6] as the creation, publication, and propagation of the news. The impact of fake news spread on social media is immense [7]. It can cause a decline in stock prices, a drop in potential investments, etc. [6]. For instance, the 2016 US election was heavily impacted by fake news [2]. The fake news about the death of President [PERSON] led to the loss of USD 130 billion in the stock market in just a fraction of time. The intent of fake news may be to malign someone for political or personal intent or to mislead people [6]. There are numerous websites used for detecting fake news, such as FactCheck, Snopes, TruthorFiction, and PolitiFact. Moreover, Google has also launched an initiative called Google News Initiative to counter fake news [3]. However, fake news detection is still a cumbersome task. This is because fake news often contains misleading information contaminated with credible facts [2]. The motivation behind fake news can be driven by politics, financial benefit, or ideology [3; 5]. In the literature, various approaches based on linguistic features or deep learning techniques, such as the recurrent neural network, convolutional neural network, transformer, bidirectional encoder representations from transformers (BERT), and their combination, have been used for fake news detection [8]. Detection of fake news can be classified as a binary or multi-class classification problem. Alternatively, it can be modeled as a regression problem. A number of datasets are also available for fake news classification, such as Kaggle, ISOT, and LIAR [3]. Despite the extensive studies being carried out, the problem of fake news detection is still very challenging, and it is believed that it requires a comprehensive multi-phased approach. Addressing this problem, this paper proposes a novel approach to validate the authenticity of news.