Fake news classification is one of the most interesting problems that has attracted huge attention to the researchers of artificial intelligence, natural language processing, and machine learning (ML). Most of the current works on fake news detection are in the English language, and hence this has limited its widespread usability, especially outside the English literate population. Although there has been a growth in multilingual web content, fake news classification in low-resource languages is still a challenge due to the non-availability of an annotated corpus and tools. This article proposes an effective neural model based on the multilingual Bidirectional Encoder Representations from Transformer (BERT) for domain-agnostic multilingual fake news classification. Large varieties of experiments, including language-specific and domain-specific settings, are conducted. The proposed model achieves high accuracy in domain-specific and domain-agnostic experiments, and it also outperforms the current state-of-the-art models. We perform experiments on zero-shot settings to assess the effectiveness of language-agnostic feature transfer across different languages, showing encouraging results. Cross-domain transfer experiments are also performed to assess language-independent feature transfer of the model. We also offer a multilingual multidomain fake news detection dataset of five languages and seven different domains that could be useful for the research and development in resource-scarce scenarios.

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When the world is facing the COVID-19 pandemic, society is also fighting another battle to tackle misinformation. Due to the widespread effect of COVID 19 and increased usage of social media, fake news and rumors about COVID-19 are being spread rapidly. Identifying such misinformation is a challenging and active research problem. The lack of suitable datasets and external world knowledge contribute to the challenges associated with this task. In this paper, we propose MiCNA, a multi-context neural architecture to mitigate the problem of COVID-19 fake news detection. In the proposed model, we leverage the rich information of the three different pre-trained transformer-based models, i.e., BERT, BERTweet and COVID-Twitter-BERT to three different aspects of information (viz. general English language semantics, Tweet semantics, and information related to tweets on COVID 19) which together gives us a single multi-context representation. Our experiments provide evidence that the proposed model outperforms the existing baseline and the candidate models (i.e., three transformer architectures) and becomes a state-of-the-art model on the task of COVID-19 fakenews detection. We achieve new state-of-the-art performance on a benchmark COVID-19 fake-news dataset with 98.78% accuracy on the validation dataset and 98.69% accuracy on the test dataset. CCS CONCEPTS• Computing methodologies → Language resources; Machine learning algorithms.

In this article, we present a description of our systems as a part of our participation in the shared task namely Artificial Intelligence for Legal Assistance (AILA 2019). This is an integral event of Forum for Information Retrieval Evaluation -2019. The outcomes of this track would be helpful for the automation of the working process of the Indian Judiciary System. The manual working procedures and documentation at any level (from lower to higher court) of the judiciary system are very complex in nature. The systems produced as a part of this track would assist the law practitioners'. It would be helpful for common men too. This kind of track also opens the path of research of Natural Language Processing (NLP) in the judicial domain. This track defined two problems such as Task 1 : Identifying relevant prior cases for a given situation and Task 2 : Identifying the most relevant statutes for a given situation. We tackled both of them. Our proposed approaches are based on BM25 and Doc2Vec. As per the results declared by the task organizers', we are in 3rd and a modest position in Task 1 and Task 2 respectively.