Machine Learning Approaches to Assess Mood of the News Editorial

Social media and news channels have always been vital sources for spreading information and raising awareness about recent occurrences. As reported by a survey, 82 percent of respondents from India stated that they sourced their news online, which included social media, as of the year 2021, making it a popular form of accessing news1. For a long time, information about COVID - 19 has been one of the most popular topics. News channel networks and editorials were one of the first places where knowledge regarding COVID - 19 was widely disseminated. In this study, sentiment analysis models have been developed to categorize tweets by some of India's most well-known news stations into positive and negative during the COVID - 19 virus was new in India from June 2020 to July 2020. We attempted to do so by developing nine various models based on different datasets and classification algorithms to investigate the news channels' tweets more thoroughly. According to our findings, the model that provided us with the highest accuracy and performance has been trained using the NLTK Dataset and the Logistic Regression Classifier. © 2022 IEEE.

AMUSED: An Annotation Framework of Multimodal Social Media Data

Social media has become popular among users for social interaction and news sources. Users spread misinformation in multiple data formats. However, systematic studying of social media phenomena has been challenging due to the lack of labelled data. This paper presents a semi-automated annotation framework AMUSED for gathering multilingual multimodal annotated data from social networking sites. The framework is designed to mitigate the workload in collecting and annotating social media data by cohesively combining machines and humans in the data collection process. AMUSED detects links to social media posts from a given list of news articles and then downloads the data from the respective social networking sites and labels them. The framework gathers the annotated data from multiple platforms like Twitter, YouTube, and Reddit. For the use case, we have implemented the framework for collecting COVID-19 misinformation data from different social media sites and have categorised 8,077 fact-checked articles into four different classes of misinformation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Cross-lingual COVID-19 Fake News Detection

The COVID-19 pandemic poses a great threat to global public health. Meanwhile, there is massive misinformation associated with the pandemic which advocates unfounded or unscientific claims. Even major social media and news outlets have made an extra effort in debunking COVID-19 misinformation, most of the fact-checking information is in English, whereas some unmoderated COVID-19 misinformation is still circulating in other languages, threatening the health of less-informed people in immigrant communities and developing countries. In this paper, we make the first attempt to detect COVID-19 misinformation in a low-resource language (Chinese) only using the fact-checked news in a high-resource language (English). We start by curating a Chinese realfake news dataset according to existing fact-checking information. Then, we propose a deep learning framework named CrossFake to jointly encode the cross-lingual news body texts and capture the news content as much as possible. Empirical results on our dataset demonstrate the effectiveness of CorssFake under the cross-lingual setting and it also outperforms several monolingual and cross-lingual fake news detectors. The dataset is available at https://github.com/YingtongDou/CrossFake. © 2021 IEEE.