NLP -based Feature Extraction for the Detection of COVID -19 Misinformation Videos on Y ou T ube (2020)

Abstract: We present a simple NLP methodology for detecting COVID-19 misinformation videos on YouTube by leveraging user comments. We use transfer learning pre-trained models to generate a multi-label classifier that can categorize conspiratorial content. We use the percentage of misinformation comments on each video as a new feature for video classification.

## introduction the covid-19 health crisis was accompanied by a misinfodemic: the limited knowledge on the nature and origin of the virus gave ample space for the emergence of conspiracy theories, which were diffused on youtube, and online social networks. although youtube accelerated attempts to detect and filter related misinformation, it yielded moderate results @xcite . in this study, we present a simple nlp-based methodology that can support fact checkers in detecting covid-19 misinformation on youtube. instead of training models on the videos themselves and predicting their nature, we exploit the vast amount of available comments on each youtube video and extract features that can be used in misinformation detection. our methodology comes with the advantage that labeling comments is simpler and faster than video labeling. additionally, no complex neural architecture is needed for the classification of videos. our study provides the following contributions: ## related work previous research studies have extensively investigated the possibilities and limits of nlp for detecting misinformation. researchers have provided theoretical frameworks for understanding the lingual and contextual properties of various types of misinformation, such as rumors, false news, and propaganda @xcite @xcite . given the general difficulty in detecting misinformation, scientists have also developed dedicated benchmark datasets to evaluate the effectiveness of nlp architectures in misinformation-related classification tasks @xcite . given the vast amount of misinformation appearing in online social networks, various research studies propose case-specific nlp methodologies for tracing misinformation. for example, della vedova et al. (2018) and popat et al. (2018) combined lingual properties of articles and other meta-data for the detection of false news. volkova et al. (2017), qazvinian et al. (2011) and kumar and carley (2019) created special architectures that take into consideration the microblogging structure of online social networks, while de sarkar et al. (2018) and gupta et al. (2019) exploited sentencelevel semantics for misinformation detection. despite the deployment of such architectures for fact checking, locating malicious content and promptly removing it remains an open challenge @xcite . in the case of covid-19 misinformation, a large share of conspiratorial contents remain online on youtube and other platforms, influencing the public despite content moderation practices @xcite @xcite . given this, it is important to develop case-specific nlp tools that can assist policymakers and researchers in the process of detecting covid-19 misinformation and managing it accordingly. towards this end, we illustrate how nlp-based feature extraction @xcite based on user comments can be effectively used for this task. user comment data has been employed to annotate social media objects @xcite , infer the political leaning of news articles @xcite , and to predict popularity @xcite . previous studies explicitly employed comments as proxies for video content classification @xcite @xcite . however, only @xcite have analyzed user content to identify misinformation. however, they focused on linguistic signals and concluded that users' comments were not strong signals for detecting misinformation. ## discussion we have leveraged large quantities of user comments to extract a simple feature that is effective in predicting misinformation videos. given that the classifier is also accurate for videos with few comments, it can be used for online learning. for example, the user comments of videos containing coronavirus can be tracked and classified as they are posted. high levels of conspiracy comments could then indicate that the video includes misinformation claims. for this to work, it is not necessary to have a conspiracy classifier with perfect accuracy given that the percentage of conspiracy comments feature is based on aggregating the classification results from all the comments. an improved classifier would be able to define a threshold that allows a balanced number of false positives and true negatives. the average percentage of conspiratorial comments would be maintained, irrespective of the wrong classifications. on the other hand, the accuracy of the video classifier is more critical. we found that using simple classifiers on the raw