Exploiting Diverse Contextual Features through Transformers for Detecting Informative Tweets

Twitter is deemed the most reliable and convenient microblogging platform for getting real-time news and information. During the COVID-19 pandemic, people are keen to share various information ranging from new cases, healthcare guidelines, medication, and vaccine news on Twitter. However, a major portion of the shared tweets is uninformative and misleading which may create mass panic. Hence, it is an important task to distinguish and label a COVID-19 tweet as informative or uninformative. Prior works mostly focused on various pretrained transformer models and different types of contextual feature extractors to address this task. However, most of the works applied these models one at a time and didn't employ any effective neural layer at the bottom to distill the tweet contexts effectively. Since a tweet may contain a multifarious context, therefore, representing a tweet using only one kind of feature extractor may not work well. To overcome this limitation, we present an approach that leverages an ensemble of various cutting-edge transformer models to capture the diverse contextual dimension of the tweets. We exploit the BERT, CTBERT, BERTweet, RoBERTa, and XLM-RoBERTa models in our proposed method. Next, we perform a pooling operation on those extracted embedding features to transform them into document embedding vectors. Then, we utilize a feed-forward neural architecture with a linear activation function for the classification task. To generate final prediction, we utilize the majority voting-driven ensemble technique. Experiments on WNUT-2020 COVID-19 English Tweet dataset manifested the efficacy of our method over other state-of-the-art methods. © 2022 IEEE.

How to Save Lives with Microblogs? Lessons From the Usage of Weibo for Requests for Medical Assistance During COVID-19

During recent crises like COVID-19, microblogging platforms have become popular channels for affected people seeking assistance such as medical supplies and rescue operations from emergency responders and the public. Despite this common practice, the affordances of microblogging services for help-seeking during crises that needs immediate attention are not well understood. To fill this gap, we analyzed 8K posts from COVID-19 patients or caregivers requesting urgent medical assistance on Weibo, the largest microblogging site in China. Our mixed-methods analyses suggest that existing microblogging functions need to be improved in multiple aspects to sufficiently facilitate help-seeking in emergencies, including capabilities of search and tracking requests, ease of use, and privacy protection. We also find that people tend to stick to certain well-established functions for publishing requests, even after better alternatives emerge. These findings have implications for designing microblogging tools to better support help requesting and responding during crises. © 2022 ACM.

Exploiting Ensemble of Transformer Models for Detecting Informative Tweets

Microblogging platforms especially Twitter is considered as one of the prominent medium of getting user-generated information. Millions of tweets were posted daily during COVID-19 pandemic days and the rate increases gradually. Tweets include a wide range of information including healthcare information, recent cases, and vaccination updates. This information helps individuals stay informed about the situation and assists safety personnel in making decisions. Apart from these, large amounts of propaganda and misinformation have spread on Twitter during this period. The impact of this infodemic is multifarious. Therefore, it is considered a formidable task to determine whether a tweet related to COVID-19 is informative or uninformative. However, the noisy and nonformal nature of tweets makes it difficult to determine the tweets' informativeness. In this paper, we propose an approach that exploits the benefits of finetuned transformer models for informative tweet identification. Upon extracting features from pre-trained COVID-Twitter-BERT and RoBERTa models, we leverage the stacked embedding technique to combine them. The features are then fed to a BiLSTM module to learn the contextual dimension effectively. Finally, a simple feed-forward linear architecture is employed to obtain the predicted label. Experimental result on WNUT-2020 benchmark informative tweet detection dataset demonstrates the potency of our method over various state-of-the-art approaches. © 2021 IEEE.