- Home
- A-Z Publications
- Computational Communication Research
- Previous Issues
- Volume 7, Issue 1, 2025
Computational Communication Research - Volume 7, Issue 1, 2025
Volume 7, Issue 1, 2025
-
-
Boosting Transformers: Recognizing Textual Entailment for Classification of Vaccine News Coverage
Authors: Luiz Neves, Chico Camargo & Luisa MassaraniThe introduction of Transformers, neural networks employing self-attention mechanisms, revolutionized Natural Language Processing, handling long-range dependencies and capturing context effectively. Models like BERT and GPT, trained on massive text data, are at the forefront of Large Language Models and have found widespread use in text classification. Despite their benchmark performance, real-world applications pose challenges, including the requirement for substantial labeled data and class balance. Few-shot learning approaches, like the Recognizing Textual Entailment framework, have emerged to address these issues. RTE identifies relationships between a text T and a hypothesis H. T entails H if the meaning of H, as interpreted in the context of T, can be inferred from the meaning of T. This study explores an RTE- based framework for classifying vaccine-related news headlines with only 751 labeled data points distributed unevenly across 10 classes. The study evaluates eight models and procedures. The results highlight that deep transfer learning, combining language and task knowledge, like Transformers and RTE, enables the development of text classification models with superior performance, effectively addressing data scarcity and class imbalance. This approach provides a valuable protocol for creating new text classification models and delivers an advanced automated model for classifying vaccine- related content.
-
-
-
Candidate Party, Gender, and the Face Mask as a Political Symbol in Campaign Advertisements
Authors: Jielu Yao, Travis Ridout, Markus Neumann & Erika Franklin FowlerDuring the COVID-19 pandemic, wearing a face mask became politicized in the United States, with politicians and reporters employing competing public safety and civil liberties frames in discussions of masking. In this research, we argue that political candidates’ decisions to speak about and depict mask-wearing in their political advertising were strategic, depending on both the candidate’s party and gender. We examine political ads run on Facebook and on television by federal candidates during the 2020 U.S. campaigns. We use Amazon’s deep learning algorithms for PPE (personal protective equipment) detection. We extract the text and audio of each ad to identify mentions of masks and use an à la Carte embedding regression model to understand how the usage of the term mask differs across covariates. We find that images of masks are much more common than mentions of masks, that there are significant partisan, but not gender, differences in the use of masks, and that there are both partisan and gender differences in the way that candidates speak about masking. This research demonstrates the utility of a novel approach to collecting data. It also suggests that public health measures can become partisan in a campaign environment, with the potential to polarize both the views and behaviors of Democrats and Republicans.
-
Most Read This Month

Most Cited Most Cited RSS feed
-
-
Computational observation
Authors: Mario Haim & Angela Nienierza
-
- More Less