Computational Communication Research - Current Issue

The 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.

Political polarization in the United States is often framed as an ideological divide between the left and the right. However, emerging research highlights a second, orthogonal dimension: the divide between establishment and anti- establishment movements. While prior work emphasizes the role of morality in shaping political motivations along both dimensions, we extend this by examining how moral rhetoric structures intergroup dynamics within a two-dimensional political space. Using computational linguistic analysis on over 600,000 posts and comments from three representative political communities on Reddit (r/Trump, r/JoeBiden, and r/SandersForPresident), we investigate how moral foundations are invoked differently across these communities. Our results show that anti-establishment communities exhibit consistently higher levels of moral expression than their establishment counterparts. Yet, patterns of engagement suggest that ideological identity—more than anti-establishment alignment—continues to anchor online political engagement. This study contributes a novel computational framework for mapping social identity dynamics in online political communication. Theoretically, it deepens our understanding of how emerging political identities interact with the ideological divide, revealing that while anti-establishment movements may reshape rhetorical styles, the ideological divide remains the primary axis of online political engagement.

During 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.

ammico (AI-based Media and Misinformation Content Analysis Tool) is a publicly available software package written in Python 3, whose purpose is the simultaneous evaluation of the text and graphical content of image files. After describing the software features, we provide an assessment of its performance using a multi-country, multi-language data set containing COVID-19 social media disinformation posts. We conclude by highlighting the tool’s advantages for communication research.

As media platforms continue to develop content moderation policies, alternative platforms have emerged as safe havens for deplatformed content. As these alternatives to major media platforms grow, the importance of understanding their role in the media ecosystem grows too. In this paper, we perform a longitudinal study of the content dynamics of one such alternative media platform, BitChute. BitChute is an alternative video-hosting site similar to YouTube. We first theorize what technological affordances may drive the supply and demand of content on BitChute. We then test those theories through an analysis of 6,363,596 videos from 82,162 channels, which were viewed 2,868,117,905 times, over 54 months. We find that BitChute’s minimal content moderation drives much of the content supply and demand. Videos which were more offensive, certain, and covered commonly deplatformed topics were most popular. In particular, we find that BitChute fills a demand gap created by moderation policies on major media platforms around COVID-19 and - to a lesser extent - elections fraud. The most popular videos on the platform were re-uploaded videos that were banned by YouTube and Facebook. As a whole, our results suggest that BitChute’s current role is less as a town square and more as a backup for deplatformed video content.

English is the international standard of social research, but scholars are increasingly conscious of their responsibility to meet the need for scholarly insight into communication processes globally. This tension is as true in computational methods as in any other area, with revolutionary advances in the tools for English language texts leaving most other languages far behind. In this paper, we aim to leverage those very advances to demonstrate that multi- language analysis is currently accessible to all computational scholars. We show that English-trained measures computed after translation to English have adequate-to-excellent accuracy compared to source-language measures computed on original texts. We show this for three major analytics—sentiment analysis, topic analysis, and word embeddings—over 16 languages, including Spanish, Chinese, Hindi, and Arabic. We validate this claim by comparing predictions on original language tweets and their back-translations: double translations from their source language to English and back to the source language. Our results suggest that Google Translate, a simple and widely accessible tool, effectively preserves semantic content across languages and methods. Modern machine translation can thus help computational scholars make more inclusive and general claims about human communication.

Misinformation has to be uncovered before it can be used for research purposes. This is a resource intensive process, which is why fact-checks have been a popular data source. They have been used directly as proxy for misinformation and indirectly to identify its sources and analyze its content and spread. However, there is little research on the limitations of fact-checks as a data source. Are there patterns in their topics that might lead to biased research results? How does the fact-checkers’ choice of targeting certain actors and social media platforms influence their article’s content? The study provides answers to these questions. It analyzes fact-checks from four German outlets between 2019 and 2023. The study finds that certain topics appear continuously, while for others coverage is event- driven. Furthermore, political actors are covered only to a small extent and even less when they are the originators of misinformation. Finally, fact-checks focus strongly on misinformation on Facebook and the findings indicate that the topic distribution of fact-checks might be different if other platforms were focused. The article discusses the findings with respect to limitations of fact-checks as a data source and concludes with practical recommendations for future research.

This paper introduces CooRTweet, an innovative R package designed for detecting and analyzing coordinated behavior. CooRTweet’s distinctiveness lies in its essential architecture, derived from a minimal definition of coordinated behavior that captures its core elements in an abstract way. This approach makes it possible for the tool to be applied to the widest range of cases, from mono-modal network analysis on a single social media platform, to multi-modal and cross-platform network analysis, and to any types of objects shared by a network, whether singular identical objects (e.g., the same tweet), similar objects (e.g., clusters of similar images), or complex objects (e.g., a combination of hashtags, images, and emojis). Additionally, it offers a comprehensive view of coordinated activities that include both explicit coordination and organic forms of content sharing. The comprehensive architecture of CooRTweet provides flexibility and a broad scope for analyzing coordinated activities across various digital landscapes. This positions it as a distinctive resource for researchers investigating coordinated communication online. More generally, CooRTweet provides a valuable example to methodologists and research tool developers of how software tools for research can be developed in a generalized and thus flexible way. This is particularly important for social media research, given how quickly new APIs are being released, modified, and even shut down. This paper aims to provide an introduction to CooRTweet and the analysis of coordinated behavior, demonstrating the software’s application through a case study of cross-platform coordinated behavior during the 2021 German elections.

As smartphones become increasingly integral to daily life, their importance for understanding human behavior will only continue to grow. Recognizing the potential of objective data on smartphone usage and the challenges associated with raw Android event log data, this paper provides a foundational guide for extracting meaningful measures of smartphone usage from such data. We describe the characteristics of Android event log data, define key smartphone usage types (i.e., glances, sessions, and episodes), and briefly discuss common challenges in handling these data. The core of the paper presents a detailed practical procedure to extract relevant usage metrics (sessions, glances, app episodes) from raw Android event logs, described visually, verbally, and with pseudo-code (with sample data and code in R available in the supplementary materials). This guide aims to equip researchers with the knowledge and tools to effectively utilize Android event log data, advancing knowledge of smartphone use patterns and their effects.