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This study explores the affective responses and newsworthiness perceptions of generative AI for visual journalism. While generative AI offers advantages for newsrooms in terms of producing unique images and cutting costs, the potential misuse of AI-generated news images is a cause for concern. For our study, we designed a 3-part news image codebook for affect-labeling news images based on journalism ethics and photography guidelines. We collected 200 news headlines and images retrieved from a variety of U.S. news sources on the topics of gun violence and climate change, generated corresponding news images from DALL-E 2 and asked annotators their emotional responses to the human-selected and AI-generated news images following the codebook. We also examined the impact of modality on emotions by measuring the effects of visual and textual modalities on emotional responses. The findings of this study provide insights into the quality and emotional impact of generative news images produced by humans and AI. Further, results of this work can be useful in developing technical guidelines as well as policy measures for the ethical use of generative AI systems in journalistic production. The codebook, images and annotations are made publicly available to facilitate future research in affective computing, specifically tailored to civic and public-interest journalism. 

We aim to develop methods for understanding how multimedia news exposure can affect people’s emotional responses, and we especially focus on news content related to gun violence, a very important yet polarizing issue in the U.S. We created the dataset NEmo+ by significantly extending the U.S. gun violence news-to-emotions dataset, BU-NEmo, from 320 to 1,297 news headline and lead image pairings and collecting 38,910 annotations in a large crowdsourcing experiment. In curating the NEmo+ dataset, we developed methods to identify news items that will trigger similar versus divergent emotional responses. For news items that trigger similar emotional responses, we compiled them into the NEmo+-Consensus dataset. We benchmark models on this dataset that predict a person’s dominant emotional response toward the target news item (single-label prediction). On the full NEmo+ dataset, containing news items that would lead to both differing and similar emotional responses, we also benchmark models for the novel task of predicting the distribution of evoked emotional responses in humans when presented with multi-modal news content. Our single-label and multi-label prediction models outperform baselines by large margins across several metrics. 

Focusing on a polarized issue—U.S. gun violence—this study examines agenda setting as an antecedent of political expression on social media. A state-of-the-art machine-learning model was used to analyze news coverage from 25 media outlets—mainstream and partisan. Those results were paired with a two-wave panel survey conducted during the 2018 U.S. midterm elections. Findings show mainstream media shape public opinion about gun violence, which then stimulates expression about the issue on social media. The study also reveals that partisan media’s gun violence coverage has significant cross-cutting effects. Notably, exposure to conservative media will decrease public salience of gun violence, pivot opinion in a more conservative direction, and discourage social media expression; and all of these effects are stronger among liberals.

 

Text information in scanned documents becomes accessible only when extracted and interpreted by a text recognizer. For a recognizer to work successfully, it must have detailed location information about the regions of the document images that it is asked to analyse. It will need focus on page regions with text skipping non-text regions that include illustrations or photographs. However, text recognizers do not work as logical analyzers. Logical layout analysis automatically determines the function of a document text region, that is, it labels each region as a title, paragraph, or caption, and so on, and thus is an essential part of a document understanding system. In the past, rule-based algorithms have been used to conduct logical layout analysis, using limited size data sets. We here instead focus on supervised learning methods for logical layout analysis. We describe LABA, a system based on multiple support vector machines to perform logical Layout Analysis of scanned Books pages in Arabic. The system detects the function of a text region based on the analysis of various images features and a voting mechanism. For a baseline comparison, we implemented an older but state-of-the-art neural network method. We evaluated LABA using a data set of scanned pages from illustrated Arabic books and obtained high recall and precision values. We also found that the F-measure of LABA is higher for five of the tested six classes compared to the state-of-the-art method. 

Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists. 

We propose a five-step computational framing analysis framework that researchers can use to analyze multilingual news data. The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual deep learning model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Most importantly, anyone can perform the proposed computational framing analysis using a free, open-sourced system, created by a team of communication scholars, computer scientists, web designers and web developers. Making advanced computational analysis available to researchers without a programming background to some degree bridges the digital divide within the communication research discipline in particular and the academic community in general. 

Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source domain so that a model can be trained with annotations from the latter. However, such methods highly rely on an image translator or feature extractor trained in an elaborated mechanism including adversarial training, which brings in extra complexity and instability in the adaptation process. Furthermore, these methods mainly focus on taking advantage of the labeled source dataset, leaving the unlabeled target dataset not fully utilized. In this paper, we propose a bidirectional style-induced domain adaptation method, called BiSIDA, that employs consistency regularization to efficiently exploit information from the unlabeled target domain dataset, requiring only a simple neural style transfer model. BiSIDA aligns domains by not only transferring source images into the style of target images but also transferring target images into the style of source images to perform high-dimensional perturbation on the unlabeled target images, which is crucial to the success in applying consistency regularization in segmentation tasks. Extensive experiments show that our BiSIDA achieves new state-of-the-art on two commonly-used synthetic-to-real domain adaptation benchmarks: GTA5-to-CityScapes and SYNTHIA-to-CityScapes. Code and pretrained style transfer model are available at: https://github.com/wangkaihong/BiSIDA.

 

Background: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke. Methods: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data. Results: The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P <0.001) or a single feature set (F1 range=0.68–0.84, P <0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87). Conclusions: While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.