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  1. 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. 
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  2. 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.

     
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  3. null (Ed.)
    The explosion of user-generated content (UGC)—e.g. social media posts and comments and and reviews—has motivated the development of NLP applications tailored to these types of informal texts. Prevalent among these applications have been sentiment analysis and machine translation (MT). Grounded in the observation that UGC features highly idiomatic and sentiment-charged language and we propose a decoder-side approach that incorporates automatic sentiment scoring into the MT candidate selection process. We train monolingual sentiment classifiers in English and Spanish and in addition to a multilingual sentiment model and by fine-tuning BERT and XLM-RoBERTa. Using n-best candidates generated by a baseline MT model with beam search and we select the candidate that minimizes the absolute difference between the sentiment score of the source sentence and that of the translation and and perform two human evaluations to assess the produced translations. Unlike previous work and we select this minimally divergent translation by considering the sentiment scores of the source sentence and translation on a continuous interval and rather than using e.g. binary classification and allowing for more fine-grained selection of translation candidates. The results of human evaluations show that and in comparison to the open-source MT baseline model on top of which our sentiment-based pipeline is built and our pipeline produces more accurate translations of colloquial and sentiment-heavy source texts. 
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  4. 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. 
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  5. null (Ed.)
    Datasets of documents in Arabic are urgently needed to promote computer vision and natural language processing research that addresses the specifics of the language. Unfortunately, publicly available Arabic datasets are limited in size and restricted to certain document domains. This paper presents the release of BE-Arabic-9K, a dataset of more than 9000 high-quality scanned images from over 700 Arabic books. Among these, 1500 images have been manually segmented into regions and labeled by their functionality. BE-Arabic-9K includes book pages with a wide variety of complex layouts and page contents, making it suitable for various document layout analysis and text recognition research tasks. The paper also presents a page layout segmentation and text extraction baseline model based on fine-tuned Faster R-CNN structure (FFRA). This baseline model yields cross-validation results with an average accuracy of 99.4% and F1 score of 99.1% for text versus non-text block classification on 1500 annotated images of BE-Arabic-9K. These results are remarkably better than those of the state-of-the-art Arabic book page segmentation system ECDP. FFRA also outperforms three other prior systems when tested on a competition benchmark dataset, making it an outstanding baseline model to challenge. 
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  6. null (Ed.)