Gender and Candidate Communication: An Analysis of Televised Ads in the 2020 US Senate Races

Recent studies of the content of television ads of female versus male political candidates have shown that women and men are increasingly similar in their communication styles and strategies, with some notable exceptions. However, few studies examining gender and political ad content have focused exclusively on US Senate races, considered the influence of the candidates’ political party, or compared the messages of women running against female versus male opponents. This study examines 236 political ads—160 from mixed-gender and 76 from female–female—U.S. Senate races in 2020 for their verbal and visual content. Results show gendered and partisan differences in the issues emphasized and the tone used. Candidates were similar in the images emphasized. Female candidates were more balanced between formal and casual attire compared to previous election cycles. And candidates in mixed-gender races used different strategies than those in female–female contests as to the issues and political actors mentioned.

Communicating Photograph Content Through Tactile Images to People With Visual Impairments

Millions of people with a visual impairment across the world are denied access to visual images. They are unable to enjoy the simple pleasures of viewing family photographs, those in textbooks or tourist brochures and the pictorial embellishment of news stories etc. We propose a simple, inexpensive but effective approach, to make content accessible via touch. We use state-of-the-art algorithms to automatically process an input photograph into a collage of icons, that depict the most important semantic aspects of a scene. This collage is then printed onto swell paper. Our experiments show that people can recognise content with an accuracy exceeding 70% and create plausible narratives to explain it. This means that people can understand image content via touch. Communicating scene foreground is a step forward, but there are many other steps needed to provide the visually impaired with the fullest possible access to visual content.

There’s Certainly a Lot of History Here, But We’re Here to Roast Oysters: Afterlives of Trans-Atlantic Exchange in Top Chef: Charleston

This article reads the premiere episode of Top Chef’s fourteenth season, Top Chef: Charleston (2016), for its engagement with the history of slavery in the United States, arguing that Top Chef deploys acknowledgments of historical violences for the purpose of concealing those same violences. By analyzing the discursive and visual content of Charleston’s premiere’s elimination challenge, which required two chefs to cook head-to-head at a plantation, this article outlines how race shapes the action of Top Chef both overtly and covertly, emerging as an organizing factor for the program as a whole. Charleston’s premiere episode illuminates how history is repackaged into popular discursive and material formations, while also suggesting the potential for such formations to cohere around race in unexpected and unpredictable ways.

Unfolding visual characteristics of social media communication: reflections of smart tourism destinations

Purpose This study aims to explore the visual social media (SM) (Instagram) communication and the visual characteristics of smart tourism destination (STD) communication from destination marketing/management organizations (DMOs) and user-generated content (UGC) perspectives, which refer to projected image and perceived image, respectively. Design/methodology/approach Three DMO official accounts of STDs (Helsinki, Gothenburg and Lyon) and corresponding official hashtags were selected for the sample and total 6,000 post data (1,000 × 6) were retrieved from Instagram. Visual communication content was examined with a netnographic design over a proposed four-level visual content framework using corresponding methodological approaches (thematic analysis, visual analysis, object detection and text mining) for each level. Findings Among the eight emerging themes dominating the images, communication of smart elements conveys far less than expected textual and visual signals from DMOs despite their smart status, and in turn, from UGC as well. UGC revealed three extra image themes regardless of smartness perception. DMOs tend to project and give voice to their standard metropolitan areas and neighborhoods while UGCs focus on food-related and emotional elements. The findings show a partial overlap between DMOs and UGCs, revealing discrepancies in objects contained in visuals, hashtags and emojis. Additionally, as a rare attempt, the proposed framework for visual content analysis showed the importance of integrated methods to investigate visual content effectively. Research limitations/implications The number of attributes in visual analysis and focusing on the observed elements in text content (text, hashtags and emojis) are the limitations of the study in terms of methodology. Originality/value Apart from the multiple integrated methods used over a netnographic design, this study differs from existing SM and smart destinations intersection literature by attempting to fill a gap in focusing on and exploring visual SM communication, which is scarce in tourism context, for the contents generated by DMOs and users.

A large scale study of reader interactions with images on Wikipedia

Wikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article’s body. However, despite their widespread usage on web platforms and the huge volume of visual content on Wikipedia, little is known about the importance of images in the context of free knowledge environments. To bridge this gap, we collect data about English Wikipedia reader interactions with images during one month and perform the first large-scale analysis of how interactions with images happen on Wikipedia. First, we quantify the overall engagement with images, finding that one in 29 pageviews results in a click on at least one image, one order of magnitude higher than interactions with other types of article content. Second, we study what factors associate with image engagement and observe that clicks on images occur more often in shorter articles and articles about visual arts or transports and biographies of less well-known people. Third, we look at interactions with Wikipedia article previews and find that images help support reader information need when navigating through the site, especially for more popular pages. The findings in this study deepen our understanding of the role of images for free knowledge and provide a guide for Wikipedia editors and web user communities to enrich the world’s largest source of encyclopedic knowledge.

Mapping Imagined Territory. Quaresmio's Chorographia and Later Franciscan Holy Land Maps, in Constructing and Representing Territory in Late Medieval and Early Modern Europe, edited by Mario Damen and Kim Overlaet (Amsterdam University Press).

This contribution examines pre-modern cartography as a territorial technique for representing imagined territory, linking social groups to geographical space. It suggests that pre-modern maps could project territory by means other than visualising boundaries, and that accompanying texts could play a significant role, as in the case of Friar Francesco Quaresmio’s map of the Holy Land in Terrae Sanctae Elucidatio (1639). By analysing the immediate context of Quaresmio’s map – a lavish book publication – I show how Quaresmio’s Chorographia represents Franciscan territorial claims through an interaction between the map’s visual content and its immediate textual context within the book. Like other Franciscan maps also discussed here, it employs the imagined territories of the Bible as a versatile cartographical topos for various purposes, territorial or otherwise.

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.