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This paper aims to systematically visualize the structure and trends from 2005 to 2021, which will help scholars gain a deeper appreciation for existing studies and grasp future research possibilities and directions.

The approach is bibliometric, using VOSviewer and CiteSpace to analyze 765 journal articles and reviews from the Web of Science (WoS) and Scopus databases over the past 16 years.

There is considerable interest in urban tourism destination image (U-TDI), partly because of the role of image in promoting the economic development of urban tourism and the associated benefits to stakeholders. Most research output concerns China, the USA, Spain and the United Kingdom (UK); research in the USA context has had a particularly wide range of influence. Highly cited journals play a crucial role, while subject structure, key articles and high-frequency keywords indicate popular topics, sub-themes and development trends. Drawing on these findings, the authors identify four topics that deserve further study.

This systematic review will enhance understanding of U-TDI research and inform future research directions as well as highlighting the need to explore destination image in greater depth, it guides policymakers in the tourism industry seeking to develop city image.

This study aims to use a bottom-up, inductive approach to derive destination image attributes from large quantities of online consumer narratives and establish a destination classification system based on relationships among attributes and places.

Content and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then used to group destinations by attributes, and ANOVA provided comparisons.

Twenty-two attributes were identified and combined into three groups (core, expected, latent). Destinations were classified into three clusters (comprehensive urban, scenic and lifestyle) based on their network centralities. Using data on Chinese tourism, the most mentioned (core) attributes were determined to be landscape, traffic within the destination, food and beverages and resource-based attractions. Social life was meaningful in consumer narratives but often overlooked by researchers.

Destinations should determine into which category they belong and then appeal to the real needs of tourists. Destination management organizations should provide the essential attributes while paying greater attention to highlighting the destinations’ social life atmosphere.

This research produced empirical work on Chinese tourism by combining a bottom-up, inductive research design with big data. It divided the 49 destinations into three categories and established a new system based on rich data to classify travel destinations.

本研究旨在使用自下而上的归纳方法从大量的在线消费者的叙述中总结出目的地形象的属性, 并根据目的地形象的属性和地点之间的关系建立一个目的地分类系统。

首先通过内容分析方法和社会网络分析方法分析在线消费者的叙述数据得出目的地的消费者形象结构, 然后采用聚类分析方法按照属性对目的地形象进行分组, 并通过方差分析进行比较。

结果显示总结出22种属性, 并将其组合为三组(核心、预期和潜在)。目的地根据其网络中心度被分为三个集群(综合城市、风景和生活方式)。最常被提及的(核心)属性是景观、目的地的交通、食品和饮料以及资源型景点。在消费者的叙述数据中表明社会生活是有意义的, 但常常被研究人员忽视。

首先本研究通过将自下而上的归纳研究设计与大数据相结合对中国旅游业进行了实证研究。其次通过将49个旅游目的地分为三类以及基于大数据建立了一个新的旅游目的地分类系统。

旅游目的地应该明确自己属于哪一类目的地类型然后迎合游客的真正需求。DMOs应该提供旅游目的地的基本属性, 注重提升旅游目的地的社会生活氛围。

Se realizó un análisis de contenido en redes sociales para explorar la estructura de la imagen de los destinos por parte de los consumidores basándose en las descripciones en línea. A continuación, se empleó el análisis de clusters para agrupar los destinos por atributos, estableciendo comparaciones mediante el análisis ANOVA.

Los propósitos de esta investigación eran utilizar un enfoque ascendente e inductivo para obtener atributos de imagen de los destinos a partir de grandes cantidades de descripciones de consumidores en línea, y establecer un sistema de clasificación de destinos basado en las relaciones entre atributos y lugares.

Se identificaron 22 atributos que luego se agruparon en tres grupos (principales, esperados, latentes). Los destinos se clasificaron en tres grupos (urbano integral, paisajístico y de estilo de vida) en función de sus centralidades de red. Utilizando datos sobre el turismo chino, se determinó que los atributos (centrales) más mencionados eran el paisaje, el tráfico dentro del destino, la comida y las bebidas, y las atracciones basadas en los recursos. La vida social era importante en los comentarios de los consumidores, pero a menudo los investigadores la pasaban por alto.

Los destinos deberían determinar a qué categoría pertenecen y luego apelar a las necesidades reales de los turistas. Los DMO deberían proporcionar los atributos esenciales prestando mayor atención a resaltar el entorno de vida social de los destinos.

Esta investigación elaboró un trabajo empírico sobre el turismo chino combinando un diseño de investigación inductiva ascendente con big data. Dividió los 49 destinos en tres categorías y estableció un nuevo sistema basado en los grandes datos para clasificar los destinos turísticos.

Satellite images can be a powerful source of data for analyses of conflict dynamics and social movements, but sociology has been slow to develop methods and metadata standards for transforming those images into data. We ask: How can satellite images become useful data? What are the key methodological and ethical considerations for incorporating high-resolution satellite images into conflict research? Why are metadata important in this work? We begin with a review of recent developments in satellite-based social scientific work on conflict, then discuss the technical and epistemological issues raised by machine processing of satellite information into user-ready images. We argue that high-resolution images can be useful analytical tools provided they are used with full awareness of their ethical and technical parameters. To support our analysis, we draw on two novel studies of satellite data research practices during the Syrian war. We conclude with a discussion of specific methodological procedures tried and tested in our ongoing work.

Image Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.

The Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.

The research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.

This research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Understanding the multifaceted images of tourism destinations is critical for effective destination marketing and management strategies. Traditional approaches, including conceptualization of destination images or analysis of their antecedents and consequences, are commonly used. This study aims to advocate the inclusion of visitors’ latent profiles based on cognitive images to enrich the evaluation and formulation of destination marketing and management strategies.

The analysis focuses on Serbia, an emerging destination, that attracts an increasing number of first-time, repeat and prospective visitors. Exploratory factor analysis and confirmatory factor analysis were used to test the potential dimensions (tangible and intangible cultural destination; infrastructural and accessible destination; active, nature and family destination; sensory and hospitable destination; and welcoming, value for money (VFM) and safe destination) of the cognitive destination image factors scale while subtypes (profiles) were obtained using latent profile analysis (LPA).

The cognitive image component encompasses the perceived attributes of a destination, whether derived from direct experience or acquired through other means. The study identified the following profiles: conventional destination; sensory and hospitable destination; welcoming, VFM and safe destination; secure and active family destination and accessible cultural destination, which are presented individually with their sociodemographic assets.

The main contribution of the paper is the application of a novel method (LPA) for profiling visitor segments based on cognitive destination image. From a theoretical perspective, this research contributes to the extant body of literature pertaining to the destination image, thereby facilitating the identification of discrete latent visitor segments and elucidating noteworthy differences among them concerning a cognitive image.

Photographs are primary source documents that, like manuscripts and printed documents, carry layers of embedded information. As an example of a research strategy that can be used to study the time, place, and context of the development of early photographic businesses in America, a project to research and geo-reference the early photographic studios in New York City using information culled from imprints, census records, city directories, and other period sources is described. This case study example will focus on analyzing photographs and photographers operating in New York City and Brooklyn from the birth of popular photography in the 1840s to ca 1870s, and what researchers can learn about the development of the urban environments during this era. The study will provide an example of a research trajectory, brief background on the processes and early photographic business development, and note some of the research challenges that arise using historic photographs to study urban environments.

This study aims to examine how veganism is “seen” by young adult non-vegan consumers and how prevailing attitudes reinforce or challenge stigmas around veganism.

Photovoice methodology was used to explore young non-vegan consumers’ attitudes and beliefs towards veganism. Data was collected from students studying advertising at a major university in Australia, who produced images and narratives reflective of their own attitudes towards veganism. Polytextual thematic analysis of the resulting visual data was then undertaken to reveal the dominant themes underpinning participants’ attitudes. Participant narratives were then reviewed to confirm whether the ascribed meaning aligned with participants’ intended meaning.

Participant images were reflective of first, how they saw their world and their place within it, which showed the interplay and interconnectedness between humans, animals and nature, and second, how they saw vegans within this world, with both positive and negative attitudes expressed. Interestingly, vegans were simultaneously admired and condemned. By situating these attitudes along a spectrum of moral evaluation, bounded by stigmatisation and moral legitimacy, participants saw vegans as being either Radicals, Pretenders, Virtuous or Pragmatists. For veganism to become more widely accepted by non-vegans, there is an important role to be played by each vegan type.

This study offers a more nuanced understanding of how and why dissociative groups, such as vegans, become stigmatised, which has implications for messaging and marketing practices around veganism and associated products/services. Future research could use a similar methodology to understand why other minority groups in society are stereotyped and stigmatised, which has broader social implications.

This study aims to increase the understanding of the emergence of a values-based (VB) premium private label (PL) brand reputation within a multiple-tier PL brand portfolio in retailing.

By building on the research on PLs, brand image, brand reputation and consumer values, this study creates a conceptual foundation for the emergence of VB PL brand reputation within a multiple-tier brand portfolio among consumers and examines the emergence of such reputation empirically using interpretive exploratory qualitative laddering interviews in the context of fast-moving consumer goods.

The findings of this study illustrate that the VB reputations of the premium PL product brand and the PL brand store intertwine, ultimately relating to two terminal values: pleasure and doing good. These reputations differ remarkably from the VB reputations of the economy PL brand and the umbrella brand of the retail chain (not doing good and financial security).

This study explains the emergence of VB brand reputation within a multiple-tier brand portfolio and introduces the use of the laddering technique in such research.

This study reminds brand managers to carefully design the relevant brand strategy for brands and their relationships under a brand umbrella.

Although much is known about PL brands and brand reputation, to the best of the authors’ knowledge, this study might be the first to increase the understanding of how a VB premium PL brand reputation emerges and accumulates from brand images within a multiple-tier brand portfolio.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.