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The purpose of this paper is to use user-generated content (UGC) on social media platforms to infer the possible place brand identities of two famous metropolitan areas in Bangkok, Thailand, namely, Khaosan Road and Yaowarat (Bangkok’s Chinatown), both of which are famous for their street vendors and nightlife. These two places are interesting study sites because of recent identity conflicts among their stakeholders. The method developed in this research can help other places to better understand place brand identities and, as such, effectively plan for and manage those places.

The author used content analysis to study 782 user-generated images on Flickr and 9,633 user-generated textual reviews of Khaosan Road and Yaowarat from TripAdvisor and Google Maps’ Local Guide. MAXQDA was used to code all the images. User-generated textual reviews were studied using Leximancer. The author also introduced a positivity of concept analysis to identify positive and negative components of place brand identity.

The author developed a place brand identity framework that includes three pillars, namely, place physics, place practices and place personality. Content analysis of the images generated 105 codes and a count of the frequency of the codes that represent place brand identity. Content analysis of textual reviews created the concepts in the three pillars and identified the positive and negative concepts for both places. The results of both image and text analyses showed that street food vending is one of the most salient components of place brand identity for both Khaosan Road and Yaowarat.

The author suggested several place branding strategies for the Bangkok Metropolitan Administration such as turning Khaosan Road into a music scene for both visitors and locals, controlling excessive and aggressive commercialism, sponsoring the production of creative and authentic content, initiating a compelling online campaign that focusses on the items sold in Yaowarat, hosting a spotlight event such as a seafood festival and improving hygiene and walkability.

Both the advancement of digital technologies and the complexity of stakeholders create a need for empirical studies on place branding involving the participation of the widest possible range of stakeholders and studies on the influence of social media. This research is the first to use both image and text analyses to study place brand identity from UGC. The use of both analyses allows the two methods to complement one another while mitigating the weaknesses of each.

This paper aims to introduce a novel method for the analysis of open cell porous components fabricated by laser-based powder bed metal additive manufacturing (AM) for the purpose of quality control. This method uses photogrammetric analysis, the extraction of geometric information from an image through the use of algorithms. By applying this technique to porous AM components, a rapid, low-cost inspection of geometric properties such as material thickness and pore size is achieved. Such measurements take on greater importance, as the production of porous additive manufactured orthopaedic devices increases in number, causing other, slower and more expensive methods of analysis to become impractical.

Here the development of the photogrammetric method is discussed and compared to standard techniques including scanning electron microscopy, micro computed tomography scanning and the recently developed focus variation (FV) imaging. The system is also validated against test graticules and simple wire geometries of known size, prior to the more complex orthopaedic structures.

The photogrammetric method shows an ability to analyse the variability in build fidelity of AM porous structures for use in inspection purposes to compare component properties. While measured values for material thickness and pore size differed from those of other techniques, the new photogrammetric technique demonstrated a low deviation when repeating measurements, and was able to analyse components at a much faster rate and lower cost than the competing systems, with less requirement for specific expertise or training.

The advantages demonstrated by the image-based technique described indicate the system to be suitable for implementation as a means of in-line process control for quality and inspection applications, particularly for high-volume production where existing methods would be impractical.

Along with the ever growingWeb is the proliferation of objectionable content, such as sex, violence, racism, etc. We need efficient tools for classifying and filtering undesirable web content. In this paper, we investigate this problem through WebGuard, our automatic machine learning based pornographic website classification and filtering system. Facing the Internet more and more visual and multimedia as exemplified by pornographic websites, we focus here our attention on the use of skin color related visual content based analysis along with textual and structural content based analysis for improving pornographic website filtering. While the most commercial filtering products on the marketplace are mainly based on textual content‐based analysis such as indicative keywords detection or manually collected black list checking, the originality of our work resides on the addition of structural and visual content‐based analysis to the classical textual content‐based analysis along with several major‐data mining techniques for learning and classifying. Experimented on a testbed of 400 websites including 200 adult sites and 200 non pornographic ones, WebGuard, our Web filtering engine scored a 96.1% classification accuracy rate when only textual and structural content based analysis are used, and 97.4% classification accuracy rate when skin color related visual content based analysis is driven in addition. Further experiments on a black list of 12 311 adult websites manually collected and classified by the French Ministry of Education showed that WebGuard scored 87.82% classification accuracy rate when using only textual and structural content‐based analysis, and 95.62% classification accuracy rate when the visual content‐based analysis is driven in addition. The basic framework of WebGuard can apply to other categorization problems of websites which combine, as most of them do today, textual and visual content.

This paper aims to quantify atmospheric corrosion by image analyses. The corrosion extent, form and distribution of corrosion product on Q235B and T91 steels exposed to a Zhoushan marine atmosphere over one year are characterized by image analysis.

Image analysis of corrosion images were achieved using the gray value, wavelet analysis and fuzzy Kolmogorov–Sinai (K–S) entropy.

As corrosion becomes extensive, the gray value of corrosion images decreases, and the energy value of nine subimages after wavelength decomposition decreases. Fuzzy K–S entropy increases as localized corrosion propagates but decreases as uniform corrosion spreads.

The methods proposed in this work open a new way for fast corrosion evaluation of metallic materials exposed to atmospheric conditions.

In the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.

The study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.

In the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.

Previous research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

The development of qualitative research methods addresses the need to explore, understand and interpret complex and subjective phenomena across various fields of study. These methods are guided by methodological frameworks, and data collection involves taking several precautions for observation or interviews. While these guidelines facilitate an emphasis on the objective aspects of discourse, accounting for subjectivity and emotions proves more challenging. However, these subjectivity and emotions are deemed as significant sources of information. In this chapter, we propose an innovative data collection method centred around creating collages and engaging in group discussions to decipher their meaning. Collage serves as a visual medium, and we recommend utilising semiotic analysis tools to comprehend its significance. To gain a more precise understanding of the value of collage as a data collection method, we studied a collage workshop organised by CREE. Through image analysis and exchanges, our findings reveal that collage acts as a physical medium that fosters exchanges, deepens ideas and restricts digressions. Additionally, collage allows for the expression and discussion of emotions linked to the image rather than the individual. The space of intersubjective reflexivity facilitated by collage enables a profound comprehension, critical assessment and augmentation of ideas and the interpretation of emotions without compromising the sensitivity of the author. This chapter’s main contribution is evidently manifested here.

Can sustainability disclosures be a tool for executing image restoration strategies after corporate manslaughter? This is the question explored in this study of Costa Crociere's sustainability reports after the Concordia disaster.

Merging traditional textual content analysis with visual analysis and supported by machine learning tools, this is a predominantly qualitative study framed by legitimacy theory, image restoration theory and impression management.

Costa Crociere's voluntary sustainability reporting is strongly influenced by a mix of text and visual signals that distract readers' attention from the disaster. A “nothing really happened” communication strategy pervades the disclosures, with the only rational motivation being to change perceptions and erase memories of this tragic and avoidable event.

Although the analysis covered multiple sources of corporate information, media coverage was not one of them. A more in-depth exploration of sustainability reporting in the cruise industry, including evidence of similar cases, to test impression management theory would be a worthwhile avenue for future research.

While Costa Crociere technically followed the customary guidelines of disclosing human resource impacts, there was almost no acknowledgement of the people involved in the accident. Costa Concierevastly understated their responsibility for the accident, did not apologize, and conveyed very little remorse. The majority of disclosures centred on disaster recovery management.

The authors discuss why and how a company can overcome a legitimacy threat by completely freezing its voluntary sustainability reporting, and the authors show how a company can restore its image by minimizing specific aspects of an accident and shifting attention from the human victims to corporate operations. Incorporating image recognition driven by AI models and combining the results with narrative disclosures contributes an innovative and original analysis technique to the field of impression management. In addition, this research also contributes to our knowledge on the cruise industry – a sector currently under scrutiny for its ethical, social and environmental practices.

World Tourism Organization (WTO) recognizes image as the main aspect to be considered by a destination in its promotion and marketing process. Cities try to build valued and recognized images, established from an identity defined based on their own values. One of the strategies adopted for this construction is to hold events, through which it is possible to promote tourism, move the economy, improve the infrastructure, change the image and influence intentions to visit the destination. From the point of view of supply and demand, theorists have proposed two categories of destination image: the projected image and the perceived image. In this context, the objective of the research was to propose a model for measuring the Projected and Perceived Image through the Organizational Identity of the Volvo Ocean Race Brazil.

With a quali-quantitative approach, the study universe is composed of in-depth interviews with the main members of the organizing committee, documentary and netnographic analysis of the event's social networks. For the analysis and interpretation of qualitative data, the collective subject discourse was used. Documentary and netnographic analysis were by means of deductive content analysis and correspondence analysis.

The results supported the three secondary hypotheses of the research, leading to confirm the central hypothesis that the constructed organizational identity, projected by the image, is perceived by visitors to the event studied.

It is understood as fundamental the expansion of studies regarding projected and perceived image, identity and the possibility of its application in tourist events, as social representations, as support also for the definition of management and marketing strategies.

Lake tourism is a growing academic sub-field of tourism studies with an emerging body of literature. However, little research attention has been given to lake-destinations’ projected or perceived tourism images. Specifically, there has been a scarcity of literature investigating the variables involved in the formation of a lake-destination image. Therefore, this study aims to explore the main attributes that might potentially influence this type of destination, and simultaneously, contribute to conceptualizing and defining lake tourism as recent research area. An explorative study was then conducted in order to generate a set of image variables through the use of textual and photographic data. The results will contribute to characterize potential lake-destinations and to develop a final list of variables specifically related to this type of destination.

Closed circuit television (CCTV) imaging is an increasingly used technology and it is now common place for law enforcement to access CCTV footage as an investigative tool to assist in the nomination of a person of interest, or to aid in the prosecution of an offender. The purpose of this paper is to discuss the role of imaging practitioners in the analysis and interpretation of CCTV images within a law enforcement context. It explores and addresses the limitations of CCTV imaging in evidence with a focus on the interpretation of changes in the visual representation of clothing items.

This paper demonstrates the variations observed in four dark toned garments imaged using one CCTV camera with two different recording settings – visible light and near infrared. The device used was installed and operated in a manner comparable to that used in the public domain, the resulting images indicative of those experienced in casework.

The results display a noticeable change to the tonality of each clothing item between the varied recording conditions. These inconsistencies highlight the limitations of layperson analysis and identify the importance of the inclusion of imaging practitioners when interpreting and analysing such images as evidence.

With an abundance of images in the society, layperson interpretation has become common place. Recognising the value of trained imaging practitioners who can assist law enforcement in analysis and interpretation is paramount to ensuring CCTV images as evidence are used appropriately.