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This study aims to examine the effects of character body size, subject body size and product type on female adolescents’ attitudes toward the character. Given prior research showing that adolescents identify more strongly with those whom they view as similar to themselves, it is possible that heavy and obese adolescents will react more favorably to plus-size ad characters.

Two studies were conducted, one with females aged 12–14 and a second with females aged 15–17. Based on their body mass index, subjects were classified as of small/average size or overweight/obese. Ads featured either a thin, average-size or plus-size character, and promoted either a healthful or an unhealthful snack item.

In general, small/average size female adolescents responded more favorably toward thin characters, whereas their overweight/obese counterparts responded more favorably toward plus-size characters. Moreover, subjects’ responses were not moderated by the nutritional value (healthful vs unhealthful) of the product being advertised.

To effectively promote healthy foods to overweight/obese adolescents, it may be advantageous to incorporate plus-size characters. Additional research is needed, however, to determine whether this approach can effectively influence brand attitudes and consumption behaviors.

As obesity rates continue to rise, it has become vitally important to encourage healthier food choices among youth. To develop effective communication strategies, marketers need to better understand how young consumers respond to various advertising cues.

To the best of the authors’ knowledge, this is the first study to examine the effect of character size and subject size on female adolescents’ attitudes toward the character.

Using Generation Z consumers from China as an example and focusing on the nostalgia-driven design of brand spokes-character, this study sought to update research on the causal relationship between nostalgia and brand attitude for younger consumers. Two types of nostalgic brand spokes-characters (i.e., eliciting personal nostalgia and historical nostalgia) were examined separately and compared to verify their contributions to more positive brand attitude, as well as related mechanisms, that is, whether consumer trust in the spokes-character mediated the relationship between nostalgic spokes-characters and brand attitude.

An experiment was first conducted to test the causal effects of brand spokes-characters designed to elicit two types of nostalgic feelings (i.e., personal nostalgia and historical nostalgia). Then, the authors investigated the influencing mechanism of nostalgic brand spokes-characters based on bootstrap mediation models.

The results revealed that for less familiar brand spokes-characters, either type of nostalgia-driven design would enhance consumers' brand attitude. Moreover, consumer trust in the spokes-character mediated the relationship between personal-nostalgic brand spokes-characters and brand attitude.

This study was the first to examine personal nostalgia and historical nostalgia separately regarding the effects of nostalgic spokes-characters and related mechanisms. By combining methods of experimental design and bootstrap mediation modeling, it provided a more robust evaluation of nostalgia-driven design, and supported using certain nostalgic styles for designing brand spokes-characters, which can help modern brands draw more interest from young consumers and promote more positive brand attitude.

In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages.

In view of the particularity of Chinese text and the requirement of sentiment analysis, a Chinese sentiment analysis model integrating multi-granularity semantic features is proposed in this paper. This model introduces the radical and part-of-speech features based on the character and word features, with the application of bidirectional long short-term memory, attention mechanism and recurrent convolutional neural network.

The comparative experiments showed that the F1 values of this model reaches 88.28 and 84.80 per cent on the man-made dataset and the NLPECC dataset, respectively. Meanwhile, an ablation experiment was conducted to verify the effectiveness of attention mechanism, part of speech, radical, character and word factors in Chinese sentiment analysis. The performance of the proposed model exceeds that of existing models to some extent.

The academic contribution of this paper is as follows: first, in view of the particularity of Chinese texts and the requirement of sentiment analysis, this paper focuses on solving the deficiency problem of Chinese sentiment analysis under the big data context. Second, this paper borrows ideas from multiple interdisciplinary frontier theories and methods, such as information science, linguistics and artificial intelligence, which makes it innovative and comprehensive. Finally, this paper deeply integrates multi-granularity semantic features such as character, word, radical and part of speech, which further complements the theoretical framework and method system of Chinese sentiment analysis.

Hazard warning schemes provide efficient hazard recognition and promote project safety. Nevertheless, these schemes perform poorly because the warning information is calibrated for individual characters and is not prioritized for the entire system. This study proposes a hazard warning scheme that prioritizes hazard characters from the inspection process based on the inspectors' experience.

First, hazard descriptions were decomposed into their characters, forming a double-layer network. Second, warning schemes based on cascading effects were proposed. Third, character-based warning schemes were simulated for various experiences.

The results show that when a specific hazard is detected, the degree centrality is the most effective parameter for prioritization, and hazard characters should be prioritized based on betweenness centrality for experienced inspectors, whereas degree centrality is preferred for novice inspectors.

The warning scheme theoretically supplements the information-processing theory in construction hazard warnings and provides a practical warning scheme with priority for the development of automated hazard navigation systems.

This paper aims to examine how character strengths have an important dual role in mental health in both promoting well-being and mental wellness and also in reducing symptoms and suffering. While there are many studies that have touched upon variables that character strengths can enhance for mental well-being or reduce for suffering, the author actually knows very little about how character strengths might relate to or impact mental health.

A large-scale study of 12,050 individuals was conducted to explore the self-perceived character strengths that are most helpful for mental health, for handling physical adversity, for handling social adversity and for fostering psychological well-being.

Some character strengths showed a general effect – showing a strong perceived impact across multiple domains – such as love, perspective, kindness, hope, humor and curiosity. Other character strengths showed a specific effect in that there was a strong perceived impact in one domain, such as perseverance and self-regulation for physical health, spirituality and social intelligence for social health and creativity for mental health. A strength-based approach to understanding and managing emotions was substantially more preferred than cognitive or behavioral approaches. Other findings examined the character strengths most desired to be improved upon for mental health.

The research strategy was cross-sectional, thereby causality cannot be determined. Because of the large sample size, researchers are encouraged to consider examining the findings in intervention studies.

This study indicates that character strengths are highly relevant for mental health, all 24 character strengths are possible pathways to impact mental health (some more than others) and individuals can readily connect ways they can use their character strengths to positively improve their well-being and manage their suffering.

Character strengths and their substantial positive potential provide an avenue for public impact on a large scale.

To the best of the author’s knowledge, this is the first known study to directly examine multiple intersections among mental health and character strengths in a large sample.

With the rapid advancement of generative artificial intelligence (AI), it is important to consider how young people are making sense of these tools in their everyday lives. Drawing on critical postdigital approaches to learning and literacy, this study aims to center the experiences and perspectives of young people who encounter and experiment with generative AI in their daily writing practices.

This critical case study of one digital platform – Character.ai – brings together an adolescent and adult authorship team to inquire about the intertwining of young people’s playful and critical perspectives when writing on/with digital platforms. Drawing on critical walkthrough methodology (Light et al., 2018), the authors engage digital methods to study how the creative and “fun” uses of AI in youths’ writing lives are situated in broader platform ecologies.

The findings suggest experimentation and pleasure are key aspects of young people’s engagement with generative AI. The authors demonstrate how one platform works to capitalize on these dimensions, even as youth users engage critically and artfully with the platform and develop their digital writing practices.

This study highlights how playful experimentation with generative AI can engage young people both in pleasurable digital writing and in exploration and contemplation of platforms dynamics and structures that shape their and others’ literate activities. Educators can consider young people’s creative uses of these evolving technologies as potential opportunities to develop a critical awareness of how commercial platforms seek to benefit from their users.

This study contributes to the development of a critical and humanist research agenda around generative AI by centering the experiences, perspectives and practices of young people who are underrepresented in the burgeoning research devoted to AI and literacies.

The purpose of this study is to examine the Values in Action (VIA) character strengths profile of people in addiction recovery, to identify which strengths are meaningfully represented in this population. This was compared with the generalised profile of a normative population to identify the differentiating features. Reasons for the profile variance and the significance this has for addiction recovery have also been explored.

An independent group design was adopted using purposive sampling. This saw participants (n = 100) complete the VIA Inventory of Strengths-P assessment to establish a character strengths profile for people in addiction recovery. To identify the differences in this profile, a mean score and rank order comparison was conducted, using data taken from a normative population. Additional exploratory analysis was conducted to establish if there were any significant differences in the character strength profile of males and females.

In descending order, the top five ranked strengths were kindness, humour, honesty, fairness and teamwork. The lesser five strengths in the profile were spirituality, zest, perseverance, prudence and self-regulation. A distinguishing feature was the presence of humour as a top five strength for people in addiction recovery. The existence of teamwork also deviated from the generalised normative population. There were two strengths shown to have a meaningfully higher score for females, teamwork and love. This did not change the strengths present in the overall top five for males or females, however.

This study contributes useful knowledge to the understanding of character strengths in addiction recovery. To the best of the authors’ knowledge, this is the first time character strengths have been examined in a sample of people in addiction recovery.

The purpose of this paper is to add to the understanding of the human personality of fraudsters. This paper will explore their human personality by reviewing three characters from realist novels that have fraudsters as their leading characters. This pa[er will also contribute to literature that intersects between the humanities and criminology.

This paper reviews three fraudster characters from realist novels to explore their human personality, which includes qualitative phenomena resistant to positivist research.

Literature character review that adds to understanding of the qualitative nature of the personality of fraudsters. This qualitative nature of the human personality has been neglected in fraud research and the findings contribute to expanding understanding of the qualitative nature of fraud and fraudsters.

This paper is limited to a literature review from three characters from realist novels.

By expanding understanding of the human personality of fraudsters literary insights can contribute to fraud identification and prevention.

This paper reviews the human personality of three characters from novels to expand understanding of fraudsters, and thus contributes to the intersection of research between the humanities and criminology and fraud research.

Although governments have used social media platforms to interact with the public in an attempt to minimize anxiety and provide a forum for public discussion during the pandemic, governments require sufficient crisis communication skills to engage citizens in taking appropriate action effectively. This study aims to examine how the National Health Commission of China (NHCC) has used TikTok, the leading short video–based platform, to facilitate public engagement during COVID-19.

Building upon dual process theories, this study integrates the activation of information exposure, prosocial interaction theory and social sharing of emotion theory to explore how public engagement is related to message sensation value (MSV), media character, content theme and emotional valence. A total of 354 TikTok videos posted by NHCC were collected during the pandemic to explore the determinants of public engagement in crises.

The findings demonstrate that MSV negatively predicts public engagement with government TikTok, but that instructional information increases engagement. The presence of celebrities and health-care professionals negatively affects public engagement with government TikTok accounts. In addition, emotional valence serves a moderating role between MSV, media characters and public engagement.

Government agencies must be fully aware of the different combinations of MSV and emotion use in the video title when releasing crisis-related videos. Government agencies can also leverage media characters – health professionals in particular – to enhance public engagement. Government agencies are encouraged to solicit public demand for the specific content of instructing information through data mining techniques.

The tender documents, an essential data source for internet-based logistics tendering platforms, incorporate massive fine-grained data, ranging from information on tenderee, shipping location and shipping items. Automated information extraction in this area is, however, under-researched, making the extraction process a time- and effort-consuming one. For Chinese logistics tender entities, in particular, existing named entity recognition (NER) solutions are mostly unsuitable as they involve domain-specific terminologies and possess different semantic features.

To tackle this problem, a novel lattice long short-term memory (LSTM) model, combining a variant contextual feature representation and a conditional random field (CRF) layer, is proposed in this paper for identifying valuable entities from logistic tender documents. Instead of traditional word embedding, the proposed model uses the pretrained Bidirectional Encoder Representations from Transformers (BERT) model as input to augment the contextual feature representation. Subsequently, with the Lattice-LSTM model, the information of characters and words is effectively utilized to avoid error segmentation.

The proposed model is then verified by the Chinese logistic tender named entity corpus. Moreover, the results suggest that the proposed model excels in the logistics tender corpus over other mainstream NER models. The proposed model underpins the automatic extraction of logistics tender information, enabling logistic companies to perceive the ever-changing market trends and make far-sighted logistic decisions.

(1) A practical model for logistic tender NER is proposed in the manuscript. By employing and fine-tuning BERT into the downstream task with a small amount of data, the experiment results show that the model has a better performance than other existing models. This is the first study, to the best of the authors' knowledge, to extract named entities from Chinese logistic tender documents. (2) A real logistic tender corpus for practical use is constructed and a program of the model for online-processing real logistic tender documents is developed in this work. The authors believe that the model will facilitate logistic companies in converting unstructured documents to structured data and further perceive the ever-changing market trends to make far-sighted logistic decisions.