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With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

We argue this method of inquiry better represents the participants' learning, lives and experiences in the formal neoliberal education system prioritising performativity, categorising and ranking students.

The paper explores using poetry as a research method to reveal the learning experiences of adult learners, who have often had disruptive experiences of the formal schooling system and return to study in community-based education spaces. Inspired by Laurel Richardson’s transgressive technique of presenting sociological data through poetry as method, we use poetic representations of these learners' lives alongside case study research methodology. The research was conducted in conjunction with Neighbourhood Houses in Victoria, Australia. Qualitative data were generated through conducting multiple case studies of learners across various adult community education (ACE) sites. In this research, some case studies were presented in the traditional method of writing biography, others were written in the form of found poetry, which we refer to as data as poetry and text. The paper uses found poetry through participant-voiced poems written from interview transcripts. We argue this method of inquiry better represents the participants' learning, lives and experiences in the formal neoliberal education system prioritising performativity, categorising and ranking students. Our findings highlight the benefits of using poetry to communicate data in case study research as it effectively represents the experiences of adult learners' lives in a creative and concise form, transgressing normative practices of writing education research. These poetic representations of data reveal learner experiences in an embodied and agentic way while providing readers with a deep and rich understanding of these crucial adult learning spaces.

Our findings highlight the benefits of using poetry to communicate data in case study research as it effectively represents the experiences of adult learners' lives in a creative and concise form, transgressing normative practices of writing education research.

This research paper is empirical research and has not been submitted elsewhere for publication.

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

This study aims to explore the impact of board size, board gender diversity and federation age on the likelihood of having a female chair in National Sports Federations (NSF).

A quantitative methodology compares 300 sports boards in five countries (Italy, Portugal, Spain, Turkey and the UK), using data collected from NSF’s websites.

The board size and federation age have no significant impact on having a female board chair when the countries and the percentage of female directors are included in the model. When the number of women is measured in absolute value rather than in relative terms, the only variable that predicts a woman chair is the country. When the model does not include country differences, the percentage of female directors is key in predicting a chairwoman, and when the number of women is used as a variable instead of the percentage, a board’s smaller size increases the odds of having a chairwoman.

There are some limitations to this study which we believe provide useful directions for future research. Firstly, the authors have not considered the role of gender typing in sports activities which explains the extent that women participate in specific sports (Sobal and Milgrim, 2019) and the related perception of such sports in society. The social representation of sports activities classified as masculine, feminine or gender-neutral can hypothetically influence women’s access to that specific federations’s leadership. The authors included the country factor only partially, as a control variable, as the social representation of sports usually goes beyond national boundaries.

This study has implications for sport policymakers and stakeholders, and for institutions such as the IOC or the European Union that implement equality policies. If the aim is to increase female presence in the highest position of a sports board and to achieve gender equality more generally, other policies need to be implemented alongside gender quotas for the sports boards, namely, those specifically related to the recruitment and selection of the sports board chairs (Mikkonen et al., 2021). For example, given the implications of critical mass and its ability to increase more female’s engagement then the role of existing chairs acting as mentors and taking initiative in this objective may be warranted. Furthermore, attention should be paid to the existing gender portfolio of each board and its subsequent influence on recruiting a female chair, regardless of the organization’s age. Knoppers et al. (2021) concluded that resistance to gender balance by board members is often related to discriminatory discourses against women. The normalization of the discourses of meritocracy, neoliberalism, silence/passivity about the responsibility of structures and an artificial defence of diversity emphasise that equality should not only be determined by women (Knoppers et al., 2021).

When countries are included in the model, the results suggest that the social representation of a female board member is different from that of a female board chair.

The originality of the study is that it shows the factors that constrain women taking up a chair position on NSFs. Theoretically, it contributes to existing literature by demonstrating how a critical mass of females on boards may also extend to the higher and most powerful position of chair.

The purpose of this paper is to address the challenging issue of developing a quantitative approach for the representation of cultural significance data in heritage information systems (HIS). The authors propose to provide experts in the field with a dedicated framework to structure and integrate targeted data about historical objects' significance in such environments.

This research seeks the identification of key indicators which allow to better inform decision-makers about cultural significance. Identified concepts are formalized in a data structure through conceptual data modeling, taking advantage on unified modeling language (HIS). The design science research (DSR) method is implemented to facilitate the development of the data model.

This paper proposes a practical solution for the formalization of data related to the significance of objects in HIS. The authors end up with a data model which enables multiple knowledge representations through data analysis and information retrieval.

The framework proposed in this article supports a more sustainable vision of heritage preservation as the framework enhances the involvement of all stakeholders in the conservation and management of historical sites. The data model supports explicit communications of the significance of historical objects and strengthens the synergy between the stakeholders involved in different phases of the conservation process.

This chapter presents a methodological discussion about ethnographic practice from a feminist perspective that contributes to the field of communication studies methodology and theory. The ethnography engages Black (Afro-Brasileiro and African-American) and economically disadvantaged youth from Rio de Janeiro (Brazil) and New Orleans (USA) regarding their strategies of social and media visibility. This multi-sited ethnography proposes to improve the objectives of ethnography through theoretical flexibility, liberation from a priori assumptions, greater representation of the voices of community members, disavowal of the imperatives of positivist work, and abiding respect for the “other.”

This study aims to examine how teacher candidates (TCs) in Türkiye and the USA navigate their intercultural communication skills in a telecollaboration project.

Forty-eight TCs participated (26 in Türkiye and 22 in the USA) in the study. TCs discussed critical issues in multicultural education on an online learning platform for six weeks. Their discussions were analyzed using content and discourse analysis.

The findings indicated that TCs approached the telecollaborative space as a translingual contact zone and positioned themselves and their interlocutors in the discourse by using the personal pronouns; I, we, you and they. When they positioned themselves using we (people in Türkiye/USA), they spoke on behalf of everyone included in the scope of we. Their interlocutors responded to these positionings either by accepting this positioning and responding with a parallel positioning or by engaging in translingual negotiation strategies to revise the scope of we and sharing some differences/nuances in beliefs and practices in their community.

When TCs talk about their culture and community in a singular manner using we, they frame them as the same across every member in that community. When they ask questions to each other using you, the framing of the questions prime the respondents to sometimes relay their own specific experiences as the norm or consider experiences from different points of view through translingual negotiation strategies. A singular approach to culture(s) may affect the marginalized communities the most because they are lost in this representation, and their experiences and voices are not integrated in the narratives or integrated with stereotypical representation.

Teachers and teacher educators should first pay attention to their language choices, especially use of pronouns, which may communicate inclusion or exclusion in intercultural conversations. Next, they should prepare their students to adopt and practice language choices that communicate respect for cultural diversity and are inclusive of marginalized populations.

Speakers’ pronoun use includes identity construction in discourse by drawing borders around and between communities and cultures with generalization and particularity, and by patrolling those borders to decide who is included and excluded. As a response, interlocutors use pronouns either to acknowledge those borders and respond with corresponding ones from their own context or negotiate alternative representations or further investigate for particularity or complexity. In short, pronouns could lead the direction of intercultural conversations toward criticality and complexity or otherwise, and might be reasons where there are breakdowns in communication or to fix those breakdowns.

This study shows that translingual negotiation strategies have explanatory power to examine how speakers from different language backgrounds negotiate second and third order positionings in the telecollaborative space.

The purpose of this study is to propose a new mapping tool called Circular Value Stream Mapping (C-VSM) that combines Circular Economy principles with Lean tools to enhance sustainability performance in operations.

To develop the C-VSM tool, the researchers conducted a literature review and a focus group. The tool was then applied to two real case studies in the agri-food sector, specifically analyzing an artichoke and olive oil producer, to assess its validity and effectiveness.

The study introduces the Circular Resource Box (CRB) as a key innovation in the C-VSM tool. This visual representation effectively captures resource circularity and how resources and wastes are managed, making it easy to identify circularity in the production process. By combining qualitative and quantitative information with this visual representation, companies can identify improvement opportunities aligned with the CE.

The research is limited in scope as it focuses on the application of the C-VSM tool in the agri-food sector. Further research could explore its applicability in other industries and settings to understand its broader impact.

The C-VSM tool provides practical benefits to companies seeking to transition from linear to circular production processes. It enables practitioners to identify opportunities to reduce environmental impacts and optimize production operations in line with CE.

The introduction of the C-VSM tool is a novel approach that bridges the gap between Lean Manufacturing and CE concepts, advancing the understanding of how CE thinking can be effectively implemented in operations.