Communication Model Research Articles

Who We Are and How We Talk About Volunteering: Older Adult Volunteers' Perspective.

Guided by the Communicative Ecology Model of Successful Aging (CEMSA), we conducted an interview study with 25 older adult volunteers (M = 71.8 years old, SD = 5.19) to explore how they discursively negotiated their identities and communicated about volunteering during the aging process. The conventional view of old age and aging tends to be negative, however, our findings from the older adult volunteers' accounts revealed positive themes regarding their self-categorization and volunteering experiences. These themes include: (a) positive representations of self and other older adult volunteers, (b) transcendence of negative perceptions of old age and aging through volunteering involvement, and (c) communication about engaging in selection, optimization, and compensation in managing volunteer work. Our findings carry significant theoretical and practical implications, underscoring how positive self-categorization and adaptive management of volunteering remarkably contribute to building an ecology of successful aging.

THE ISLAMIC COMMUNICATION STRATEGIES OF THE NATIONAL AMIL ZAKAT AGENCY (BAZNAS) MAKASSAR IN PUBLIC SERVICE

<p><strong><em>This study aims to identify the Islamic communication strategies implemented by the National Amil Zakat Agency (BAZNAS) Makassar and to assess the community’s response to this communication model. The research utilizes Islamic communication theory, which is grounded in the Quran and Hadith, focusing on the morality of the communicator, the content, and the ethics of communication. The study adopts a mixed-method approach (qualitative and quantitative). Descriptive analysis was conducted throughout the research process to draw conclusions. The results reveal that the Islamic communication strategies applied by BAZNAS Makassar adheres to the core functions of Islamic communication, which include conveying information, socializing, reminding, guiding, motivating, and convincing the public about the importance and benefits of zakat, infaq, and alms (ZIA). Furthermore, the delivery of messages incorporates the principles of qaulan ma'rufan, qaulan kariman, qaulan maysuran, qaulan balighan, and qaulan sadidan, along with other Islamic communication ethics. The impact of this approach has led to high levels of public satisfaction with BAZNAS’s communication methods and models, which are perceived as effective in conveying Islamic values. This model has also increased public trust in the transparency and accountability of ZIA management in Makassar City, thereby enhancing community participation. </em></strong></p><p><strong><em>Keywords: BAZNAS Makassar, infaq and alms, islamic communication strategies, zakat management </em></strong></p>

Metapragmatic conventions and integrative context: Introducing Sībawayhian pragmatics

Abstract The aim of this paper is to shed light on theories of discourse meaning from the Arabic, namely Sībawayhian, linguistics tradition, that are novel in the contemporary pragmatics space and prompts us to rethink and reshape the Anglo-American, namely Gricean and post-Gricean, lens through which current theories are predominantly defined. The Arabic linguistics tradition was founded by Sībawayhi more than ten centuries ago, yet it is severely underrepresented and under-researched in modern (Western) linguistics. Sībawayhi posited a discourse-oriented, pragmatics-centric, and intuition-driven model of language and communication, providing unique insights into how intentions and conventions figure in meaning representation. Despite his monumental contribution, Sībawayhian theory has hitherto been missing from the present-day field of pragmatics. Instead, pragmatics as a line of study is usually attributed to Grice and subsequent post-Gricean developments, where theory construction has been limited to intra-theoretical analyses and falls short of cross-cultural epistemological perspectives. Many of these analyses are as such reinventions of what can be found in Sībawayhian and post-Sībawayhian pragmatics. And there are many more components of Arabic pragmatic thought that remain to be (re)discovered, with the potential to further current thinking in pragmatics and open a new orientation of contemporary pragmatic study. In this paper, I attempt to address all of the above by closely examining selected sections of Sībawayhi’s monumental al-Kitāb. I particularly introduce two main conceptions Sībawayhi relies on in his theory of meaning – metapragmatic conventions (conventionsmp) and integrative context (contexti) – and investigate their role in shaping major areas of pragmatic theorization, namely syntax, discourse compositionality, and inference.

Intersectionality in Health Communication: How Health Communication Influences the Association Between Intersectional Discrimination and Health Information Seeking

With an intersectional orientation, we examine associations between discrimination, health communication, and information-seeking intention about HIV prevention in the context of pre-exposure prophylaxis (PrEP) among cisgender women through an online survey (N = 341). We elaborate on the idea that intersectional discrimination is a social determinant of health by considering the context with differing power relations—day-to-day life and the healthcare field—based on Bourdieu’s field theory, and explore moderating roles of health communication in this relationship according to the structural influence model of communication (SIM). The relationships between intersectional discrimination and intention show different patterns by the context in which intersectional experiences are considered, and the relationships are moderated differently by the mode of health communication. We discuss how to conduct intersectionality-informed health communication research without sacrificing intersectionality’s foundational foci.

“Bridging cultures with love”: Spirituality in fostering intercultural effectiveness. The effects of language

Abstract Research yields contradictory results on the relationship between an individual’s spirituality, that is, the relationship with God/the Transcendent, and their cultural development. To address this gap, we conceptualize a model that investigates the effects of two spirituality dimensions, namely felt love for God and love for others, on the behavioral aspect of intercultural competence, that is, intercultural effectiveness (IE), through ethnocentrism. This model undergoes testing using a sample of 144 international students who self-identify as Christians, Muslims, belonging to other religions, and non-religious. The results confirm the positive impact of spirituality on IE by reducing ethnocentrism. We establish suppressing effects of both love for God and love for others, suggesting intricate dynamics of these two spirituality dimensions within the intercultural communication model. Furthermore, we identify the direct effects of host language and English as a lingua franca proficiency on IE. We also define the mitigating effect of host language proficiency on the adverse impact of ethnocentrism on IE. This study underscores the importance of higher education institutions to nurture students’ relational spirituality and support them in overcoming ethnocentric perspectives.

Theoretical Approaches to Communicative Practices in the Study of Intergenerational Communication and Aging.

Intergenerational communication and aging is a thriving interdisciplinary, methodologically diverse field having significant implications for understanding the aging process. This opening article to the special issue provides a brief overview of this research domain, with particular attention to theoretical practices within it. Communication accommodation theory (CAT) is introduced given it has provided a foundation for other well-cited models of communication and aging. In so doing, a couple of the CAT principles are elaborated, based on recent work on age meta-stereotyping and intergroup felt understanding. CAT is also a component of the influential "communication ecology model of successful aging" and, after exploring some of its tenets, its visually schematic representation is elaborated as well as connections speculated regarding its relationship with the communicative lives of SuperAgers. Thereafter, highlights emerging from the articles in this special issue that follow are drawn out.

Evaluating the visual literacy of the Kuwaiti government’s Arabic infographics during the COVID-19 pandemic: an automated visual analysis and AI approach

PurposeWith infographics emerging as key communication tools on social media platforms, this study explores the visual literacy of governments in creating effective infographics, especially during crises. Using the Kuwaiti government during the COVID-19 pandemic as a case study, the investigation evaluates the government’s visual competency and strategy in its infographics on X.Design/methodology/approachFor competence, AI-based techniques were employed to analyze the proportion of text region size to total infographic size, word count per infographic and the most prominent colors used. Regarding strategy, the study utilized the crisis and emergency risk communication (CERC) model as a framework to examine how the Kuwaiti government integrated crisis communication response strategies into infographics.FindingsWhen communicating complex messages, the government resorted to text-heavy infographics instead of creative visualizations, casting doubt on its visual competence. The inconsistent use of colors further undermined a recognizable visual identity. Regarding strategy, infographics on crisis updates were most frequent, supporting CERC’s emphasis on reducing uncertainty. Yet, prioritizing bolstering strategies above empathy and action steps goes against the advice of existing literature.Originality/valueWhile crisis communication research is widely based on textual analysis, this study extends the literature by examining visuals, specifically infographics. Additionally, focusing on Arabic infographics from Kuwait, the study expands the crisis communication literature, which has mainly concentrated on Western countries and the English language. Given the lack of consensus on the best methods to measure visual literacy, this study’s AI approaches contribute to the literature.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0172

Older Adults' Understanding of COVID-19 and Successful Aging: An Application of CEMSA to Ghana and Uganda.

COVID-19 heightened the uncertainty of aging, mainly because older adults were considered most vulnerable to the virus's effects. This study utilized the Communicative Ecology Model of Successful Aging (CEMSA) as a framework to understand the intersection of aging, COVID-19, and communication. In-depth interviews were conducted to explore the nuanced accounts given by fourteen eligible participants from Ghana and Uganda to understand their communicative practices about successful aging during COVID-19. Findings revealed that expressing optimism about aging, planning for future care needs, self-categorization as old, or attributing behavior to old age, and using emerging communication technologies to foster engagement with friends and loved ones are crucial aspects of older adults' communication around successful aging during the pandemic. The study also extended CEMSA to the African context and introduced faith as a new element that influenced how older adults communicate about successful aging that the model had not previously accounted for.

Analysis of flows in social media uncovers a new multi-step model of information spread

Abstract Since the advent of the internet, communication paradigms have continuously evolved, resulting in a present-day landscape where the dynamics of information dissemination have undergone a complete transformation compared to the past. In this study, we challenge the conventional two-step flow communication model, a long-standing paradigm in the field. Our approach introduces a more intricate multi-step and multi-actor model that effectively captures the complexities of modern information spread. We test our hypothesis by examining the spread of information on the Twitter platform. Our findings support the multi-step and multi-actor model hypothesis. In this framework, influencers (individuals with a significant presence in social media) emerge as new central figures and partially take on the role previously attributed to opinion leaders. However, this does not apply to opinion leaders who adapt and reaffirm their influential position on social media, here defined as opinion-leading influencers. Additionally, we note a substantial number of adopters directly accessing information sources, suggesting a potential decline in influence in both opinion leaders and influencers. Finally, we found distinctions in the diffusion patterns of left-/right-leaning groups, indicating variations in the underlying structure of information dissemination across different ideologies.

Research on path planning in UAV-assisted emergency communication

The rapid development of UAV communication technology makes it have application potential in wireless systems. However, for the optimization problem of UAV base station providing communication for ground personnel in the disaster area under special natural disasters, traditional deep reinforcement learning is used. Algorithms cannot solve such problems well. This article proposes the AD3QN algorithm combined with the attention mechanism, which can communicate with disaster victims on the ground better and more quickly, providing better information collection for rescue missions and completing the task of communication optimization. In the mission simulation environment, in order to make the environment more realistic, we innovatively designed the ground user distribution model, air-to-ground communication model, and electromagnetic interference model. Finally, the effect of the proposed algorithm is evaluated by comparing different deep reinforcement learning algorithms. The results show that the algorithm we proposed can provide communication services faster and has higher practicability in terms of algorithm.

BioRxToolbox: A computational framework to streamline genetic circuit design in molecular data communications

ABSTRACT Developments in bioengineering and nanotechnology have ignited the research on biological and molecular communication systems. Despite potential benefits, engineering communication systems to carry data signals using biological messenger molecules and engineered cells is challenging. Diffusing molecules may fall behind their schedule to arrive at the receiver, interfering with symbols of subsequent time slots and distorting the signal. Existing theoretical molecular communication models often focus solely on the characteristics of a communication channel and fail to provide an end-to-end system response since they assume a simple thresholding process for a receiver cell and overlook how the receiver can detect the incoming distorted molecular signal. In this paper, we present a model-based and computational framework called BioRxToolbox for designing diffusion-based and end-to-end molecular communication systems coupled with synthetic genetic circuits. We describe a novel framework to encode information as a sequence of bits, each transmitted from the sender as a burst of molecules, control cellular behavior at the receiver, and minimize cellular signal interference by employing equalization techniques from communication theory. This approach allows the encoding and decoding of data bits efficiently using two different types of molecules that act as the data carrier and the antagonist to cancel out the heavy tail of the former. Here, BioRxToolbox is demonstrated using a biological design and computational simulations for various communication scenarios. This toolbox facilitates automating the choice of communication parameters and identifying the best communication scenarios that can produce efficient cellular signals.

Modeling of network public opinion communication based on social combustion theory under sudden social hot events.

Sudden social hot events spread rapidly in the Internet era, which can easily cause group behaviors. Therefore, studying the law of public opinion communication has important theoretical and practical significance for controlling public opinion. This paper constructs the DBUS (Divorced-Burning-Unburning-Stable) model according to the social combustion theory deduced from the analogy of natural combustion phenomena. This paper comprehensively considers the influence of individual characteristics and external environment on the dissemination of online public opinion, introduces indicators such as individuals' self-awareness ability, authority to reflect individual differences, and takes the surrounding individual influence as an external environment factor. Through simulations, we find that (1) the speed of public opinion communication and the dissipation speed are related to cognitive ability, conformity and the network structure; (2) the credibility of the government and the media and the transparency of the information released are the most important factors affecting the scale of public opinion communication; In addition, the rationality and effectiveness of the model established in this paper are verified by an example.

Sentiment analysis in infodemic management: leveraging the eppm Risk communication model

Infodemics are the rapid spread of false or misleading information related to public health emergencies, often through digital platforms. They can cause confusion, fear, and even harm to public health. This study investigates the application of sentiment analysis for infodemic management during disease outbreaks. Leveraging the Extended Parallel Process Model (EPPM) of risk communication, the research aims to categorize rumors based on their perceived threat level (high, medium, or low). Machine learning is employed to analyze infodemic text data collected from two Nigerian states (Oyo and Bauchi) to assess threat appraisal according to the EPPM model. The findings can inform targeted interventions for effective infodemic management before, during, and after outbreak of diseases.

Communication in Mediation

Abstract The purpose of this study is to establish a communication model between the parties to the conflict in mediation based on the research of communication, information, mediation and conflict approaches and definitions. The main methods of communication for finding a solution to a conflict are dialogue between the parties to the conflict and non-violent communication. The model has been designed and practically used to achieve the results of mediation –resolution of a conflict, as well as education of the parties about conflict-free communication for their future relationships. The study/article offers a method for measuring the effectiveness of mediation using the proposed communication model.

Collaboration to Reduce Sudden Unexpected Infant Death With Child Fatality Review and Outreach.

Sudden unexpected infant death (SUID) is the leading cause of postneonatal infant mortality in the United States, with disproportionately high rates in Baltimore City and Baltimore County in Maryland. This Advocacy Case Study describes the collaboration between the City and County Child Fatality Review teams to decrease infant mortality. B'more for Healthy Babies, formed in 2009 by the Bureau of Maternal Child Health in Baltimore City with the goal of reducing infant mortality through policy change, service improvements, community mobilization, and behavior change has had a sustained effort to respond to SUID. Recognizing that infants born in Baltimore City often reside in Baltimore County (and vice versa), collaboration with Baltimore County has been essential to increasing B'more for Healthy Babies' scope, reach, and effectiveness. Public health messaging campaigns, creation, and dissemination of a "SLEEP SAFE" testimonial video, and Safe Sleep Summits have involved caregivers, healthcare providers, and community partners. Outcomes of this ongoing collaboration demonstrate a decrease in sleep-related infant mortality rates. Our findings also highlight the need for continued, real time monitoring of sleep-related infant mortality trends with a coordinated, multidisciplinary, and crossjurisdictional response. This initiative can serve as a model of cross-sector communication that can be replicated in other similar geographic locations to further reduce SUID.

A 3D Continuous-Space Electromagnetic Channel Model for 6G Tri-Polarized Multi-User Communications

A 3D Continuous-Space Electromagnetic Channel Model for 6G Tri-Polarized Multi-User Communications

Optimizing Power System Wide-area Damping Controllers under Communication Time Delays and Model Reduction Errors

Optimizing Power System Wide-area Damping Controllers under Communication Time Delays and Model Reduction Errors

Designing persuasive communication models for vaccine acceptance in isolated communities: A mass communication approach

This study introduces the Persuasive Communication Model as a novel approach to address vaccine hesitancy in isolated U.S. communities, where infectious diseases such as measles and influenza continue to pose significant public health threats. By integrating mass communication theories like the Elaboration Likelihood Model (ELM) and Social Cognitive Theory (SCT), with AI-driven message personalization, the model effectively tailors health communication strategies to the cultural, cognitive, and socio-economic contexts of vaccine-hesitant populations. In a randomized controlled trial, the model increased vaccination uptake by over 20% compared to traditional methods, while also enhancing trust in healthcare systems. The model’s adaptive communication, utilizing both digital platforms and community leaders, was instrumental in overcoming barriers related to geographic isolation, religious beliefs, and cultural resistance. These findings are particularly relevant for enhancing national public health security and reducing the economic burden of vaccine-preventable diseases. The study highlights the model's superiority over static communication frameworks, offering scalable applications for broader public health interventions.

Design and development of an ai-driven communication model for stimulating memory recall in patients with Alzheimer’s and other neurodegenerative diseases

This study presents the development and evaluation of an AI-driven communication tool designed to stimulate memory recall in patients with Alzheimer’s disease and other neurodegenerative disorders. The tool integrates machine learning algorithms, including Random Forest and Long Short-Term Memory (LSTM) networks, with mass communication theories to deliver personalized and adaptive communication strategies. The AI models were trained and tested on patient interaction data, enabling the tool to dynamically adjust its prompts based on real-time cognitive states. Performance metrics such as accuracy, precision, and AUC-ROC were used to assess model efficacy, with the Random Forest model achieving the highest performance across all metrics. Clinical trials demonstrated significant improvements in memory recall and emotional engagement in patients using the AI tool compared to standard cognitive stimulation therapy. Caregivers also reported higher satisfaction with the tool's usability and impact on patient interaction quality. These findings highlight the potential of AI-driven, personalized communication tools to revolutionize Alzheimer's care, offering scalable, adaptive interventions that improve cognitive and emotional health. Academically, this work advances the integration of AI and health communication, emphasizing culturally sensitive and patient-centered approaches. In healthcare, it presents a promising non-pharmacological intervention that can be easily scaled for widespread use, potentially reducing caregiver burden and improving patient outcomes.

Resource allocation optimization of device-to-device communication using machine learning algorithms

Device-to-device communication is envisioning next-generation wireless communication. The utility of the device-to-device communication model encourages emerging communication systems such as 5G and the Internet of Things. The allocation of resources and channel interference are major challenges in device-to-device communication. This paper proposes machine-learning-based algorithms for resource allocation and optimization of device-to-device communication. The proposed machine learning algorithm is a cascaded support vector machine. The cascaded support vector machine mapped the parameters of CUEs and DUEs. We create an iterative algorithm to achieve low-power, energy-efficient resource allocation with mode selection by formulating a novel optimization problem to maximize energy efficiency using the subtractive form method to solve a fractional objective function. We obtain data samples from a suboptimal algorithm to train the cascaded algorithm and verify the trained algorithm. Our numerical results show that the proposed cascaded machine learning-based transmission algorithm's accuracy reaches about 88%–95% despite its simple structure due to the limitation in computing power. The analysis of results suggests that the proposed algorithm is more efficient than SVM, DSVM, and the reinforcement learning (RL) algorithm.