Search Results (79)

The entry is intended to define the concepts of risk communication and emergency communication. At the same time, it explains the difference not only from a communication point of view but also from a cultural one. Risk and emergency are two sociologically relevant events, and they are culturally constructed. They are events that bring about a socio-cultural change, which, in turn, is triggered by the population’s responses on the basis of the social perception of the events themselves, also conveyed by the different forms of communication. When communicating risk and emergencies, it is essential to educate people about alert and emergency systems. Above all, what they refer to and what kind of message they contain. The “warning communication” must be specific and refer exclusively to the threat to start the first phase of the communication through which it is possible to understand the type of threat and define the communication plan to be implemented later. The use of social media, which is strongly spread in digital society, allows not only rapid dissemination of information but also rapid communication and message selection (speed and content of the message are equally important). Alert and warning systems are very often linked to risk systems, since the risk from natural disasters (eruptions, earthquakes, tsunamis) or technological catastrophes (nuclear power plant explosions) follows emergency phases when the phenomenon occurs. The communication processes, in and emergency, must be able to explain, persuade but also confer an assist the political decision-maker and the decision-making process itself through an alert system (especially in the first phase), followed by continuous dissemination through the media that the digital society offers, as well as through the usual systems adopted by government bodies (for example, bulletins and news), specialized research institutions and institutes with information and communication functions. In risk and emergency management, information and communication are to be considered, respectively, a basic element and a means of dissemination and training to educate the population to perceive a risk, to recognise emergencies and the possible impact of the risk. Differences will be expressed and analysed with reference to international examples. Full article

Effective spam detection systems are essential in online social media networks (OSNs) and cybersecurity, and they directly influence the quality of decision-making pertaining to security. With today’s digital communications, unsolicited spam degrades user experiences and threatens platform security. Machine learning-based spam detection systems offer an automated defense. Despite their effectiveness, such methods are frequently hindered by the “black box” problem, an interpretability deficiency that constrains their deployment in security applications, which, in order to comprehend the rationale of classification processes, is crucial for efficient threat evaluation and response strategies. However, their effectiveness hinges on selecting an optimal feature subset. To address these issues, we propose a lightweight, explainable spam detection model that integrates a nature-inspired optimizer. The approach employs clean data with data preprocessing and feature selection using a swarm-based, nature-inspired meta-heuristic Rüppell’s Fox Optimization (RFO) algorithm. To the best of our knowledge, this is the first time the algorithm has been adapted to the field of cybersecurity. The resulting minimal feature set is used to train a supervised classifier that achieves high detection rates and accuracy with respect to spam accounts. For the interpretation of model predictions, Shapley values are computed and illustrated through swarm and summary charts. The proposed system was empirically assessed using two datasets, achieving accuracies of 99.10%, 98.77%, 96.57%, and 92.24% on Dataset 1 using RFO with DT, KNN, AdaBoost, and LR and 98.94%, 98.67%, 95.04%, and 94.52% on Dataset 2, respectively. The results validate the efficacy of the suggested approach, providing an accurate and understandable model for spam account identification. This study represents notable progress in the field, offering a thorough and dependable resolution for spam account detection issues. Full article

Recent research in digital migration studies analyzes both refugee populations’ social media use and so-called integration outcomes. Against the backdrop of digitalization, we argue for understanding belonging as part of an ongoing process in physical, virtual and hybrid continuities, where material aspects cannot be divorced from imagined constructions. We take the case of Ukrainian women arriving in the Netherlands following the Russian aggression against Ukraine, in 15 semi-structured interviews from 2023. Adopting an intersectional lens, our research asks the following questions: How does social media use mediate or inform a sense of belonging? How do observations correspond to or differ from previous studies on refugees and social media, given this population’s temporary protection (TP) status? As participants leverage digital platforms differently in evolving situations, they navigate connectedness to the new community; we argue this may be linked to distinct TP status and other characteristics of difference, including educational levels and digital literacy. Conclusions indicate that an intersectional lens could be applied in understanding Ukrainian perspectives in other Member States—or beneficiaries of differing origin—to better account for how intersectional dimensions like legal status can be mutually constitutive of virtual connectedness versus material, physical rootedness or vulnerability. Full article

Scientific disinformation has emerged as a critical challenge at the interface of science and society. This paper examines how false or misleading scientific content proliferates across both social media and traditional media and evaluates strategies to counteract its spread. We conducted a comprehensive literature review of research on scientific misinformation across disciplines and regions, with particular focus on climate change and public health as exemplars. Our findings indicate that social media algorithms and user dynamics can amplify false scientific claims, as seen in case studies of viral misinformation campaigns on vaccines and climate change. Traditional media, meanwhile, are not immune to spreading inaccuracies—journalistic practices such as sensationalism or “false balance” in reporting have at times distorted scientific facts, impacting public understanding. We review efforts to fight disinformation, including technological tools for detection, the application of inoculation theory and prebunking techniques, and collaborative approaches that bridge scientists and journalists. To empower individuals, we propose practical guidelines for critically evaluating scientific information sources and emphasize the importance of digital and scientific literacy. Finally, we discuss methods to quantify the prevalence and impact of scientific disinformation—ranging from social network analysis to surveys of public belief—and compare trends across regions and scientific domains. Our results underscore that combating scientific disinformation requires an interdisciplinary, multi-pronged approach, combining improvements in science communication, education, and policy. We conducted a scoping review of 85 open-access studies focused on climate-related misinformation and disinformation, selected through a systematic screening process based on PRISMA criteria. This approach was chosen to address the lack of comprehensive mappings that synthesize key themes and identify research gaps in this fast-growing field. The analysis classified the literature into 17 thematic clusters, highlighting key trends, gaps, and emerging challenges in the field. Our results reveal a strong dominance of studies centered on social media amplification, political denialism, and cognitive inoculation strategies, while underlining a lack of research on fact-checking mechanisms and non-Western contexts. We conclude with recommendations for strengthening the resilience of both the public and information ecosystems against the spread of false scientific claims. Full article

The Russian invasion of Ukraine has also ignited a battleground in the domain of information. The conflict has been accompanied by a relentless disinformation offensive designed to manipulate public opinion and undermine democratic processes. This paper deals with the role of academia and scholars in focusing this information warfare. This study conducts a comprehensive analysis of scientific articles to examine how researchers and institutions have addressed fact-checking initiatives. To this end, performance analysis and literature review are combined to observe the state of academic investigations on fact-checking during the first thousand days of war in Ukraine (from 24 February 2022 to 19 November 2024). To do this, we identified 595 fact-checking articles in the Web of Science database within the “Social Sciences” category and narrowed the focus to 270 articles in the field of “Communication”. Finally, through an in-depth literature review of eight manuscripts, we seek to understand the specific strategies employed by academics to address the conflict between Russia and Ukraine through fact-checking. Our findings suggest that fact-checking research on the Russia–Ukraine war predominantly examines the impact of disinformation in conflict contexts, the role of media literacy in countering false narratives, and the contribution of citizen journalism to verification efforts. These conclusions can shed light on the crucial role of academia in safeguarding truth and fostering informed public debate in an era of information overload and manipulation. Full article

This study aims to provide a comprehensive knowledge mapping and extensive analysis of NeuroIS research, elucidating global trends and directions within this field from January 2007 to January 2024. A visual analysis of 256 research articles sourced from the Scopus database is conducted. The knowledge mapping, utilizing CiteSpace (CiteSpace 3.6 R1) and VOSviewer (VOSviewer 1.6.19), illustrates the current research landscape, encompassing collaboration networks, co-citation networks, references exhibiting citation bursts, and keyword analysis. The findings highlight the United States and Germany as leading nations in the exploration of NeuroIS, with the Karlsruher Institut für Technologie in Germany identified as a prominent institution in this domain. René Riedl, Pierre-Majorique Léger, Marc T. P. Adam, and Christof Weinhardt emerge as the most prolific authors in the field. Noteworthy themes that have garnered attention in recent years include customer experience, information systems, and information processing. Document analysis reveals that the study by Dimoka et al. in 2012 is the most cited work, providing a comprehensive overview of global NeuroIS research. Analysis of the document co-citation network identifies electroencephalography (EEG) in the context of technostress, the social impact of information in security alerts, and user experience in human–computer interaction as key areas of focus. René Riedl is recognized as the most cited researcher, while MIS Quarterly is distinguished as the leading journal in this field. Twelve NeuroIS papers exhibit high citation counts, with significant activity noted in 2021 and 2022. The timeline delineates the evolution of topics such as neuroscience, fMRI, cognitive neuroscience, social media, trust, eye tracking, and human–computer interaction. This study pioneers the examination of the current research status of NeuroIS through bibliometric analysis and the latest available data. It advocates for enhanced collaborations among scholars and institutions to improve information systems management and foster the development of NeuroIS. The study underscores the importance of ongoing research and cooperation in NeuroIS to deepen our understanding of how neuroscience can inform information systems design and management, thereby enhancing human–technology interaction. By identifying key trends, influential authors, and prominent themes, this analysis lays the groundwork for further exploration and innovation in this interdisciplinary domain. As technology continues to advance and our reliance on information systems intensifies, the insights derived from NeuroIS research can provide valuable perspectives on enhancing user experiences, optimizing information processing, and applying neuroscientific principles to develop more effective IT artifacts. Through sustained collaboration and knowledge sharing, the NeuroIS community can drive progress and shape the future of information systems management in an increasingly dynamic digital landscape. Full article

Digital transformation (DT) has become an important engine for the development of the digital economy and an important means of reshaping corporate culture, business processes, management models, and so on. Different social communities at different levels have different needs and understandings of digital transformation. Therefore, this paper proposes to explore the communication themes of digital transformation on social media. This study’s main objective is to uncover underlying thematic structures and core ideas from large amounts of textual data in different social media communities to better understand the significance of the communication themes. This paper also aims to reveal the characteristics of diffusion patterns of DT themes by opinion-themed mining. This study uses text mining and social network analysis methods to mine DT themes, theme structure, and the statistical characteristics of hot words across various online communities. The main findings of this study are as follows. The Huawei forum discusses the technological drivers of the digital economy from a micro level. Sohu News explores business operation strategies at a macro level. The Zhihu forum discusses the elements of digital development at the micro level. Moreover, the hot words’ degree centrality and betweenness centrality across various online communities exhibited a power law distribution. In conclusion, this research paper studies and analyzes DT themes of different social media platforms to discover the opinions and attitudes of various social groups in the digital transformation era and deeply interprets social trends and public opinions in order to provide valuable decision-making theoretical support for managers, enterprises, and governments. Full article

Background: This systematic review will address the influence of Information and Communication Technologies (ICTs) on democratic processes and citizens’ participation, which is enabled by such tools as social media, e-voting systems, e-government initiatives, and e-participation platforms. Methods: Based on an in-depth analysis of 46 peer-reviewed articles published between 1999 and 2024, this review emphasizes how ICTs have improved democratic engagement quality, efficiency, and transparency, but highlights key challenges and research gaps. Results: From this angle, ICT tools have great potential to nurture civic engagement and good governance through transparency. Challenges persist with the ethical and social implications of surveillance technologies, security concerns about digital voting systems, and the widening digital divide disproportionately affecting marginalized populations. The current regulatory framework dealing with privacy and misinformation issues is relatively weak, and there is also a lack of understanding about ICTs’ long-term effects on democratic governance. Conclusions: This review underlines the duality of the roles played by ICT as both an enabler and a challenge to democratic processes. It calls for regulatory measures to protect privacy, fight disinformation, and reduce the digital divide. Future research in this area should focus on the long-term effects of ICTs and how they can be equitably and efficiently integrated into democratic systems, with strategies aimed at maximizing benefits while minimizing risks. Full article

This article for the first time examines the activities of transnational information campaigns initiated by young Shiʿa Muslims across European, Russian, and Armenian cities. These campaigns aim to disseminate knowledge about Imam Hussain and his mission to non-Shiʿa audiences. The campaigns serve as vivid examples of the struggles faced by Muslims in relatively hostile contexts as they seek integration and acceptance as law-abiding and peaceful citizens. They also represent new avenues of Islamic activism, focusing on dismantling stereotypes and correcting “misunderstandings” within host societies. Drawing on semi-structured interviews, participant observation, and an analysis of campaign handouts and social media content between 2018 and 2024, this study explores how campaigners adapt the narrative of the Karbala tragedy—widely regarded as the metahistorical cornerstone of Shiʿa identity—to make it accessible and relevant to external audiences. These strategies enable campaigners to engage with out-group communities, testing and refining effective methods for presenting this pivotal historical event. The findings reveal that, through this process, the Karbala narrative undergoes both content-based and linguistic modifications, while the concept of justice—central to the commemoration of this tragedy—is reinterpreted in new contexts. This research contributes to the understanding of transnational Islamic activism and highlights the importance of strategic communication in fostering intercultural dialogue and promoting mutual understanding in diverse societies. Full article

Rainstorm disasters have wide-ranging impacts on communities, but traditional information collection methods are often hampered by high labor costs and limited coverage. Social media platforms such as Weibo provide new opportunities for monitoring and analyzing disaster-related information in real-time. In this paper, we present ETEN_BERT_QA, a novel model for extracting event arguments from Weibo rainstorm disaster texts. The model incorporates the event text enhancement network (ETEN) to enhance the extraction process by improving the semantic representation of event information in combination with event trigger words. To support our approach, we constructed RainEE, a dataset dedicated to rainstorm disaster event extraction, and implemented a two-step process, as follows: (1) event detection, which identifies trigger words and classifies them into event types, and (2) event argument extraction, which identifies event arguments and classifies them into argument roles. Our ETEN_BERT_QA model combines ETEN with a BERT-based question-answering mechanism to further improve the understanding of the event text. Experimental evaluations on the RainEE and DuEE datasets show that ETEN_BERT_QA significantly outperforms the baseline model in terms of accuracy and the number of event argument extractions, validating its effectiveness in analyzing rainstorm disaster-related Weibo texts. Full article

Background/Objectives: The way people communicate has significantly changed due to technological advances and penetration of cell phones and broadband connection into everyday activities. Nowadays, individuals are constantly connected through the Internet. This influences social experiences, self and social identity, and can cause digital stress, which often leads to negative emotions, psychosomatic outcomes, and diseases. It is a matter of concern for the adolescents and their families. This paper investigates how Information and Communication Technologies (ICTs) and social media influence adolescents’ and parents’ digital stress and anxiety. Methods: Based on two digital stressors scales, a 30-item questionnaire was created. A quantitative analysis of data deriving from 164 Finnish adolescents and 53 of their parents regarding digital stress was conducted. Results: “Privacy Intrusion”, “Availability stress/Online vigilance”, and “Usefulness” were the most essential stressor factors for both students and parents. Additionally, the most significant stressor factors for students were the “Fear of Missing Out/FoMO” and “Approval Anxiety” factors while for parents were the “Safety/Security” and “Technical Support” factors. No significant differences were observed between the overall digital stress of adolescents and parents. In contrast to the existing literature, no connection between “Connection (Information) Overload” and digital stress was found. Conclusions: This paper contributes to debates regarding the effects of the use of digital resources, ICTs, and social media on processes at school, at work, and at home. The findings of our study confirm and further validate those of previous studies with the exception of “Connection (Information) Overload”, which needs to be further investigated to understand the reasons behind it. Full article

Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represented in many African languages. While state-of-the-art Afrocentric pre-trained language models (PLMs) have been developed for various natural language processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain largely unexplored. In this study, we propose a novel approach that combines Afrocentric PLMs with XAI techniques for sentiment analysis. We demonstrate the effectiveness of incorporating attention mechanisms and visualization techniques in improving the transparency, trustworthiness, and decision-making capabilities of transformer-based models when making sentiment predictions. To validate our approach, we employ the SAfriSenti corpus, a multilingual sentiment dataset for South African under-resourced languages, and perform a series of sentiment analysis experiments. These experiments enable comprehensive evaluations, comparing the performance of Afrocentric models against mainstream PLMs. Our results show that the Afro-XLMR model outperforms all other models, achieving an average F1-score of 71.04% across five tested languages, and the lowest error rate among the evaluated models. Additionally, we enhance the interpretability and explainability of the Afro-XLMR model using Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These XAI techniques ensure that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in AI-driven NLP technologies, particularly in the context of African languages. Full article

This study aimed to investigate factors associated with birth-related PTSD symptoms and the subsequent impact on mother–infant relationship quality, exploring what women felt they needed to be different about their birth process to further understand the problem. Participants were recruited using social media advertising. A multi-method approach, using qualitative and quantitative analyses, was employed. The study included 142 pregnant women residing in Australia aged between 19 and 42 years (M = 31.24; SD = 4.70). High extraversion significantly predicted greater birth-related PTSD symptoms. There was a significant relationship between birth-related PTSD symptoms and poorer mother–infant relationship quality. The content analysis of the responses from women who reported a desire for a different or improved birth experience generated four themes: (1) less intrusive intervention, (2) better communication, (3) access to more supportive intervention, and (4) better post-birth care. The findings highlight the importance of supporting women’s choices during birth by promoting informed and respectful woman-centred care. Postnatally, the screening and assessment of women should go beyond mood screening and include an assessment of the woman’s response to her birth experience. Full article

Different actors have observed divergences and imbalances related to quality understanding and value appropriation in the coffee value chain. Reducing this gap and ensuring sustainability requires innovative strategies. The “Coffee Grower’s Laboratory” in Ituango, Colombia, was established to enhance local coffee farmers’ capacities in science, technology, and innovation. Over 13 participatory methodologies were developed with design thinking to facilitate knowledge exchange between the farmers’ traditional expressions and international commercial practices. Key methodologies included brand co-creation (Artesanato), roast curves (Anatomy of Roasting), roaster operation (The Roaster’s Journey), understanding beans (Know the Bean), and coffee tasting (Deliciousometer). Additionally, methodologies for strengthening intra and interpersonal skills, such as self-care (Crossing the River) and empowerment (The Four Pillars of Trust), were included. Altogether 302 coffee farmers participated. We co-created a community brand, Itucafé, promoted on social media to highlight the farmers’ traditions and innovations nationally and internationally. Farmers, who previously sold only washed parchment coffee, now process, taste, and sell their coffee directly to buyers. Selling directly to buyers increased coffee growers’ profits to around 50% through the “Coffee Grower’s Laboratory”, compared to the less than 10% they earned previously. They also experiment with new fermentations using local fruits, creating unique sensory profiles for more competitive pricing. The “Coffee Grower’s Laboratory” has strengthened the coffee community’s capabilities and competitiveness, enhancing the visibility and recognition of Ituango coffee. It provides an ideal environment for continuous training and producer-buyer relationships, serving as a replicable model for other coffee-producing regions in Colombia. Full article

Traditional service quality models, which are survey-based methods, have been noted by researchers to contain operational errors in their application. Researchers criticize service quality models such as SERVQUAL and SERVPERF for containing operational errors, high implementation costs, and the issue of response recall. Additionally, these models face difficulties when applied to different sectors, as they were developed for the retail industry. The adaptation of the model, data collection, and processing have become outdated in comparison to current information processing technologies. With the rise in the use of social media, new communication paradigms have emerged. In this new paradigm, direct communication is established between people and institutions through social media. Institutions analyze social media data using text mining and sentiment analysis methods to keep up with this change. There are studies in the literature proposing new methods for measuring service quality by separately using text mining and sentiment analysis techniques. In this study, these two techniques have been combined. It is believed that combining these two techniques will result in a more robust service quality measurement model. Additionally, an application has been developed to demonstrate the functionality of the model. A municipality was specifically chosen as the application area because social media allows for fast, efficient, and inclusive participation between citizens and the municipality. The proposed model will enable the better identification of service quality deficiencies, leading to a more efficient use of municipal resources and fostering a more sustainable understanding of the municipality. With the implementation of the model, 463,886 tweets sent to the @ankarabbld and @mavimasa accounts were analyzed to identify 10 service quality dimensions and 106 keywords representing these dimensions, which would reveal the municipality’s service quality. The sentiment analysis technique was applied to 187,084 tweets containing the identified keywords. Thus, an attempt was made to uncover the municipality’s service quality. Full article