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In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [ABSTRACT FROM AUTHOR]

Sociolinguistic research has demonstrated that 'urban contact dialects' tend to diffuse beyond the speech communities in which they first emerge. However, no research has attempted to explore the distribution of these varieties across an entire nation nor isolate the social mechanisms that propel their spread. In this paper, we use a corpus of 1.8 billion geo‐tagged tweets to explore the spread of Multicultural London English (MLE) lexis across the United Kingdom. We find evidence for the diffusion of MLE lexis from East and North London into other ethnically and culturally diverse urban centres across England, particularly those in the South (e.g. Luton), but find lower frequencies of MLE lexis in the North of England (e.g. Manchester), and in Scotland and Wales. Concluding, we emphasise the role of demographic similarity in the diffusion of linguistic innovations by demonstrating that this variety originated in London and diffused into other urban areas in England through the social networks of Black and Asian users. [ABSTRACT FROM AUTHOR]

In real network systems, there are numerous information propagation phenomena. However, the impact of homogeneity on information propagation is still not well understood. In this paper, we introduce a dynamic information propagation model named H-SIR. We also incorporate the propagation mechanism of content homogeneity and define the dynamic propagation rate based on users' interest locking and fatigue level. Through simulation experiments, we demonstrate how homogeneous information spreads and evolves on the network. Furthermore, we compare the model with real data of COVID-19 information on the Sina-Weibo social platform and the traditional SIR model to jointly validate our model. Our results show that in social networks, if the themes of homogeneous information are diverse, even with an increasing amount of homogeneous information, it will hardly affect the propagation of this type of information. However, if users are recommended excessive homogeneous information, they will not focus entirely but instead display selective contact behavior. In the case of information fatigue, a type of information with a low degree of homogeneity will survive longer than a type of information with a high degree of homogeneity. Finally, we propose corresponding strategies to alleviate the phenomenon of information homogenization in social networks. [ABSTRACT FROM AUTHOR]

Introduction: The COVID‐19 pandemic (2020–2021) resulted in isolation due to social distancing rules and lockdowns, during which technology was used to enable families and friends to maintain contact. Despite loneliness being high in parents, little is known about which parents are more likely to experience loneliness and strategies to support them. Method: This mixed‐methods study examines which parents were lonely during the pandemic and how digital technologies were used to reduce loneliness and social isolation. Data were collected during the first UK lockdown from May to July 2020 via an online survey (N = 145) and follow‐up interviews (n = 13). Results: Loneliness reported during lockdown was greater than retrospective reporting of loneliness for all respondents, but it was higher in single parents, those caring for children with specific needs, and those with lower household incomes before and during lockdown. Parents rapidly adapted to and experimented with using technology for social connection during the lockdown, which helped to reduce feelings of isolation and loneliness. Video calling was found to be useful for making connections and enhancing social presence, but parents also experienced anxieties and difficulties with its use. There were mixed views about technology use; some were keen to continue using technology for socializing after the lockdown ended, but for others, it was only a temporary measure. Conclusion: Although technology was invaluable during the pandemic, it was not a panacea, and the way parents used it was influenced by their approach to technology and existing social behaviors and networks. [ABSTRACT FROM AUTHOR]

While the networking nature of social media opens up opportunities for nonprofit e-advocacy, nonprofits' coalition building through social media remains an understudied area of research. Drawing on previous research, we collected longitudinal Twitter data of nonprofit homeless shelters in the United States. By conducting a series of network and textual analyses, this study seeks to understand how social media's connecting tools are used by shelter providers over time. We also observe the interorganizational networks that developed over time to examine whether online coalitions are built in the homelessness field. Results indicate that while nonprofit homeless shelters tend to underutilize the potential of social media for inter-organizational networking, the inter-organizational network tends to develop into a set of more complicated and stronger ties. [ABSTRACT FROM AUTHOR]

Social media is commonly used by governments to disseminate information related to transparency and institutional promotion, but its potential for the dissemination of cultural resources is often overlooked. This research proposes a strategy for adopting social media to achieve cultural dissemination in the municipality of Lamas, Peru, which has tourism potential and has been awarded the "Best Tourism Villages" seal of the World Tourism Organisation. Methodologically, we propose a case study at a propositional exploratory level. We start by determining the current state of dissemination of tangible and intangible resources, as well as the practical use of social media, through a survey of 20 officials. The results reveal that crafts are the most widely disseminated tangible resource in the municipality (according to 55% of officials), and intangible resources are occasionally disseminated (50%). We also find that Facebook is the only digital channel used to disseminate cultural information. In this context, we formulate a strategy for adopting social media to improve cultural dissemination, which consists of six phases: (i) forming a work team, (ii) preparing a budget plan, (iii) strengthening capacities, (iv) taking inventory of cultural resources, (v) coordinating community interests and (vi) preparing a cultural dissemination plan. The findings can contribute to formulating public initiatives for the institutionalisation of social media in governments for the purposes of preserving, promoting and guaranteeing the accessibility of cultural heritage. [ABSTRACT FROM AUTHOR]

As social media becomes more ingrained in political and social discourse around the world, the need for systematic measures and interpretations of online polarization grows. In this work, we introduce five major organizational forms of community-level polarization and nine subforms to support comparison of community dynamics in controversial discussions across time, platforms, languages, and domains. We propose a multi-dimensional social network analysis approach to characterize the structure within and between ideologically opposed communities and evaluate the organizational form. Through three case studies, we demonstrate applying the proposed methodology on two social media platforms (Twitter and Reddit) and levels of conflict (topic and partisanship). This work has implications for depolarization interventions and policies on social media. [ABSTRACT FROM AUTHOR]

Numerous studies have suggested that active social media use can promote well-being by enhancing perceived social support. However, the relationship between social media use and perceived social support remains inconsistent across studies. This study explores possible mechanisms underlying the relationship between active social media use, perceived social support, and well-being during and after a COVID-19 lockdown. Using online surveys with Chinese participants during (N = 1,131) and after (N = 407) the lockdown period, our findings support a sequential mediation model. Specifically, active social media use was positively associated with perceived online network responsiveness, which in turn, predicted augmented perceived social support. Ultimately, increased social support was linked to reduced loneliness and increased life satisfaction. These findings were consistent both during and after the lockdown, indicating that social media has the potential to complement offline social interactions and effectively fulfill individuals' social needs. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To enhance your understanding of the relationship between eating disorders and social media use in adolescents • To recognise the potential protective and adverse effects of social media in relation to eating disorders • To be aware of possible gender differences in social media use and the development of eating disorders. There is a wealth of research on the relationship between young people’s increasing use of social media and the development of mental health issues. In the context of eating disorders, evidence suggests that social media use can have a direct and indirect role in dietary restriction, body dissatisfaction and valuing of thinness in adolescents, which are linked to the development of eating disorders. This article reports on a scoping review that explored the relationship between social media use and eating disorders in adolescents. The findings suggest that social media use is not directly related to the development of eating disorders in adolescents, but rather the potential development of eating disorder cognitions which may increase the risk of eating disorders. The findings also suggest that social media use may have some protective effects, where positive online peer relationships may support adolescents to increase their body appreciation. [ABSTRACT FROM AUTHOR]

Background: The anal symptoms occurring during pregnancy and post-partum, mainly related to Haemorrhoidal Disease (HD), have been reported with in a wide range of incidence in the literature. Although in many cases the course of the disease is mild and self-limiting, sometimes it is severe enough to affect quality of life. Methods: Our study has been conducted through a questionnaire administered via social media with the aim of obtaining epidemiologic data on the incidence of the symptoms of HD in an unselected population of pregnant women. In addition, we looked for the presence of those factors notoriously predisposing or associated to HD (constipation, straining on the toilet, low dietary fibres and fluid intake). Results: Out of 133 patients 51% reported symptoms of HD during pregnancy, mainly in the second and third trimester. Constipation, straining on the toilet, low dietary fibres and fluid intake were not significantly related to incidence of HD. Only a previous history of HD was correlated to onset of symptoms of HD in pregnancy and reached a statistical significance (odds ratio = 5.2, p < 0.001). Conclusion: Although with the limitations posed by the nature of our retrospective study via a self-assessment interview, our results suggest that the occurrence of HD in pregnancy seems not sustained by the classical risk factors observed in the general population. At the moment, specific therapeutic measures are lacking and treatment relies on empiric suggestions concerning diet, fluid intake, bowel care, local ointment. Further studies are needed in order to identify a targeted etiologic treatment. [ABSTRACT FROM AUTHOR]

Previous studies have examined the relationship between social media and extroversion, often relying on correlational designs. This study introduced a social media induction procedure to examine the relationship between social media and extroversion with an experimental design. In a procedure adapted from the personality research literature, participants used a written prompt to imagine themselves as a social media influencer and were then asked to use social media with this mindset. Participants' extroversion was measured before and after this activity using the Big Five Inventory. The results show that this social media induction procedure resulted in increased reports of extroversion after the task, and this post-task change was not predicted by other related social media factors (such as hours of social media use per day). These results complicate the assertion that differences in social media use between extroverts and introverts are due to its stronger appeal to extroverts. [ABSTRACT FROM AUTHOR]

Based on the extended parallel process model, this study investigated the relationship between young adults' media exposure to COVID-19 and their adoption of protective behaviors. This study surveyed 141 college students and found that increased risk perceptions led to greater intentions to engage in COVID-19-preventive behaviors and that these intentions were mediated by normative beliefs. There was no significant difference in risk perceptions between traditional media and social media. The results showed that college students took precautions against COVID-19 because they perceived themselves to be both vulnerable and capable. [ABSTRACT FROM AUTHOR]

The Internet has revolutionized communication, enabling interactions between individuals from diverse cultures. This is especially true for social media which encourages participation in millions of online communities on a wide range of topics, such as Reddit. Research has largely neglected the intricate relationship between new media and intercultural issues. This study aims to address this gap by exploring the use experience of bicultural and multicultural individuals on Reddit and investigating the impact of identity‐level (identity categorization, compartmentalization, and integration) and contextual‐level (identification with the online network, online intergroup contact frequency and quality) factors on intercultural sensitivity. A sample of 241 bicultural (n = 90) and multicultural (n = 151) Reddit users (females = 100, 41.49%) participated in the study. Hierarchical regression results showed that both identity‐level and contextual‐level factors significantly contributed to intercultural sensitivity. Specifically, on the identity‐level, integration was positively related to intercultural sensitivity, while compartmentalization was negatively associated with it. Additionally, on the contextual‐level, identification with the online network and high‐quality online intergroup contact were associated with greater intercultural sensitivity. This research highlights the importance of understanding how intercultural dynamics unfold within social media platforms and provides insights for fostering intercultural sensitivity in online communities. [ABSTRACT FROM AUTHOR]

Precision dairy tools (PDT) can provide timely information on individual cow's physiological and behavioral parameters, which can lead to more efficient management of the dairy farm. Although the economic rationale behind the adoption of PDT has been extensively discussed in the literature, the socio-psychological aspects related to the adoption of these technologies have received far less attention. Therefore, this paper proposes a socio-psychological model that builds upon the theory of planned behavior and develops hypotheses regarding cognitive constructs, their interaction with the farmers' perceived risks and social networks, and their overall influence on adoption. These hypotheses are tested using a generalized structural equation model for (a) the adoption of automatic milking systems (AMS) on the farms and (b) the PDT that are usually adopted with the AMS. Results show that adoption of these technologies is affected directly by intention, and the effects of subjective norms, perceived control, and attitudes on adoption are mediated through intention. A unit increase in perceived control score is associated with an increase in marginal probability of adoption of AMS and PDT by 0.05 and 0.19, respectively. Subjective norms are associated with an increase in marginal probability of adoption of AMS and PDT by 0.009 and 0.05, respectively. These results suggest that perceived control exerts a stronger influence on adoption of AMS and PDT, particularly compared with their subjective norms. Technology-related social networks are associated with an increase in marginal probability of adoption of AMS and PDT by 0.026 and 0.10, respectively. Perceived risks related to AMS and PDT negatively affect probability of adoption by 0.042 and 0.16, respectively, by having negative effects on attitudes, perceived self-confidence, and intentions. These results imply that integrating farmers within knowledge-sharing networks, minimizing perceived risks associated with these technologies, and enhancing farmers' confidence in their ability to use these technologies can significantly enhance uptake. [ABSTRACT FROM AUTHOR]

While a rich literature investigates how and why NPOs use social media, research on why they differ in their social media adoption (SMA) is limited. In this paper we examine how NPOs' interorganizational partner portfolio characteristics can enable or constrain their adoption of social media, including blogs and videos, conventional social media (Facebook, Twitter...) and crowd-based platforms (crowdfunding and petitions). Based on a survey distributed to a sample of environmental NPOs in France, results indicate that NPOs having open networks, whose partners are physically distant, and that have more cross-sectoral partners have higher SMA. Network portfolio management can thus make up for a shortage of financial resources to invest in social media. [ABSTRACT FROM AUTHOR]

Objective: The widespread presence of students in Telegram virtual social networks (VSNs) has provided a unique opportunity to track the effects of using this medium on their academic failure and mental health. Accordingly, this study explores whether the use of social media has a relationship with educational decline and student mental health. Methods: This correlation study was conducted in high schools in Khaf City, Iran, in the 2018-2019 academic year and included 1250 students (550 girls and 700 boys) who were selected via simple stratified random sampling. The data collection tools comprised a researcher-made questionnaire on the use of Telegram VSNs and the Keyes (2005) model of mental health. The data were analyzed using the SPSS software, version 22, and related tests. Results: We found a significant relationship between the use of the Telegram network and educational decline. There is also a significant relationship between the use of the Telegram network and mental health. As the use of Telegram VSNs increases, there is an increase in fatigue in class and a reduction in study hours. Hence, this affects students' educational decline directly and mental health adversely. Conclusion: We recommend that information about the positive and negative effects of Telegram VSNs should be provided to users, and measures should be taken in schools to introduce and create a culture and useful and scientific use of Telegram VSNs. [ABSTRACT FROM AUTHOR]

Directed and weighted graphs can be used for many real-world applications to model and analyse the quality and structure of communication within the system, the distribution and flow of information, and various resources, dependencies, resilience, etc. On social media platforms, for example, highly networked members, so-called influencers, disseminate information, opinions and trends to their followers, who in turn increase the popularity of the influencers through likes and comments. Both types of interaction have a major influence on discussions and activities in the social network. To identify the nodes with the highest integration and interconnectivity within the neighbourhood subnetwork, we introduce the Directed Semi-Local Integration ( D S L I ) centrality measure for directed and weighted graphs. This centrality measure evaluates the integration of nodes assessed by the presence of connection, the strength of links, the organisation and optimisation of inbound and outbound interconnectivity, and the redundancy in the local subnetwork, and provides a stronger differentiation of the importance of nodes than standard centrality measures. Thus, D S L I has the potential to be used for analysing the degree of integration for the uptake and dissemination of resources in complex networks in many different contexts. [ABSTRACT FROM AUTHOR]

Although an increasing body of literature has linked social experiences to physical health, research has yet to consider how specific aspects of social experiences taking place on social media during late adolescence may predict future physical health outcomes. This study thus examined qualities of social media posts received from peers at age 21 as predictors of participants' physical health (e.g., Interleukin-6 (inflammation), sleep problems, problems with physical functioning, and BMI) at age 28. Participants included 138 youth (59 men and 79 women); 57% of participants identified as White, 30% as Black/African American, and 13% as from other or mixed racial/ethnic groups. Posts from friends and participants at age 21 characterized by social ties predicted lower levels of future physical health problems, whereas socially inappropriate "faux pas" posts that deviated from peer norms by friends predicted higher levels of physical health problems at age 28. These associations were found after accounting for factors typically associated with physical health outcomes, including participants' baseline social competence, internalizing and externalizing symptoms, alcohol use, observed physical attractiveness, and history of prior hospitalizations. The results of this study suggest the importance of both achieving social integration with peers online and adhering to peer norms in the online domain as key predictors of future physical health. [ABSTRACT FROM AUTHOR]

Recognising the rapid growth and enormous potential of gamification and social media advertising in today's digital age, along with the increasing popularity of social commerce (s-commerce), especially in emerging markets, this study analysed attitudes towards gamification advertising in the Vietnamese s-commerce context. This study followed a quantitative approach by conducting interviews with 386 users who interacted with gamification advertising on s-commerce platforms in Vietnam. The empirical results indicate that perceived gamification in advertising (as measured by the experience of gamification ads) has a significant positive indirect effect on attitudes towards advertising via hedonic and utilitarian values. In addition, this study experimentally verified the impact of the prominent characteristics of s-commerce (interactivity, personalisation, and privacy concerns) on both the gameful experiences of ads and attitudes towards advertising in the s-commerce market. Our research has meaningful implications for marketers and social networking platform developers, in that marketers should be more proactive in gamifying advertisements to improve customer attitudes towards them. For social networking platform developers, it is essential to develop optimal algorithms to enhance the interactivity of the platform by taking advantage of a database to generate relevant advertising recommendations for users. [ABSTRACT FROM AUTHOR]

TikTok is known for its lighthearted dance and lip-synch videos, yet videos with the hashtag #politics have garnered nearly 14 billion views. Does young adults' politically oriented expression on TikTok lead to increased civic engagement offline? TikTok helps incorporate young adults into political social networks that may encourage additional civic activity. In addition, the playful, humorous nature of TikTok-based political expression encourages young adults to develop participatory, political selves. Using data from a 2020 survey of Americans between 18 and 25 years old, we find that posting political videos on TikTok connects with higher offline civic engagement. The results suggest that playful political expression is an important feature for promoting young adult civic engagement. [ABSTRACT FROM AUTHOR]

With the rise of social media, politicians face new challenges and opportunities in presenting their messages online to large and diverse constituencies. Populists especially have capitalized on social media to achieve wide electoral success. This paper explores populists' opportunities to target their constituencies online using the case of India's Bharatiya Janata Party (BJP) on Twitter. First, the structure of the BJP's network on Twitter is analyzed through social network analysis. Second, a content analysis is used to examine the messages of important accounts in different networks clusters. The findings suggest the ruling party in India has the opportunity to employ microtargeting to siloed clusters within its network of supporters. This practice could damage democratic discourse by reducing the possibility of debate in the public square. [ABSTRACT FROM AUTHOR]

In this study, we explore two parallel networks of discourse during the United Nations Framework Convention on Climate Change (UNFCCC) negotiations of 2019 in Madrid (25th Conference of the Parties, COP25): one produced by news media coverage of the talks; the other by Twitter users who shared news content about the talks. Findings show that transnational public spheres can emerge out of relatively homogeneous moments internal to networks and external to networks (i.e. across multiple networks) at the intersection of certain actors and topics, cultural practices, and commercial and non-commercial (state) institutions. Yet there are persistent divisions along language, geographic, and other lines that encourage the formation of distinct micro-spheres of networked actors (internal heterogeneity), as well as distinct media practices that work to differentiate mass media networks from networks produced by a different set of publics on social media (external heterogeneity). [ABSTRACT FROM AUTHOR]

There is little research that explores the experiences of Black people exposed to police killings of other unarmed Blacks, despite the fact these incidents repeatedly and disproportionately affect this population. While some quantitative studies have examined the impacts of police violence exposure on mental health in recent years, there remains a dearth of in-depth qualitative research exploring the processes through which chronic exposures to police killings contribute to traumatic and complex life experiences for Black people, particularly. Informed by theories of critical race, stress and cognitive appraisal, historical trauma, and loss and grief, this qualitative study used in-depth semi-structured interviews to explore the experiences of stress, trauma, and grief among Black undergraduate college students exposed to police killings of other unarmed Black people, and to examine the perceived impacts of these experiences in the lives of students and their close personal networks. [ABSTRACT FROM AUTHOR]

Food literacy has emerged as a key individual trait to promote the transformation of food systems toward healthy and sustainable diets. Childhood and adolescence are key periods for establishing the foundations of eating habits. Different food literacy competencies are acquired as children develop different cognitive abilities, skills, and experiences, contributing to the development of critical tools that allow them to navigate a complex food system. Thus, the design and implementation of programs to support the development of food literacy from early childhood can contribute to healthier and more sustainable eating habits. In this context, the aim of the present narrative review is to provide an in-depth description of how different food literacy competencies are developed in childhood and adolescence, integrating the extensive body of evidence on cognitive, social, and food-related development. Implications for the development of multisectoral strategies to target the multidimensional nature of food literacy and promote the development of the 3 types of competencies (relational, functional, and critical) are discussed. [ABSTRACT FROM AUTHOR]

The present investigation is part of the Social Networks and Agency Project (SNAP), an 18-month longitudinal mixed methods study following adolescents located in New South Wales, Australia. The SNAP study aims to understand how online and offline social networks impact the development of sexual agency over time. This paper presents an analysis of the second interview time point in the SNAP study. Fifty adolescents (aged 14–17) completed a semi-structured interview either face-to-face or via Skype about their romantic relationships, including where they get their sexual health information and how they flirt (both online and offline). Interviews were transcribed and analyzed using a grounded theory approach. The core theme was: Young people clearly demonstrate emerging sexual agency. The three characterizing themes were: (1) Young people have complex schemas about flirting, dating, and entering romantic and sexual relationships; (2) Young people have high literacy in social media, traditional media and pornography; and (3) Peers influence most aspects of developing sexual agency, including sex and relationship attitudes. We discuss possible methods to utilize adolescents' social networks in facilitating the development of sexual agency. [ABSTRACT FROM AUTHOR]

The popularity of social networks as primary mediums for sharing visual content has made it crucial for forensic experts to identify the original platform of multimedia content. Various methods address this challenge, but the constant emergence of new platforms and updates to existing ones often render forensic tools ineffective shortly after release. This necessitates the regular updating of methods and models, which can be particularly cumbersome for techniques based on neural networks which cannot quickly adapt to new classes without sacrificing performance on previously learned ones – a phenomenon known as catastrophic forgetting. Recently, researchers aimed at mitigating this problem via a family of techniques known as continual learning. In this paper we study the applicability of continual learning techniques to the social network identification task by evaluating two relevant forensic scenarios: Incremental Social Platform Classification , for handling newly introduced social media platforms, and Incremental Social Version Classification , for addressing updated versions of a set of existing social networks. We perform an extensive experimental evaluation of a variety of continual learning approaches applied to these two scenarios. Experimental results demonstrate that, although Continual Social Network Identification remains a difficult problem, catastrophic forgetting can be significantly mitigated in both scenarios by retaining only a fraction of the image patches from past task training samples or by employing previous tasks prototypes. • We investigate continual learning methods for social network identification. • We exploit a state-of-the-art dual-branch neural network designed for this task. • We define two realistic experimental scenarios on multiple datasets. • Exemplar-based methods yield good performance with limited memory requirements. • Prototype-based methods are a viable solution when storing exemplars is not feasible. [ABSTRACT FROM AUTHOR]

Social media networks provide an aggregation of news and content, allowing users to share and discuss topics of greatest interest to them. Users can enrich the news by providing context and opinions that are useful to other users. Understanding topics of interest sheds light on the collective thinking of a group of individuals and offers important insights for exploring a given field. Among the fields of interest on social media networks, finance stands out. Automatically identifying and organizing the main issues that users discuss can be useful for multiple purposes, e.g., identifying the preferred types of loans could be useful for refining targeted advertising. Our work aims to identify and organize the topics discussed on a social media network that are related to the financial sector. For this, we propose an approach that consists of analyzing posts from Reddit communities oriented to finance. First, posts were gathered and cleaned to remove punctuation, links, and images. Then, textual similarity was computed to match posts with classes from dedicated ontologies designed for the financial sector. Finally, the populated ontology was analyzed to identify clusters of concepts. The results showed that the proposed approach and corresponding tool can summarize topics from a large number of Reddit posts using the identified classes. Over 70% of posts were linked to ontologies when considering both posts and comments, which shows that the automatic support given to posts related to financial concepts had a high degree of success. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to explore unpaid carers' experiences of supporting people with dementia to use social media.Methods: Unpaid carers (n = 234) responded to an online survey about their attitudes towards people with dementia using social media and any experiences supporting this usage. Responses to closed questions were analysed using frequency analysis; qualitative data were analysed thematically.Results: Fifty-five carers (23.5%) cared for someone with dementia who used social media. Thematic analysis produced four themes: (1) carers as social media navigators; (2) social media supports care; (3) carers as social media guardians; and (4) labour-intensive work. Carers valued the social connectivity and stimulation social media provided but remained vigilant about online safety. They carefully managed the online experiences of people with dementia, balancing perceived benefits with safety, security, and caring demands.Conclusions: These findings shed light on the complexities of caring in the digital age. Many carers are supporting people with dementia in using social media, but there is little guidance on how best to do this. As older adults continue to embrace social media, carers, support organisations, and policymakers must adapt and work with technology developers to ensure safe and supportive online experiences. [ABSTRACT FROM AUTHOR]

Within growing marketisation of publicly funded services, the internet has provided new opportunities for marketing, delivery, and coordination of those services. Using web scraping and hyperlink network analysis techniques, this paper examines the ways in which organisations operating in Australia's evolving National Disability Insurance Scheme (NDIS) system inter-connect online. Social media plays the most important role in the online network. Government agencies also play a central role, with many disability service organisations linking their web users to them. Government agency websites do not hyperlink to disability service providers, suggesting that governments do not see their role as assisting access to such services. Advocacy and peak disability organisations are important in online connections between the websites of government and service organisations. Innovative uses of the internet for online brokerage of disability services are evident. The implications of these findings for service delivery are discussed. [ABSTRACT FROM AUTHOR]

Social media has become an outlet for extremists to fundraise and organize. While governments deliberate on how to regulate, some social media companies have removed creators of offensive content —deplatforming. I estimate the effects of deplatforming on revenue and viewership, using variation in the timing of removals across two video-streaming companies — YouTube, and its far-right competitor, Bitchute. Being deplatformed on Youtube results in a 30% increase in weekly Bitcoin revenue and a 50% increase in viewership on Bitchute. This increase in Bitchute activity is less than that on YouTube, meaning that deplatforming works in decreasing a content creator's overall views and revenue. [ABSTRACT FROM AUTHOR]

The proliferation of harmful content and misinformation on social networks necessitates content moderation policies to maintain platform health. One such policy is shadow banning, which limits content visibility. The danger of shadow banning is that it can be misused by social media platforms to manipulate opinions. Here we present an optimization based approach to shadow banning that can shape opinions into a desired distribution and scale to large networks. Simulations on real network topologies show that our shadow banning policies can shift opinions and increase or decrease opinion polarization. We find that if one shadow bans with the aim of shifting opinions in a certain direction, the resulting shadow banning policy can appear neutral. This shows the potential for social media platforms to misuse shadow banning without being detected. Our results demonstrate the power and danger of shadow banning for opinion manipulation in social networks. [ABSTRACT FROM AUTHOR]

Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others. Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implemented using methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and time series prediction with the Prophet model. As a practical demonstration of the proposed model, we use tweets as data source (from the social network X, formerly known as Twitter) generated during the COVID-19 pandemic lockdown period in Spain, which are processed to identify the overall sentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined with official statistical indicators for prediction, visualizing the results through dashboards. [ABSTRACT FROM AUTHOR]

Resilience communication is a fundamental tool for destinations to improve their image and recover after a crisis. Social media are also important tools for tourism communication and for creating tourist experiences and destination image. Therefore, social media are key tools for crisis communication. Based on a series of hypotheses, this paper proposes a conceptual model of crisis communication management that responds to the need to establish a set of premises related to resilience communication in the tourism sector. The empirical analytical study consisted of the quantitative analysis of the results of a survey questionnaire applied to test the hypotheses based on the formulation of a structural equation model. The results demonstrate the importance of social media communication in Perceived Health Risk (PHR) and its effect on perceived impressions, destination image, and related behavioral responses (Destination Recommendation, DR, and Visit Intention, VI). The four hypotheses in the research model have been confirmed, as the effects are statistically significant in a sample of posts analyzed, and the selected items explain every one of the factors. Specifically, the analysis shows that PHR negatively affects Destination Image (DI) and, in a stronger way, Post Impression (PI). Secondly, PI and DI influence one another and covary to some extent. Moreover, the overall DI, which has been affected by PHR, in turn affects DR and VI, demonstrating that PHR in communication ultimately influences DR and VI negatively too. Finally, is also verified both DR and VI are related and covary as intrinsic behavioral aspects related to DI. Resilience communication is a basic tool for destinations to improve their image and recover after a crisis and this research contributes to the literature on the subject by analyzing the impact of this communication on tourists, and specifically on destination image. [ABSTRACT FROM AUTHOR]

The realm of Natural Language Processing and Text Mining has seen a surge in interest from researchers in hate speech detection, leading to an increase in related studies. This analysis aims to create a valuable resource by summarizing the methods and strategies used to combat hate speech in social media. We perform a detailed review to achieve a deep knowledge of the hate speech detection landscape from 2018 to 2023, revealing global incidents of hate speech in 2022-2023. Sixty-six relevant articles were selected for this review. Existing studies were analyzed and categorized into five method categories: Machine Learning, Deep Learning, Ensemble models, Graph Neural Networks, and Graph Convolutional Networks. These advancements can aid social networking services in identifying hate messages before being posted, reducing the risk of harassment. The review also covers available hate speech datasets and highlights research challenges, but it is clear that a definitive solution to this problem is yet to be found. Future research directions are recommended to address the ongoing challenges in Hate Speech Detection. [ABSTRACT FROM AUTHOR]

Social networking platforms have evolved into a self-promotional space. The LinkedIn platform allows users to share knowledge, research accomplishments, and network in an academic setting. Our objective was to determine LinkedIn usage trends among surgeons at a large academic institution. A list of surgeons within the department of surgery at a large academic institute was compiled. All publicly available profiles were analyzed for their LinkedIn activity and followership. Active accounts were defined as users who have posted or interacted with any posts within the last year. Comparisons of LinkedIn usage grouped by gender, surgery division, and rank were analyzed. A total of 133 surgeons were included. Among these surgeons, 88 had a LinkedIn profile (66.2%); however, only 43 surgeons had active usage on this platform (32.3%). The median number of followers among surgeons with a profile was 110 (IQR [24–427]). Male surgeons had a higher median of followers (167 IQR [38–502]) compared to female (54 IQR [21–209]). A greater percentage of male surgeons had a profile compared to female surgeons (68.5% M versus 61.0% F, P = 0.12). The transplant surgery division had the highest percentage of LinkedIn accounts (90.9%) followed by surgical oncology (87.5%). However, active usage was led by the pediatric surgery division (80.0%), followed by the plastic surgery division (71.4%). Instructors, assistant professors, associate professors, and professors all had LinkedIn accounts; however, instructors were the least active users with 33.3% being active on their LinkedIn. The lowest percentage of those with LinkedIn accounts were instructors (50%). Professors were found to be the most active users at 57.1%. Social media use is on the rise in academic surgery. Within a department of surgery at a large academic institution, only 32.3% were active users of LinkedIn, suggesting that there is room for improvement in utilizing this resource as a tool for mentorship, professional development, and networking. [ABSTRACT FROM AUTHOR]

The popularity of automated features, such as autocorrect, reflects an interesting paradox in digital media use: while users appreciate the interactivity afforded by these media, they also seem to enjoy passively observing the system perform the interaction on their behalf. We aim to understand this paradox by using the concept of interpassivity and exploring the primary gratifications users seek in automated features. Following the research methods in U&G research, we first conducted three focus groups to generate a list of 66 gratification items, which were subjected to exploratory factor analysis in a survey study (N = 498). Results show that convenience, user control, and user profiling are three distinct gratifications of automated feature usage. Furthermore, user control is universally desired across features, and user profiling motivates the use of all automated features. We discuss the implications of these findings for U&G research and interface design of automated features. [ABSTRACT FROM AUTHOR]

This study examines the effect of social learning on new turfgrass variety adoption decisions using data from 231 turfgrass professionals' Twitter accounts between 1 Jun 2018 and 31 Dec 2019. To determine the social learning effect, we decompose networking effects into social learning, individual-level and group-level similarities, herd behavior, and clustering effects. Our study estimates a spatial autoregressive probit model that directly incorporates the social network structure to account for unobservable networking effects and potential reflection problem. A Bayesian estimation procedure is used to alleviate the convergence problem caused by the complexity of model specification. Empirical results show that the social learning effect positively influences the new technology adoption and was greater than herd behavior effect. The results also suggest that turf professionals rely more on suggestions and information from online social networking among themselves than recommendations from advisors. [ABSTRACT FROM AUTHOR]

Delving into the intricate world of social media engagement, this comprehensive study analyses the dynamics of user interaction with posts from 5- star hotels on Facebook across the Visegrad Group countries. It meticulously aims to shed light on the variances among engagement metrics--shares, comments, and reactions--across different types of posted content. Furthermore, it explores the complex interrelations among these metrics to provide a holistic understanding of user engagement patterns. In pursuit of this goal, we scrutinized an extensive dataset comprising 10,820 Facebook posts shared by selected 5-star hotels throughout 2019. The data were meticulously collected from the social network utilizing the accessible API, ensuring a robust and reliable foundation for analysis. The investigation employed advanced statistical tools, namely the Kruskal-Wallis test and Spearman's rho test, to thoroughly examine and interpret the complex data. The insights gleaned from this research are invaluable, painting a detailed picture of brand management strategies on social platforms. A significant finding of this study is the variation in user engagement levels in relation to the type of content disseminated. It highlights that visual content--specifically videos and photos--tends to dominate in terms of eliciting user responses, as compared to text statuses and links. This underlines the importance of leveraging visual media to captivate and engage the audience effectively. The study also reveals that engagement metrics are intricately linked, suggesting a synergistic effect rather than isolated impacts. This indicates that an integrated approach, considering these metrics as a cohesive unit, could be more beneficial in strategizing content for social media. Understanding these relationships and the dominant role of visual content can greatly inform and transform the way brands, especially in the hospitality industry, navigate their presence on social networks. These findings serve as a guiding framework for optimizing social media content strategies, aiming to maximize engagement and enhance the effectiveness of online brand management. [ABSTRACT FROM AUTHOR]

Social media and health research have covered the benefits for the public and patients as users. Specifically, this has focused on searching for health information, connecting with others experiencing similar health issues, and communicating with their health professionals. Recently, there has been a shift in research to focus on health care professionals as users as they participate in professional development, improve communication with patients, and contribute to health research and service. However, such research has predominantly focused on text-based platforms, namely Facebook and Twitter. The scope of this article is a systematic review of publications on health care professionals' use of the image-based platform Instagram, according to the preferred reporting items for systematic reviews and meta-analyses guidelines. This study, drawing from 51 articles, shows how health care professionals use Instagram, and reveals that these professionals utilize the platform to address health concerns that may not necessarily align with their specific expertise. Images were the common format of posts created by health care professionals, with six content types identified: (a) educational, (b) promotional, (c) patient experience, (d) personal, (e) emotion based, and (f) other. Three measures of post engagement were used by researchers, including (a) likes and comments, (b) use of hashtags, and (c) number of followers. This study also identified the dangers of misleading users, including (a) lack of credentials reported, (b) edited images, (c) quality of content, and (d) patient and client confidentiality issues. In conclusion, insights into the advantages of health care professionals' use of Instagram and ways in which they can maximize its use to reach and engage with their target audience are provided. [ABSTRACT FROM AUTHOR]

Hyper-realistic images that are not differentiable from authentic images to regular viewers have become extremely easy to generate and highly accessible. Furthermore, the increasing pervasiveness of social media networks in our daily lives has facilitated the easy dissemination of fake news accompanied by such synthetic images. Hyper-realistic artificial face images are often illicitly used as profile pictures on social media sites, further using such profiles to spread fabricated information, resulting in social perils. Most available synthetic image detectors are challenging to implement in practical scenarios due to their high complexity and performance degradation for images from Online Social Networks (OSNs). In this work, we develop a deep learning-based lightweight synthetic image detector called Relative Chrominance Distance Network (RCD-Net). In this paper, we introduce the RCD image feature set for the first time, which gives a pair-wise chrominance component-based distance measure. To show its effectiveness, we explore multiple luminance-chrominance spaces. Compared to the state-of-the-art (SOTA), our model hugely reduces the network parameter requirements, making it incredibly lightweight. We also study the robustness of the proposed solution against common post-processing operations in the context of online social media networks. Experimental results prove that the proposed solution achieves SOTA performance at a much lower complexity than available solutions. • GAN-generated synthetic images, visually indistinguishable from authentic photos, pose a serious threat to society. • Synthetic images exhibit different statistical properties in the colour domain than authentic images. • Our proposed feature RCD (Relative Chrominance Distance) performs excellently to differentiate real and synthetic images. • Proposed deep RCD-Net feeds on RCD, high-frequency residual, luminance features; achieves extreme parameter optimization. • RCD-Net achieves high robustness against common image post-processing operations and compression. [ABSTRACT FROM AUTHOR]

While existing research shows why politicians' social media messages spread online, we know comparatively less about the types of individuals who see these messages. The current study tests whether Americans' exposure to posts from political elites is best explained by their partisan allegiance (homophily) or the intensity of their political engagement. To test this question, we employ data from a 2020 Cooperative Election Study module that asks respondents how often they encounter social media posts from various political figures. We find that both homophily and intensity characterize exposure to elite messages: partisans and ideologues not only tend to encounter posts from politicians on their own side of the aisle most often, but they also encounter posts from politicians on the opposite side more often than do independent or moderate respondents. The role of intensity relative to homophily is greatest for posts by former President Donald Trump, which Democrats were more likely to encounter than Republicans or independents. [ABSTRACT FROM AUTHOR]

This work analyzes the new visual conventions on YouTube used by content creators with the aim of determining the editing techniques and resources they employ and comparing their techniques with the conventional rules of film editing. The initial hypothesis is that YouTubers have departed from the rules of classical film editing and have created their own editing code on YouTube. To study and demonstrate this phenomenon, the testimonies and videos of various lifestyle-themed content creators on YouTube are taken as the subject of analysis. The results conclude that the new creators of digital audiovisual content have developed their own editing approaches inspired by the emerging audiovisual trends of social media. [ABSTRACT FROM AUTHOR]

Artificial intelligence and the evolution of social networks have allowed the appearance of virtual influencers with human appearance, especially on Instagram. These can be defined as computer-designed figures that show an image like humans on social networks. This article applies an exploratory-descriptive design that aims to analyze, through a qualitative study that follows the case method, the definition of what human-looking virtual influencers are and how they act on Instagram. In addition, it's also been analyzed how these figures work when working with brands as advertising resources. The results of this study reveal the way in which these figures behave and allow brands to understand their functioning as powerful supports for business communication. In addition, we provide a first study instrument through two analysis sheets that are based on literature and observation of the phenomenon on Instagram. Both sheets are applicable to other cases. [ABSTRACT FROM AUTHOR]

We propose a quantum-mechanical model that represents a human system of beliefs as the quantised energy levels of a physical system. This model represents a novel perspective on opinion dynamics, recreating a broad range of experimental and real-world data that exhibit an asymmetry of opinion radicalisation. In particular, the model demonstrates the phenomena of pronounced conservatism versus mild liberalism when individuals are exposed to opposing views, mirroring recent findings on opinion polarisation via social media exposure. Advancing this model, we establish a robust framework that integrates elements from physics, psychology, behavioural science, decision-making theory, and philosophy. We also emphasise the inherent advantages of the quantum approach over traditional models, suggesting a number of new directions for future research work on quantum-mechanical models of human cognition and decision-making. [ABSTRACT FROM AUTHOR]

The prevalence of smartphones and social networking sites (SNSs) has given rise to the popularity of selfie posting, presenting one's own photograph on SNSs. While previous studies have mainly investigated the effects of personal characteristics (e.g. demographics, personality traits and motivational needs) on selfie posting, the impacts exerted by individuals' social networks have been largely neglected. Drawing on the Actor-Network Theory, this study explores besides personal traits (i.e. personalities and demographics), how relational characteristics of one's ego networks (i.e. gender heterogeneity, age homophily, average tie strength, and network density) and SNSs affordances (i.e. connectivity and interactivity) affect selfie posting behavior. Based on a survey sample in which the respondents' ego network data were collected, individuals' ego-network metrics were calculated and analyzed. Our analyses show that those whose ego network having a higher proportion of opposite sex (measured by gender heterogeneity) or/and having alters more connected with one another (measured by network density) are more likely to post selfie on SNSs, while those embedded in a strong-tie network (measured by average tie strength) are less likely to post selfie on SNSs. The findings suggest that characteristics of one's ego network exert important influences on selfie posting on SNSs. [ABSTRACT FROM AUTHOR]

Misinformation disseminated via online social networks can cause social confusion and result in inadequate responses during disasters and emergencies. To contribute to social media-based disaster resilience, we aim to decipher the spread of disaster misinformation and its correction through the case study of the disaster rumor during Hurricane Sandy (2012) on Twitter. We first leveraged social network analysis to identify different types of accounts that are influential in spreading and debunking disaster misinformation. Second, we examined how the spatiotemporal proximity to the rumor event influences the sharing of misinformation and the sharing of corrections on Twitter. Third, through sentiment analysis, we went further by examining how spatiotemporal and demographic similarity between social media users affect behavioral and emotional responses to misinformation. Finally, sentiment contagion across rumor and correction networks was also examined. Our findings generate novel insights into detecting and counteracting misinformation using social media with implications for disaster management. [ABSTRACT FROM AUTHOR]

Objective: (a) to identify the Social Media Platform (SMP) most used to search for information about the Covid-19 pandemic and its users' profiles; and (b) to identify the relationship between the constructs of Knowledge Quality (KQ), Knowledge Donation (KD), and Knowledge Collection (KC) in the scope of the Covid-19 pandemic and using a knowledge quality scale and a knowledge donation and collection scale. Design/methodology/approach: Data was collected through a survey with 295 answers. First, we analyze the profile of SMP users through exploratory factor analysis and reliability, which was used to test the hypotheses, followed by multigroup analysis. Findings: Most participants are women between 25 to 34 years with complete post-graduation. The results suggest that the most used SMP to search for or receive information about Covid-19 was Facebook. Most of the respondents identified fake news on SMP but ignored them. The model presents reliability and adequate convergent and discriminant validity. KC represented a medium effect on KQ and KD. The multigroup analysis shows that there is a difference between age groups. Originality: This is the first study to test the relation between the constructs KQ, KC, and KD using data from the Covid-19 pandemic on SMPs. [ABSTRACT FROM AUTHOR]