Social Networking Activities Research Articles

FC34: Cognitive reserve and depressive burden in older adults: variation according to reserve measurement

Objective:Individual differences in the timing of dementia have been attributed to cognitive reserve (CR), thought to reflect lifelong engagement in stimulating experiences, which provide resilience against brain pathology. In older adults, dementia and depression are closely related, and some studies have linked CR with depression risk in old age. It is unclear if different ways of operationalizing CR exhibit similar association with old-age depression. We examined the association of two measures of CR with depressive burden in older adults: activity-based CR, capturing engagement in stimulating activities using proxy variables, and residual-based CR, indicating residual variance in cognition, not explained by the brain status.Methods:We used data on 354 adults aged 60+ from the Swedish National Study on Aging and Care in Kungsholmen, followed for 15 years. Residual-based reserve was computed from a regression predicting episodic memory with a brain-integrity index incorporating six structural neuroimaging markers (white- matter hyperintensities volume, whole-brain gray matter volume, hippocampal volume, lateral ventricular volume, lacunes, and perivascular spaces), age, and sex. Activity-based reserve incorporated education, work complexity, social network, and leisure activities. Depressive burden was captured over the follow- up with the Montgomery-Åsberg Depression Rating Scale and time until clinically relevant level of symptoms (>6) was modelled using Cox proportional hazard models.Results:Preliminary results indicate that, upon minimal adjustment (age, sex, brain integrity status), top tertiles (ref: bottom tertile) of both activity-based (HR: 0.77; 95% CI: 0.61-0.98) and residual-based CR (HR: 0.62; 95% CI: 0.44-0.98) were associated with a lower risk of depressive burden onset over 15 years. Upon further adjustment for anthropometrics, health behaviors, and chronic disease burden, the association of activity-based CR was attenuated, whereas residual-based CR preserved its effect on depressive burden (HR [fully adjusted model]: 0.59; 95% CI: 0.40-0.88). Next steps include evaluating the ability of reserve measures to attenuate the association of brain integrity with depressive burden using interaction analysis.Conclusion:Preliminary findings suggest that CR may be linked with depression development in older adults, although the association may vary depending on measurement of reserve. Association of activity- based reserve may be attributed to somatic disease pathways.

Social media use and various factors affecting indonesian mental health during the COVID-19: A pathway to remember

In the middle of the COVID-19 pandemic, Indonesia is experiencing a crisis in mental health, and it appears that social media is making the issue much worse. This study examines the impact of social media use and its elements on Indonesians' mental health as a first step towards identifying potential disasters. The study uses a convergent mixed methods approach, utilizing both quantitative data measured by social networking activity intensity scale (SNAIS) and general anxiety disorder 7 scale (GAD-7), and qualitative data collected through open-ended questions answered by 347 Indonesians aged 17-65 years old. The results indicate a significant link (p<0.05) between excessive social media use during the pandemic and unfavorable impacts on users' mental health, specifically the development of anxiety. The study also finds that hoaxes and rumors on social media about COVID-19 infections, inadequate health facilities, and the Indonesian government's slow response contributed to the population's uneasiness. According to the studies, social media literacy is needed to managing social media use during times of crisis. This study adds to the social media-mental heath literature. It illuminates Indonesians' challenges, providing insights that might improve public health policies, mental health therapies, and educational efforts to encourage good social media practises.

Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.

Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.

Стратегическое управление рекламной деятельностью в социальных медиа

Advertising remains the most effective tool of marketing communication. Advertising can create an attractive public image for a company by promoting its achievements and efforts to satisfy customer needs. This research featured the potential of social media marketing and a system for strategic management of advertising activities in social networks that could develop this potential. The authors appealed to the theory of strategy and the methodology of strategizing developed by Professor V.L. Kvint in an attempt to describe the core elements of the social media marketing system and their strategic management. Other research methods involved standard analysis, synthesis, comparison, generalization, and scientific abstraction. The study made it possible to draw the following conclusions. A well-structured strategy takes into account the interests of the manufacturer (seller) and consumer (buyer). It relies on planning, motivation, monitoring, and control over the achievability of strategic management goals. Such a strategy can increase the effectiveness of social media marketing. The methodology proposed by V.L. Kvint makes it possible to navigate a social media marketing project through all its stages.

A hybrid machine learning-optimization framework for modeling supply chain competitive pricing problem under social network advertising

Designing a system to improve the performance of the supply chain by linking pricing and advertising will bring significant benefits to the supply chain components. Due to the expansion of customers’ activities in social networks and society’s greater interest in obtaining information from this space, companies have changed their marketing methods. Organizations have turned to influencer marketing for advertising to take advantage of social networks’ potential. Despite the recent growth of influencer marketing, the issue of optimal identification and appropriate spending based on the characteristics of influencers to convey a company message or advertisement has not been carefully studied in the academic literature, nor has it been addressed in practice. Therefore, in this paper, a hybrid framework of machine learning and optimization is developed to help organizations (i) conduct successful and optimal marketing campaigns by selecting the best influencers and (ii) examine the effectiveness of advertising on the pricing of products in the supply chain. The framework designed in this study is validated using data extracted from the Instagram platform. The results demonstrate that businesses should adopt novel approaches to get the maximum benefit from advertisements on social media, which depends on a more accurate selection of influential users. Contrary to previous works, our results demonstrate that choosing influencers based on their influence network will not necessarily bring the expected efficiency for businesses.

Media and obesity: bibliometric analysis

Obesity is a visible public health problem that remains a neglected issue. The media can promote healthy lifestyles, however, it can become a means of disinformation. The study's objective was to perform a bibliometric analysis on the coverage of obesity in the media and social networks through the search for scientific information in the Web of Science, where 1191 open-access articles were obtained, which were analyzed in the VosViewer software. It was observed that there has been an increase in scientific publications regarding obesity in recent years. The importance of physical activity, prevention, and influence of social networks is highlighted. However, there are gaps in research, especially in media and social media communication to prevent obesity. A multidisciplinary approach is required, involving the media, governments, and health professionals.

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

The sudden spread of the COVID-19 pandemic has convincingly proved the role of social networks in human life activity as an actor of interpersonal relations. The need for isolation and the limitation of face-to-face communication between people has significantly transformed the system of interpersonal connections through an extensive increase in social contacts in the virtual environment and the growing importance of online social services. In the study, we addressed the problem of diagnosing the indicator of social success of a personality, reflecting characteristics of its interpersonal relations in offline activity through the attributes of its virtual activity in social networks. The research was based on the methods of social network analysis and traditional ways of psychodiagnostics. Social networks were analyzed using social graphs - mathematical models that describe the characteristics of relationships between users in social networks through various metrics (friends, elements, edges, density, closeness centrality, degree centrality, clustering coefficient, etc.). The study aims to prove the predictive validity of social graph indicators as predictors of personality social success through correlations of graph characteristics reflecting the features of interpersonal relations of a social network user in a virtual environment with the socio-psychological hands of traditional psychodiagnostic tools. The study included 601 subjects. Data was collected using psychological questionnaires and personal profiles from the social network VKontakte. The results of the study showed correlations between several characteristics of social graphs (density, clustering coefficient, closeness centrality, etc.) and several indicators of psychodiagnostic techniques (Dominance index in relationships with others, Organizational abilities, Desire for people, Narcissism, etc.). The significant contribution of the research is to expand the methodological apparatus of psychological science and to open new methods of predicting interpersonal relations of personality through its activity in a virtual environment.

A Survey on Recommendation Methods Based on Social Relationships

With the rapid development of online social networks recently, more and more online users have participated in social network activities and rich social relationships are formed accordingly. These social relationships provide a rich data source and research basis for in-depth study on recommender systems (RSs), while also promoting the development of RSs based on social networks. To solve the problems of cold start and sparsity in RSs, many recommendation algorithms are constantly being proposed. Motivated by the availability of rich social connections in today’s RSs, a large number of recommendation techniques based on social relationships have been proposed recently, achieving good recommendation results, and have become the mainstream research direction in the field of RSs, attracting more and more researchers to engage in this research. In this study, we mainly review and summarize the social relationship-based recommendation methods and techniques in RSs, and study some recent deep social relationship recommendation methods and techniques based on deep learning (DL), including the latest social matrix factorization (MF)-based recommendation methods and graph neural network (GNN)-based recommendation methods. Finally, we discuss the potential impact that may improve the RS and future direction. In this article, we aim to introduce the recent recommendation techniques integrating social relationships to solve data sparsity and cold start, and provide a new perspective for improving the performance of RSs, thereby providing useful resources in the state-of-the-art research results for future researchers.

Prospects for the implementation of the concept of digital citizenship in scientific libraries

The relevance of the research problem is determined by the active development of the digital society, which requires the formation of new forms of civic participation of the generation of the digital age, and Scientific libraries as leading information and communication institutions of society play a significant role in forming the value foundations of the worldview of modern youth as citizens of the digital age. The purpose of the article was to determine the prospects for the implementation of the concept of digital citizenship in scientific libraries. The research methodology was based on a combination of source analysis, comparative analysis, dialectical, systematic and logical methods of scientific research. As a result of the conducted research, it was stated that the representatives of the digital generation in the socio-communication aspect are an independent, critical, socially active community, formed thanks to the active use of the Internet and social media for obtaining information and communication. The characteristic features of the worldview of a “digital person” include fluency in digital and information-analytical technologies, speed of search and perception of new information, short attention span, giving preference to visualized information, brevity of presentation, “clip-like” perception. It is the character of the “digital person” that defines the reader not of the future, but of the present, because the generation of 20-30-year-olds is the first generation that was born in the digital world and can no longer imagine life without the mobile Internet and various gadgets, which necessitates the global transformation of traditional forms of work of libraries in the direction of active use of digital technologies. The work emphasized that university libraries should provide progressive education based on the expansion and deepening of digital competences of users, taking into account moral and ethical, socio-legal and psychological factors. The implementation of the concept of digital citizenship necessitates active digitization of library funds, expansion of the spectrum of virtual products and services, as well as active positioning of the library's activities in social networks. Prospects for further research consist in the expansion and deepening of practical recommendations for the formation of the concept of digital citizenship by means of library practices

Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools

This paper presents a scientifically based approach to analyzing large volumes of data from digital traces of students on social networks, which allows you to effectively identify emerging and most discussed problems among students, as well as highlight pain points that provide opportunities for growth, development of universities and improvement of the characteristics of the educational process, support for students etc. The study is based on a thematic analysis of messages published in university communities on the VKontakte social network using big data tools. The study results showed that Russian university students still face a number of challenges, including weak technical infrastructure at universities, a digital divide in access to online education, and negative attitudes towards distance learning.The scientific problem of the study is the contradiction between the existing volume of unstructured data of students’ digital traces in social networks and the lack of a scientifically-based and proven methodological approach to the analysis and evaluation of this voluminous data, which creates obstacles to fundamental research into the relationship between students’ activity in social networks and their satisfaction quality of the educational process. The practical focus is determined in conducting data analysis using big data tools. The findings and evidence-based implications are useful for developing innovative strategies and tools for assessing and supporting students.The results show that the use of big data tools for tracking trends based on digital traces of students on social networks provides highly accurate analytical data and can become the basis for identifying problematic situations in individual universities and the industry as a whole, for data-driven decision-making and management .

Identity, sociality and mobility: understanding internal fisher migration along India’s east coast

Fisher movement in the pursuit of fish is a well-established truism. In this paper, we explore the motivations and mechanisms deployed for internal migration within the fishing sector in coastal Tamil Nadu, rather than only on the seas, as a strategy for both economic and social mobility. Marine fisheries in India is a caste-based occupation, with its own social and political hierarchy, responsible for the governance and management of common resources. For those belonging to the subordinate fishing castes, excluded from decision-making processes, migration is an important strategy for gaining economic resources, social power and recognition as skilled and successful marine fishermen. Using qualitative research methods, the paper explores the migration of fishermen from Rajakuppam, a small fishing village in Cuddalore district, belonging to such a subordinate fishing caste, to Kasimedu, in the capital city of Chennai, the largest fishing harbour in the state of Tamil Nadu. We find that family and its social organization, in particular kinship and marriage ties, brokered by senior women, are significant factors in facilitating successful migration. Recognizing women’s contributions to the sector, both direct and through their social reproductive and networking activities, invisible in both the larger maritime literature and production-centric fisheries’ policy-making, is crucial for achieving wellbeing and sustainability outcomes.

Information and communication activities and analytics during the Russian-Ukrainian war (on the example of social networks)

The article analyzes the importance of information and communication activities during the war. In particular, it is determined that today there is a battle between Ukraine and the Russian Federation for a narrative, where both countries are looking for legitimacy and success in spreading correct information for themselves, because the perception of these narratives directly affects the course of hostilities. The article outlines the main narratives promoted by Ukraine and Russia.&#x0D; The research hypothesis is that the world's first war of social networks is taking place in Ukraine. The interdependence between the spread of the mobile Internet, access to modern gadgets and the activity of propaganda activities in social networks is analyzed.&#x0D; The main attention in intelligence is paid to the Internet as one of the most influential channels of communication, which is capable of reaching the maximum number of recipients in the shortest possible time, who then distribute the necessary information independently. The article proves that it is social networks that contain a large share of disinformation, which is "thrown in" by information scraps and fake pages. It is noted that fake or untrue content has a much higher chance of spreading than true information.&#x0D; The article reveals the communication and technological specificity of the Internet environment in terms of propaganda influence, for which even the leading Ukrainian broadcasters (STUDIYA "1+1", "UA: First", "Rada", "ICTV", "STB" and "Ukraine 24 ") united in the joint telethon "Edyni Novyni" to show the official position of the state, sometimes use unverified information from the Internet and, accordingly, resort to spreading fakes or disinformation. In this context, the activity of the single news marathon against the background of the single information pool of Russia, which is under the control of the Kremlin, is analyzed.

More Talk, More Support? The Effects of Social Network Interaction and Social Network Evaluation on Social Support via Social Media.

Do more friends and more frequent interactions on social media result in more social support? The impact of variables such as interaction size and frequency on the social support individuals receive via social platforms has been studied from the perspective of social networks, and some studies have focused on the role of interaction topics from the perspective of private engagement. Little research has been done on the impact of affect control embedded in social networks. The emotion-first nature of social media and the rise of affect control theory mean that this perspective deserves attention. This study examined 634 WeChat users by means of an online survey on variables related to social media use, such as social network size, interaction frequency, users' evaluation of network members, and personal topic involvement, and then tested the influencing mode of these variables on people's perceived social support through chain mediation analysis using Model 80 of the process in spss. (1) Interaction size influences perceived social support by affecting personal topic involvement; (2) Interaction frequency does not directly mediate the relationship between interaction size and perceived social support, but mediates the relationship in a chain by affecting personal topic involvement; and (3) Social network evaluation not only directly mediates the relationship between interaction size and perceived social support, but also further mediates the relationship by influencing personal topic involvement. The study confirmed the role of affective control in people's perceived social support through social network interactions. Those people talk about personal matters with individuals who have greater evaluation, potency, and activity in social networks are more likely to get perceived social support. The study draws our attention to the role of affection control in interpersonal relationships in the era of social media mediating people's daily interactions.

Hidden messages: mapping nations’ media campaigns

Powerful actors have engaged in information control for centuries, restricting, promoting, or influencing the information environment as it suits their evolving agendas. In the Digital Age, information control has moved online, and information operations now target the online platforms that play a critical role in news engagement and civic debate. In this paper, we use a discrete-time stochastic model to analyze coordinated activity in an online social network, representing the behaviors of accounts as interacting Markov chains. From a dataset of 31,521 tweets posted by 206 accounts, half of which were identified by Twitter as participating in a state-linked information operation, we evaluate the coordination, measured by the apparent influence, between pairs of state-linked accounts compared to unaffiliated accounts. Our analysis reveals that state-linked actors demonstrate significantly higher levels of coordination among themselves compared to their coordination with unaffiliated accounts. Furthermore, the degree of coordination observed between state-linked accounts is more than seven times greater than the coordination observed between unaffiliated accounts. Moreover, we find that the account that represented the most coordinated activity in the network had no followers, demonstrating the power of our modeling approach to unearth hidden connections even in the absence of explicit network structure.

Association of Loneliness With Change in Physical and Emotional Health of Older Adults During the COVID-19 Shutdown

To examine factors influencing loneliness and the effect of loneliness on physical and emotional health, in the context of the COVID-19 pandemic. Prospective, observational cohort. Community-dwelling participants. Older adults (n=238) enrolled in a longitudinal study. Interviews were completed July-December 2020. Loneliness was measured with the UCLA 3-item loneliness scale. Data including age, marriage, education, cognitive functioning, functional impairment, vision or hearing impairment, depression, anxiety, medical comorbidity, social network size, technology use, and activity engagement were collected. Health outcomes included self-rated health, and physical and mental composites from the 12-item Short Form Survey. Physical function was measured by a PROMIS-scaled composite score. Thirty-nine (16.4%) participants reported loneliness. Vulnerability factors for loneliness included age (RR=1.08, 95% CI 1.02-1.14); impairment with instrumental activities of daily living (RR=2.08, 95% CI 1.14-3.80); vision impairment (RR=2.09, 95% CI 1.10-3.97); depression (RR=1.34, 95% CI 1.25-1.43); and anxiety (RR=1.92, 95% CI 1.55-2.39). Significant resilience factors included high cognitive functioning (RR=0.88, 95% CI 0.83-0.94); large social network size (RR=0.92, 95% CI 0.88-0.96); technology use (RR=0.81, 95% CI 0.73-0.90); and social and physical activity engagement (RR=0.91, 95% CI 0.85-0.98). Interaction analyses showed that larger social network size moderated the effect of loneliness on physical function (protective interaction effect, RR=0.64, 95% CI 0.15-1.13, p <.01), and activity engagement moderated the effect of loneliness on mental health (protective interaction effect, RR=0.65, 95% CI 0.25-1.05, p <.001). Resilience factors may mitigate the adverse health outcomes associated with loneliness. Interventions to enhance resilience may help to diminish the detrimental effects of loneliness and hold great importance for vulnerable older adults.

Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.

Designing a Data Warehouse for Collected Data About User Activity in Social Networks Using Elasticsearch

Designing a Data Warehouse for Collected Data About User Activity in Social Networks Using Elasticsearch

고소득 미술작가의 사회적 네트워크와 거래성과 분석

This study is the first research to derive a social network activity index based on the career factors of the top 50 artists and analyze its relationship with transaction performance. The analysis results indicate that personal network centrality among artists has the most significant impact on transaction performance, followed by the importance of group network centrality. Additionally, the study categorizes artists into three clusters based on their careers: major and awards, education and number of solo exhibitions, and whether they studied abroad and social experience. These clusters demonstrate differences in transaction performance and social network characteristics. This study validates the importance of social networks that influence the transaction performance of visual artists, particularly by examining the network characteristics more closely by distinguishing between individual networks among artists and organization networks involving artists.

Preventive health behaviors among the middle-aged and elderly in China: Does social capital matter?

This study explores the status quo of preventive care use and social capital among middle-aged and elderly people (≥45 years old) in China, and employs a multi-level model to analyze whether social capital at different levels is associated with preventive care use. The data are derived from the 2018 China Health and Retirement Longitudinal Study (CHARLS), which includes 11,503 respondents and 450 communities. Preventive care use covers the utilization of routine physical examination services. Individual social capital is measured by the level of social network and social activities participation. Social network includes contacting with children or other people, for example, by phone, text message. Social activities participation is measured by the involvement in social activities, for example, playing mahjong, going to community club. Community social capital is evaluated by the richness of community facilities. Results reveals that the utilization of preventive care is 48.94% among middle-aged and elderly in China. The most used preventive service is routine blood test. The intra-class correlation (ICC) coefficient indicates that preventive health behaviors of the respondents are clustered at communities where they live. Multi-level regression shows that influence of social network is not significant to preventive care use (p ＞ 0.05). Community facilities and individual social activities participation are significantly associated with preventive care use (p < 0.05). The association between social capital and preventive care use could be considered as an important factor when making policies to promote preventive care use.

Association Between Sensory Impairment and Dementia: The Roles of Social Network and Leisure Activity.

Evidence suggests that sensory impairment is linked to dementia; however, the role of social network and leisure activity in this relationship is unclear. Examine the association of hearing and visual impairment with dementia, and whether a rich social network and leisure activity moderates this association. Dementia-free older adults from the Swedish National Study on Aging and Care in Kungsholmen (n = 2,579) were followed up for up for a median of 10 years (interquartile range = 6). Visual impairment was assessed with a reading acuity test, and hearing impairment was ascertained via self-report and medical records. Dementia was diagnosed following international criteria. Data on social network and leisure activity was collected via self-report. Hazard ratios (HRs) of dementia risk were derived from Cox regression models. Dual impairment, but not single impairment in hearing and vision was associated with a higher risk of dementia (HR: 1.62, 95% CI: 1.16 to 2.27). Compared to participants with no sensory impairment and a moderate-to-rich social network, those with dual impairment and low social network or leisure activity had higher dementia risk (HR: 2.08, 95% CI: 1.43 to 3.22; HR: 2.08, 95% CI: 1.43 to 3.22, respectively), whereas participants with dual impairment with a moderate-to-rich social network or leisure activity did not have significantly higher dementia risk (HR; 1.42, 95% CI: 0.87 to 2.33; HR; 1.42, 95% CI: 0.87 to 2.33, respectively). A richer social network and participation in stimulating activities may mitigate the higher dementia risk in older adults with dual impairment in vision and hearing.