Sameera S. Vithanage, Keith Ransom, Antonette Mendoza
et al.
Corrections given by ordinary social media users, also referred to as Social Correction have emerged as a viable intervention against misinformation as per the recent literature. However, little is known about how often users give disputing or endorsing comments and how reliable those comments are. An online experiment was conducted to investigate how users' credibility evaluations of social media posts and their confidence in those evaluations combined with online reputational concerns affect their commenting behaviour. The study found that participants exhibited a more conservative approach when giving disputing comments compared to endorsing ones. Nevertheless, participants were more discerning in their disputing comments than endorsing ones. These findings contribute to a better understanding of social correction on social media and highlight the factors influencing comment behaviour and reliability.
In rural China, the aging population faces unique challenges that affect their life satisfaction. These challenges are compounded by disparities in access to resources compared to urban areas, making it crucial to address these issues to ensure a dignified and fulfilling later life. Understanding and improving life satisfaction for this demographic is essential, not only for the well-being of the elderly but also for the overall social stability and economic sustainability of these communities. Using a rural sample from the Chinese Social Survey (CSS), this study investigates the interplay between social quality factors and life satisfaction among older adults in rural China. It focuses on the roles of socioeconomic security, social cohesion, social inclusion, and social empowerment in shaping the well-being of this demographic. It was found that the four conditional factors of social quality—socioeconomic security, social cohesion, social inclusion, and social empowerment—have different influences on life satisfaction among older adults in rural areas. The statistical model shows that the influence of absolute family income on social and economic security does not have statistical significance. In contrast, housing, pension insurance participation, and public safety perception positively affect life satisfaction among older adults in rural areas. Among the social cohesion factors, higher social morality, legal system evaluation, social identity, grassroots government, and interpersonal trust contribute to the life satisfaction of older adults in rural areas. Regarding social inclusion factors, good social tolerance, social equity, and perception of government public services can significantly improve life satisfaction among older adults in rural areas. For social empowerment factors, social participation helps expand the social support network of older adults in rural areas, enhancing their life satisfaction. Paths to improving the quality of rural society should be explored to improve relative poverty in rural areas. They should continue to be pursued to strengthen the old-age security system for older adults in rural areas. Further, the National Medium- and Long-Term Plan for Actively Responding to Population Aging should be the base for forming an elderly-friendly society. A good atmosphere promotes the participation of people, families, and society; supports social cohesion; enhances inclusion; and promotes social participation, improving life satisfaction.
Emojis, which encapsulate semantics beyond mere words or phrases, have become prevalent in social network communications. This has spurred increasing scholarly interest in exploring their attributes and functionalities. However, emoji-related research and application face two primary challenges. First, researchers typically rely on crowd-sourcing to annotate emojis in order to understand their sentiments, usage intentions, and semantic meanings. Second, subjective interpretations by users can often lead to misunderstandings of emojis and cause the communication barrier. Large Language Models (LLMs) have achieved significant success in various annotation tasks, with ChatGPT demonstrating expertise across multiple domains. In our study, we assess ChatGPT's effectiveness in handling previously annotated and downstream tasks. Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications. Our findings indicate that ChatGPT has extensive knowledge of emojis. It is adept at elucidating the meaning of emojis across various application scenarios and demonstrates the potential to replace human annotators in a range of tasks.
Moral values play a fundamental role in how we evaluate information, make decisions, and form judgements around important social issues. Controversial topics, including vaccination, abortion, racism, and sexual orientation, often elicit opinions and attitudes that are not solely based on evidence but rather reflect moral worldviews. Recent advances in Natural Language Processing (NLP) show that moral values can be gauged in human-generated textual content. Building on the Moral Foundations Theory (MFT), this paper introduces MoralBERT, a range of language representation models fine-tuned to capture moral sentiment in social discourse. We describe a framework for both aggregated and domain-adversarial training on multiple heterogeneous MFT human-annotated datasets sourced from Twitter (now X), Reddit, and Facebook that broaden textual content diversity in terms of social media audience interests, content presentation and style, and spreading patterns. We show that the proposed framework achieves an average F1 score that is between 11% and 32% higher than lexicon-based approaches, Word2Vec embeddings, and zero-shot classification with large language models such as GPT-4 for in-domain inference. Domain-adversarial training yields better out-of domain predictions than aggregate training while achieving comparable performance to zero-shot learning. Our approach contributes to annotation-free and effective morality learning, and provides useful insights towards a more comprehensive understanding of moral narratives in controversial social debates using NLP.
How can individual agents coordinate their actions to achieve a shared objective in distributed systems? This challenge spans economic, technical, and sociological domains, each confronting scalability, heterogeneity, and conflicts between individual and collective goals. In economic markets, a common currency facilitates coordination, raising the question of whether such mechanisms can be applied in other contexts. This paper explores this idea within social media platforms, where social support (likes, shares, comments) acts as a currency that shapes content production and sharing. We investigate two key questions: (1) Can social support serve as an effective coordination tool, and (2) What role do influencers play in content creation and dissemination? Our formal analysis shows that social support can coordinate user actions similarly to money in economic markets. Influencers serve dual roles, aggregating content and acting as information proxies, guiding content producers in large markets. While imperfections in information lead to a "price of influence" and suboptimal outcomes, this price diminishes as markets grow, improving social welfare. These insights provide a framework for understanding coordination in distributed environments, with applications in both sociological systems and multi-agent AI systems.
As the capabilities of Large Language Models (LLMs) emerge, they not only assist in accomplishing traditional tasks within more efficient paradigms but also stimulate the evolution of social bots. Researchers have begun exploring the implementation of LLMs as the driving core of social bots, enabling more efficient and user-friendly completion of tasks like profile completion, social behavior decision-making, and social content generation. However, there is currently a lack of systematic research on the behavioral characteristics of LLMs-driven social bots and their impact on social networks. We have curated data from Chirper, a Twitter-like social network populated by LLMs-driven social bots and embarked on an exploratory study. Our findings indicate that: (1) LLMs-driven social bots possess enhanced individual-level camouflage while exhibiting certain collective characteristics; (2) these bots have the ability to exert influence on online communities through toxic behaviors; (3) existing detection methods are applicable to the activity environment of LLMs-driven social bots but may be subject to certain limitations in effectiveness. Moreover, we have organized the data collected in our study into the Masquerade-23 dataset, which we have publicly released, thus addressing the data void in the subfield of LLMs-driven social bots behavior datasets. Our research outcomes provide primary insights for the research and governance of LLMs-driven social bots within the research community.
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin
et al.
We study social learning dynamics motivated by reviews on online platforms. The agents collectively follow a simple multi-armed bandit protocol, but each agent acts myopically, without regards to exploration. We allow the greedy (exploitation-only) algorithm, as well as a wide range of behavioral biases. Specifically, we allow myopic behaviors that are consistent with (parameterized) confidence intervals for the arms' expected rewards. We derive stark learning failures for any such behavior, and provide matching positive results. The learning-failure results extend to Bayesian agents and Bayesian bandit environments. In particular, we obtain general, quantitatively strong results on failure of the greedy bandit algorithm, both for ``frequentist" and ``Bayesian" versions. Failure results known previously are quantitatively weak, and either trivial or very specialized. Thus, we provide a theoretical foundation for designing non-trivial bandit algorithms, \ie algorithms that intentionally explore, which has been missing from the literature. Our general behavioral model can be interpreted as agents' optimism or pessimism. The matching positive results entail a maximal allowed amount of optimism. Moreover, we find that no amount of pessimism helps against the learning failures, whereas even a small-but-constant fraction of extreme optimists avoids the failures and leads to near-optimal regret rates.
Successful aging is achieved throughout the life course, and successful aging groups tend to have good psychosocial and physical conditions and are active in social activities. With increasing age, the mental health problems of older adults have become increasingly prominent, and the choice of pension mode is closely related to the mental health of older adults. Starting from the psychological level of the older adult, this paper used data from the 2018 Chinese Longitudinal Healthy Longevity Survey to study the impact of three pension methods on the mental health of older adults. The study found that, at present, there are three types of pension modes in China: living alone, family pension, and institutional care, and family pensions are still the mainstream pension mode. Older adults with deeper negative feelings are more inclined to family pensions than to live alone, but the spiritual comfort provided by family members does not improve the negative feelings of older adults. Institutional care deepens the negative feeling and reduces the positive feeling of older adults. In addition, retirement or pension and medical insurance, as life security in old age, can effectively reduce the negative feelings of old age and promote positive feelings. In view of the present situation of China’s pension mode and the psychological characteristics of the older adults, we should further build a perfect family pension security system, promote the personalized service construction of older adult care institutions, promote applicable aging renovation of existing residential areas, and encourage older adults to engage in healthy exercise.
The current period of social development is marked by institutional changes in the country's pension system, which last more than ten years and which have led to the creation of new pension institutions, the creation of a legal framework for future retirees. The need to reform pension insurance institutions in Ukraine is due to the demographic and economic problems of our pension system.In addition to economic and demographic factors outside the pension system, within the latter there are significant internal problems with maintaining the institution of early retirement, loss of insurance function and loss of earnings due to retirement due to retirement age and the mismatch between insurance rates associated with pension obligations.Measures to improve the pension system in order to achieve a socially acceptable level of pensions have yielded a number of positive results, but at the same time it is clear that the pension system in Ukraine still needs further restructuring, as reforms in this area do not have long-term financial reforms. Ensured stability and a balanced budget of the pension fund.Ongoing measures aimed at improving the pension system, achieving a socially acceptable level of pensions have made it possible to achieve a number of positive results, but at the same time it is clear that the pension system in Ukraine currently needs further transformations. whereas the reforms implemented in this area have not made it possible to achieve long-term financial stability and a balanced budget for the pension fund.There is no doubt that the emerging modern pension system should be adequate in accordance with the current conditions of socio-economic development of our country, as well as comply with international standards of pension provision, global trends in the development of pension institutions. The degree of effectiveness of a country's pension system can be determined on the basis of the high level of social security of pensioners provided by the pension institutions existing in that country.The degree of effectiveness of a country's pension system can be determined on the basis of the high level of social security of pensioners provided by the pension institutions existing in that country. At the same time, in modern society, older people, as shown by empirical studies, belong to those categories of the population who are less satisfied with their own lives and their quality than others; in addition, there has been a recent trend of a relative decline in the level of material security for pensioners.
AbstractThe Chinese social security system has been the subject of numerous publications, which have made policy developments more accessible to researchers and administrators from all countries. However, the steps introduced in response to growing demands for intervention by the authorities in favour of dependent persons have remained poorly documented in the international literature. The purpose of this article is to take stock of pilot experiments in this field since the beginning of the 13th Five‐Year Plan (2016–2020) with regard to their policy objective, operating mode and financing modalities.
This report gives a brief overview of the origin of social networks and their most popular manifestation in the modern era - the Online Social Networks (OSNs) or social media. It further discusses the positive and negative implications of OSNs on human society. The coupling of Data Science and social media (social media mining) is then put forward as a powerful tool to overcome the current challenges and pave the path for futuristic advancements
This research contributes to the security design of an advanced smart drone swarm network based on a variant of the Blockchain Governance Game (BGG), which is the theoretical game model to predict the moments of security actions before attacks, and the Strategic Alliance for Blockchain Governance Game (SABGG), which is one of the BGG variants which has been adapted to construct the best strategies to take preliminary actions based on strategic alliance for protecting smart drones in a blockchain-based swarm network. Smart drones are artificial intelligence (AI)-enabled drones which are capable of being operated autonomously without having any command center. Analytically tractable solutions from the SABGG allow us to estimate the moments of taking preliminary actions by delivering the optimal accountability of drones for preventing attacks. This advanced secured swarm network within AI-enabled drones is designed by adapting the SABGG model. This research helps users to develop a new network-architecture-level security of a smart drone swarm which is based on a decentralized network.
The US president, Donald Trump, has proposed eliminating the payroll tax to ease the financial pain faced by US people and businesses owing to the covid-19 outbreak. He also suggested help for the country’s cruise ship industry and airlines.1 The payroll tax, paid both by US employees and employers, provides funds for Social Security pensions, Medicare (health insurance for elderly people), and many other government programs. The tax brings in over a trillion dollars (£773bn; €883bn) a year and is an important part of the federal budget. New York, Massachusetts, and Connecticut have joined Washington state, California, Colorado, and Rhode Island in declaring states of emergency after cases of the virus increased. The president said that he had not been tested for covid-19, although he had attended meetings with two Congressional representatives …
People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management. To meet these time-critical needs, we present a weakly supervised approach for rapidly building high-quality classifiers that label each individual Twitter message with fine-grained event categories. Most importantly, we propose a novel method to create high-quality labeled data in a timely manner that automatically clusters tweets containing an event keyword and asks a domain expert to disambiguate event word senses and label clusters quickly. In addition, to process extremely noisy and often rather short user-generated messages, we enrich tweet representations using preceding context tweets and reply tweets in building event recognition classifiers. The evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2 person-hours of human supervision, the rapidly trained weakly supervised classifiers outperform supervised classifiers trained using more than ten thousand annotated tweets created in over 50 person-hours.
AbstractThis article uses a single male cohort microsimulation model to analyse the intra‐generational and distributional effects of a shift in Estonia from a defined benefit pay‐as‐you‐go (PAYG) pension system to a multi‐pillared system with a PAYG scheme with contribution‐based insurance components and a funded pension scheme. We contribute to the literature on microsimulation by showing how introducing contribution‐based insurance components and compulsory defined contribution (DC) schemes can increase pension inequality. Our results show that in the case of a high level of inequality in labour earnings and high long‐term unemployment rates, such as in Estonia, the introduction of a very strong link between contributions and future benefits leads to considerably higher inequality in pension incomes as measured by the Gini coefficient. Simulation results for Estonia suggest that inequality in old‐age pension incomes more than doubles when the reforms mature. In contrast, the inequality in replacement rates decreases.
Looking from a global perspective, the landscape of online social networks is highly fragmented. A large number of online social networks have appeared, which can provide users with various types of services. Generally, the information available in these online social networks is of diverse categories, which can be represented as heterogeneous social networks (HSN) formally. Meanwhile, in such an age of online social media, users usually participate in multiple online social networks simultaneously to enjoy more social networks services, who can act as bridges connecting different networks together. So multiple HSNs not only represent information in single network, but also fuse information from multiple networks. Formally, the online social networks sharing common users are named as the aligned social networks, and these shared users who act like anchors aligning the networks are called the anchor users. The heterogeneous information generated by users' social activities in the multiple aligned social networks provides social network practitioners and researchers with the opportunities to study individual user's social behaviors across multiple social platforms simultaneously. This paper presents a comprehensive survey about the latest research works on multiple aligned HSNs studies based on the broad learning setting, which covers 5 major research tasks, i.e., network alignment, link prediction, community detection, information diffusion and network embedding respectively.
Large-scale rumor spreading could pose severe social and economic damages. The emergence of online social networks along with the new media can even make rumor spreading more severe. Effective control of rumor spreading is of theoretical and practical significance. This paper takes the first step to understand how the blockchain technology can help limit the spread of rumors. Specifically, we develop a new paradigm for social networks embedded with the blockchain technology, which employs decentralized contracts to motivate trust networks as well as secure information exchange contract. We design a blockchain-based sequential algorithm which utilizes virtual information credits for each peer-to-peer information exchange. We validate the effectiveness of the blockchain-enabled social network on limiting the rumor spreading. Simulation results validate our algorithm design in avoiding rapid and intense rumor spreading, and motivate better mechanism design for trusted social networks.
Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Social Network Embedding framework (SNE), which learns representations for social actors (i.e., nodes) by preserving both the structural proximity and attribute proximity. While the structural proximity captures the global network structure, the attribute proximity accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches, SNE can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically, SNE significantly outperforms node2vec with an 8.2% relative improvement on the link prediction task, and a 12.7% gain on the node classification task.