In conditions of impetuous development of informational communicative technologies and intensification of need in remote medical services experiment on formation of telemedicine consultations acquires special actuality. The study is targeted to developing and testing experimental model of telemedicine reception of patients of otorhinolaryngology profile under out-patient conditions. The experiment consisted of two-stage examination (simulation of telemedicine consultation followed by in-person examination) and involved 10 qualified otorhinolaryngologists and 370 patients of municipal policlinic. The analysis of study results permitted to establish that preliminary remote diagnosis coincides with final face-to-face examination in 74.05% of cases. The study results demonstrated that telemedicine consultations in out-patient conditions can be an effective tool for diagnostics and treatment prescription in case of uncomplicated diseases of upper respiratory tract. In 88.65% of cases, treatment recommendations provided at first stage corresponded to ones prescribed after complete examination. The main limitation of telemedicine reception occurred impossibility to perform full-value physical examination.
Serina Chang, Alicja Chaszczewicz, Emma Wang
et al.
Generating social networks is essential for many applications, such as epidemic modeling and social simulations. The emergence of generative AI, especially large language models (LLMs), offers new possibilities for social network generation: LLMs can generate networks without additional training or need to define network parameters, and users can flexibly define individuals in the network using natural language. However, this potential raises two critical questions: 1) are the social networks generated by LLMs realistic, and 2) what are risks of bias, given the importance of demographics in forming social ties? To answer these questions, we develop three prompting methods for network generation and compare the generated networks to a suite of real social networks. We find that more realistic networks are generated with "local" methods, where the LLM constructs relations for one persona at a time, compared to "global" methods that construct the entire network at once. We also find that the generated networks match real networks on many characteristics, including density, clustering, connectivity, and degree distribution. However, we find that LLMs emphasize political homophily over all other types of homophily and significantly overestimate political homophily compared to real social networks.
Garbage is currently still a problem for the community where there is a lack of knowledge of waste management properly. The waste bank is present as a solution to assist the government in reducing the accumulation of waste and helping to improve the economy of housewives, especially residents affected by the COVID-19 pandemic. Bank Sampah Teratai (BST) is one of the social group organizations engaged in environmental economics, especially in managing household waste for residents of RW. 05 Pinang Griya Permai, Pinang - Tangerang. Bank Sampah Teratai located in Pinang Griya Permai Tangerang is a partner in this activity. Observation results show that Bank Sampah Teratai does not yet have adequate knowledge in waste management, both organic and inorganic. Based on the above problems, a community partnership program was formed with the academic community (lecturers and students) of Universitas Budi Luhur to help solve problems at the Bank Sampah Teratai. Based on these conditions, training on organic and inorganic waste management was carried out for residents of the Pinang Griya Permai housing. This training provides understanding, and appropriate skills, which help residents to carry out organic and inorganic waste management. Based on the results of the questionnaire after the training was carried out, 100% of participants stated that the material presented was in accordance with their needs, and the material presented was also considered good by 100% of the participants. The long-term result of this training is an increase in the understanding and skills of the residents to manage organic and inorganic waste. This training has been a useful contribution to improving skills for members of the Bank Sampah Teratai in particular, and skills for residents of Pinang Griya Permai housing in general.
Social history and conditions. Social problems. Social reform
En este trabajo se analizarán la Carta de Jamaica de Simón Bolívar; Nuestra América de José Martí; El problema de las razas en América Latina, la unidad de la América indoespañola, Punto de vista antimperialista, ¿Existe un pensamiento hispanoamericano? de José Carlos Mariátegui; La raza cósmica de José Vasconcelos; Casa grande y Senzela de Gilberto Freyre; Contrapunteo cubano del tabaco y el azúcar de Fernando Ortiz; Las Américas y la Civilización de Darcy Ribeiro; Nuestra América y el Occidente de Roberto Fernández Retamar; y Las Venas Abiertas de América Latina de Eduardo Galeano, con la intención de identificar los encuentros y desencuentros en los planteamientos de estos autores latinoamericanistas.
Social history and conditions. Social problems. Social reform, Social sciences (General)
Historical Background and the Emergence of New Bibliographic Units in the Context of the Contemporary Political Moment Demand a Reevaluation of Previous Interpretations Related to Events in the Territory of the Former Socialist Yugoslavia. This paper focuses on the Historical Agreement, also known as the Zulfikarpašić-Milošević Agreement, initiated by Muslims (Bosniaks). The agreement was intended as a peace and political initiative but came late in the context of the war in Croatia and the policy of regionalization pursued by the Serbian side in Bosnia and Herzegovina. After leaving the Party of Democratic Action (SDA), Adil Zulfikarpašić founded the Muslim Bosniak Organization (MBO), with the support of academician Muhamed Filipović. Dissatisfied with the policies of the SDA, Zulfikarpašić and Filipović sought to address the crisis through a different approach. In the case of the MBO, this approach involved historical reconciliation with the Serbs. While the leaders of the MBO structured this agreement as a peace and political initiative, its implementation was not possible due to the opposing state-legal concepts from the Bosniak (Muslim) side. The concept of a union of free states, central to the MBO's agreement, did not receive support from the Serbian side. In such a constellation of relationships, Yugoslavia could continue to function only as a federal state, as it best served Serbian state interests. The fundamental aim of this work is to shed light on the events preceding the agreement, what the agreement entailed, and why it ultimately failed. The introductory section of the paper analyzes Muslim (Bosniak)-Serbian historical reconciliation, which includes the period of Austro-Hungarian rule and the Kingdom of Yugoslavia when certain Muslim (Bosniak) politicians formed a specific type of alliance with the Serbs. The position of Muslims (Bosniaks) in the early 1990s significantly differed from that at the beginning of the 20th century. The paper dedicates a substantial portion of its pages to significant events in the Second Yugoslavia to provide a comprehensive synthesis. The 1974 Constitution, the Memorandum of the Serbian Academy of Sciences and Arts (SANU), the rise of Milošević, the abolition of autonomy for provinces in Serbia, and the republic-level elections were all events that preceded the agreement. Special attention in the paper is given to the participants of the agreement as significant political protagonists of that period, as well as the political parties whose members were part of the agreement and the architects of the policies of that era. Adil beg Zulfikarpašić undeniably played a central role in the efforts to reach a historical agreement between Muslims (Bosniaks) and Serbs. After World War II, Zulfikarpašić went into exile, where he launched the Bosanski pogledi magazine in the early 1960s. During his time in exile, he operated from various political-ideological positions, later emerging as one of the ideologues of Bosniak identity. In 1963, Zulfikarpašić participated in the founding of the Democratic Alternative, a movement in which Bosniak, Croatian, Serbian, and Slovenian intellectuals advocated for the democratization of Yugoslavia and the concept of free states within Yugoslavia. The foundation of this movement was a departure from Yugoslavia, i.e., communism, and alignment with the Western bloc of countries that based their policies on the principles of capitalism and democracy. Zulfikarpašić remained unwaveringly convinced of the correctness of the political initiative for reconciliation with the Serbs, considering the agreement the best solution under the circumstances and the only alternative to the impending war. A. Zulfikarpašić, as the main architect of the agreement, left Bosnia and Herzegovina in September 1991, and shortly thereafter, his party colleague, academician Muhamed Filipović, terminated all processes related to the agreement.
Social history and conditions. Social problems. Social reform
Moral reasoning reflects how people acquire and apply moral rules in particular situations. With increasingly social interactions happening online, social media data provides an unprecedented opportunity to assess in-the-wild moral reasoning. We investigate the commonsense aspects of morality in ordinary matters empirically. To this end, we examine data from a Reddit subcommunity (i.e., a subreddit) where an author may describe their behavior in a situation to seek comments about whether that behavior was appropriate. Other users comment to provide judgments and reasoning. We focus on the novel problem of understanding the moral reasoning implicit in user comments about the propriety of an author's behavior. Especially, we explore associations between the common elements of the indicated reasoning and the extractable social factors. Our results suggest the reasoning depends on the author's gender and the topic of a post, such as when expressing anger emotion and using sensible words (e.g., f-ck, hell, and damn) in work-related situations. Moreover, we find that the commonly expressed semantics also depends on commenters' interests.
Environmental Social Governance (ESG) is a widely used metric that measures the sustainability of a company practices. Currently, ESG is determined using self-reported corporate filings, which allows companies to portray themselves in an artificially positive light. As a result, ESG evaluation is subjective and inconsistent across raters, giving executives mixed signals on what to improve. This project aims to create a data-driven ESG evaluation system that can provide better guidance and more systemized scores by incorporating social sentiment. Social sentiment allows for more balanced perspectives which directly highlight public opinion, helping companies create more focused and impactful initiatives. To build this, Python web scrapers were developed to collect data from Wikipedia, Twitter, LinkedIn, and Google News for the S&P 500 companies. Data was then cleaned and passed through NLP algorithms to obtain sentiment scores for ESG subcategories. Using these features, machine-learning algorithms were trained and calibrated to S&P Global ESG Ratings to test their predictive capabilities. The Random-Forest model was the strongest model with a mean absolute error of 13.4% and a correlation of 26.1% (p-value 0.0372), showing encouraging results. Overall, measuring ESG social sentiment across sub-categories can help executives focus efforts on areas people care about most. Furthermore, this data-driven methodology can provide ratings for companies without coverage, allowing more socially responsible firms to thrive.
Amar Khelloufi, Huansheng Ning, Abdenacer Naouri
et al.
The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices' SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item-item structures underlying the sparse multi-modal contents in SIoT environment. In this paper, we propose a latent-based SIoT recommendation system that learns item-item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-the-art SIoT recommendation methods and validated its efficacy at mining latent relationships from multi-modal features.
¿Se discrimina a las mujeres cuando acceden a los servicios sociales? Mediante una etnografía focalizada, este trabajo aborda esta cuestión analizando cómo las personas usuarias de servicios sociales se enfrentan a prejuicios de género, y describimos cómo los/as trabajadores/as sociales asimilan y reproducen estos constructos. Dado que el familiarismo es un rasgo definitorio de los estados de bienestar mediterráneos, es probable que aparezcan sesgos de género, evidenciados en las intervenciones directas con las trabajadoras sociales. Nos centramos en el caso de un centro de servicios sociales generales en el País Vasco, analizando 57 casos de hombres y mujeres vulnerables, prestando atención a las intervenciones sociales que reciben. El periodo de estudio coincide con un escenario de recesión económica en el que las políticas basadas en la austeridad han tenido un papel protagonista. Los resultados evidencian la ausencia de una perspectiva de género en un sistema que no se ha transformado lo suficiente para lograr la equidad de género. Se evidencia un sesgo de género en la intervención social en un número importante de los casos, ya que las intervenciones sociales reproducen la perpetuación de la división sexual del trabajo y no promueven mecanismos de corresponsabilidad.
Social history and conditions. Social problems. Social reform, Social sciences (General)
The aim of the study was to determine whether aspects of occupational burnout, i.e. psychophysical exhaustion, lack of involvement in relations with pupils, a sense of professional ineffectiveness, and disappointment are significantly related to the specificity of educational work and care in special education.
The objective of the study was also to determine the relationship between coping styles adopted by teachers in stressful situations, i.e. task-oriented, emotion-oriented, and avoidance-oriented coping, with the degree of intellectual disability of pupils adopted as a criterion.
The degree of intellectual disability of pupils under the teachers' care was adopted as a differentiating criterion in the analysis. The research was conducted among 77 teachers from the Special Training and Education Centre in Słupsk. In order to collect research material the authors used a diagnostic survey and the following research tools: The Coping Inventory for Stressful Situations (CISS) and Link Burnout Questionnaire (LBQ) by Massimo Santinello.
The analysis of the obtained results indicates the existence of a relationship between occupational burnout among special education teachers, and their pupils' degree of disability. A dependency was observed between the sense of burnout in assessing one's own competence (sense of ineffectiveness), and the pupil's degree of intellectual disability, where effectiveness subjectively assessed by teachers decreases as disorders in the level of pupils' intellectual development and social functioning increase.
The obtained results also indicate task-oriented coping style as dominating among the studied teachers. This strategy dominated in the group who worked with pupils with mild and profound intellectual disability.
Social history and conditions. Social problems. Social reform, Education
In light of the growing number of user privacy violations in centralized social networks, the need to define effective platforms for decentralized online social networks (DOSNs) is deeply felt. Interesting solutions have been proposed in the past, which own the necessary mechanisms to allow users keeping control over their personal information and setting the rules to regulate the access of other users. Unfortunately, the effectiveness of this type of solutions is severely reduced by the fact that different user communities with a shared interest could be disconnected/separated from each other. This translates into a reduced ability in effectively spreading data of common interest towards all interested users, as it currently happens in centralized social networks. In order to overcome the cited limitation, this paper proposes a disruptive approach, which exploits the availability of a new class of Internet of Things (IoT) devices with autonomous social behaviors and cognitive abilities. Such devices can be leveraged as friendship intermediaries between devices' owners who are connected to a DOSN platform and share the same interest. We will demonstrate that clear advantages can be achieved in terms of increased percentage of Interested Reachable Nodes (a specific measure of Delivery Ratio) in distributed social networks among humans, when enhanced with so called Mediator Objects adhering to the well-known social IoT (SIoT) paradigm.
In this study, I present a theoretical social learning model to investigate how confirmation bias affects opinions when agents exchange information over a social network. Hence, besides exchanging opinions with friends, agents observe a public sequence of potentially ambiguous signals and interpret it according to a rule that includes confirmation bias. First, this study shows that regardless of level of ambiguity both for people or networked society, only two types of opinions can be formed, and both are biased. However, one opinion type is less biased than the other depending on the state of the world. The size of both biases depends on the ambiguity level and relative magnitude of the state and confirmation biases. Hence, long-run learning is not attained even when people impartially interpret ambiguity. Finally, analytically confirming the probability of emergence of the less-biased consensus when people are connected and have different priors is difficult. Hence, I used simulations to analyze its determinants and found three main results: i) some network topologies are more conducive to consensus efficiency, ii) some degree of partisanship enhances consensus efficiency even under confirmation bias and iii) open-mindedness (i.e. when partisans agree to exchange opinions with opposing partisans) might inhibit efficiency in some cases.
Stephany Rajeh, Marinette Savonnet, Eric Leclercq
et al.
Identifying key nodes is crucial for accelerating or impeding dynamic spreading in a network. Community-aware centrality measures tackle this problem by exploiting the community structure of a network. Although there is a growing trend to design new community-aware centrality measures, there is no systematic investigation of the proposed measures' effectiveness. This study performs an extensive comparative evaluation of prominent community-aware centrality measures using the Susceptible-Infected-Recovered (SIR) model on real-world online social networks. Overall, results show that K-shell with Community and Community-based Centrality measures are the most accurate in identifying influential nodes under a single-spreader problem. Additionally, the epidemic transmission rate doesn't significantly affect the behavior of the community-aware centrality measures.
While social media offers freedom of self-expression, abusive language carry significant negative social impact. Driven by the importance of the issue, research in the automated detection of abusive language has witnessed growth and improvement. However, these detection models display a reliance on strongly indicative keywords, such as slurs and profanity. This means that they can falsely (1a) miss abuse without such keywords or (1b) flag non-abuse with such keywords, and that (2) they perform poorly on unseen data. Despite the recognition of these problems, gaps and inconsistencies remain in the literature. In this study, we analyse the impact of keywords from dataset construction to model behaviour in detail, with a focus on how models make mistakes on (1a) and (1b), and how (1a) and (1b) interact with (2). Through the analysis, we provide suggestions for future research to address all three problems.
Lucas Henrique Costa de Lima, Julio Reis, Philipe Melo
et al.
Despite the valuable social interactions that online media promote, these systems provide space for speech that would be potentially detrimental to different groups of people. The moderation of content imposed by many social media has motivated the emergence of a new social system for free speech named Gab, which lacks moderation of content. This article characterizes and compares moderated textual data from Twitter with a set of unmoderated data from Gab. In particular, we analyze distinguishing characteristics of moderated and unmoderated content in terms of linguistic features, evaluate hate speech and its different forms in both environments. Our work shows that unmoderated content presents different psycholinguistic features, more negative sentiment and higher toxicity. Our findings support that unmoderated environments may have proportionally more online hate speech. We hope our analysis and findings contribute to the debate about hate speech and benefit systems aiming at deploying hate speech detection approaches.
El trabajo se enfocará la corporalidad que los en la manera en que los líderes del movimiento fidencista, un culto espírita inspirado en el Niño Fidencio, transmiten a sus fieles conocimiento corporeizado a través de la canalización de espíritus regionales. Centrándome en las prácticas corporales de estos líderes, que se mueven y viajan transnacionalmente, y ayudada de un marco de análisis de fenomenología de las prácticas corporales, mostraré algunos ejes fidencistas clave para comprender su reproducción social en un contexto como el del norte de México.
Sociology (General), Social history and conditions. Social problems. Social reform
Hemank Lamba, Shashank Srikanth, Dheeraj Reddy Pailla
et al.
In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal crashes. Social media applications have enabled users to stay connected, however, the use of such applications while driving could have serious repercussions -- often leading the user to be distracted from the road and ending up in an accident. In the context of impression management, it has been discovered that individuals often take a risk (such as teens smoking cigarettes, indulging in narcotics, and participating in unsafe sex) to improve their social standing. Therefore, viewing the phenomena of posting distracted driving posts under the lens of self-presentation, it can be hypothesized that users often indulge in risk-taking behavior on social media to improve their impression among their peers. In this paper, we first try to understand the severity of such social-media-based distractions by analyzing the content posted on a popular social media site where the user is driving and is also simultaneously creating content. To this end, we build a deep learning classifier to identify publicly posted content on social media that involves the user driving. Furthermore, a framework proposed to understand factors behind voluntary risk-taking activity observes that younger individuals are more willing to perform such activities, and men (as opposed to women) are more inclined to take risks. Grounding our observations in this framework, we test these hypotheses on 173 cities across the world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content posting behavior changes due to varied demographics. We discover that the factors put forth by the framework are significant in estimating the extent of such behavior.
Online users generate tremendous amounts of data. To better serve users, it is required to share the user-related data among researchers, advertisers and application developers. Publishing such data would raise more concerns on user privacy. To encourage data sharing and mitigate user privacy concerns, a number of anonymization and de-anonymization algorithms have been developed to help protect privacy of users. This paper reviews my doctoral research on online users privacy specifically in social media. In particular, I propose a new adversarial attack specialized for social media data. I further provide a principled way to assess effectiveness of anonymizing different aspects of social media data. My work sheds light on new privacy risks in social media data due to innate heterogeneity of user-generated data.
User modeling is a very important task for making relevant suggestions of venues to the users. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. These scores model each user by focusing on the different types of information extracted from venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content.
Social media users generate tremendous amounts of data. To better serve users, it is required to share the user-related data among researchers, advertisers and application developers. Publishing such data would raise more concerns on user privacy. To encourage data sharing and mitigate user privacy concerns, a number of anonymization and de-anonymization algorithms have been developed to help protect privacy of social media users. In this work, we propose a new adversarial attack specialized for social media data. We further provide a principled way to assess effectiveness of anonymizing different aspects of social media data. Our work sheds light on new privacy risks in social media data due to innate heterogeneity of user-generated data which require striking balance between sharing user data and protecting user privacy.