Information Network Research Articles

Features and Perspectives of the Market of Information and Communication Technologies’ Functioning in Data-Driven Society

The article is intended to define the conceptual prospects for the development of the information and communication technologies market in a data-driven society. According to the results of the study, it was established that the implementation of innovative technological startups for the reorganization of information and communication networks using modern switching equipment, characterized by high performance, remains a relevant and global task for the development of the target market. This will provide potential opportunities for providing a high level of service to several numbering zones at once and maintaining the availability of a wide range of information and communication services in the modern conditions of digitalization of society. It was determined that the sphere of communication and information occupies an important place in the economic and social revival of Ukraine. Broad access to significant amounts of information and database management ensures the efficiency of organization and management of state, production and non-production processes of economic activity, thereby expanding the possibilities of successful business, optimizing costs and ensuring a high level of economic and information security of the business entities. The information and telecommunications market were analyzed and the activity of participating operators was diagnosed. It has been studied that in a data-driven society, the success of achieving goals in the digital development of information and communication enterprises directly depends on global information influence. A significant reduction in the number of telecommunications operators and providers was analyzed. It was determined that small operators of the target market are not able to ensure the appropriate level of quality, availability and continuity of service, in connection with which a significant part of the target audience switches to the services of large providers. Based on the analysis of the main indicators of the development of the information and communication technologies market, the main promising directions of development based on the formation of income from a diverse pool of services are determined.

Design of an information-computer system for the automated modeling of systems for automatic orientation of production objects in the machine and instrument industries

An information-computer system has been developed for the automated modeling of systems for automatic orientation of production objects, which is one of the most important and complex creative flexible production systems of machine and instrument engineering. The proposed information-computer system for automated modeling of systems of automatic orientation of production objects is an effective tool for solving an important task of a scientific and applied nature. Its use makes it possible to increase the speed and efficiency of information processing and to make correct and well-founded decisions when determining the composition and method of organization of systems of automatic orientation of production objects. The structure of this information-computer system is a specific set of software and hardware and information and telecommunication tools and interactive functional modules. This structure reproduces a certain paradigm that conditions the integrity and integration of the information-computer system for automated modeling of systems for automatic orientation of production objects in flexible production systems. In addition, uniformity, extensibility, the possibility of modernization and changeability of software components, protection of information from unauthorized access and preservation of commercial secrets are ensured according to international criteria for evaluating the protection of the computer system. Neuro-fuzzy network information processing and computer vision algorithms have been implemented for automatic identification of production objects and orientation devices, which are components of automatic orientation systems of production objects. The developed information-computer system processes information in real time with high accuracy and speed

Design of an information-computer system for the automated modeling of systems for automatic orientation of production objects in the machine and instrument industries

An information-computer system has been developed for the automated modeling of systems for automatic orientation of production objects, which is one of the most important and complex creative flexible production systems of machine and instrument engineering. The proposed information-computer system for automated modeling of systems of automatic orientation of production objects is an effective tool for solving an important task of a scientific and applied nature. Its use makes it possible to increase the speed and efficiency of information processing and to make correct and well-founded decisions when determining the composition and method of organization of systems of automatic orientation of production objects. The structure of this information-computer system is a specific set of software and hardware and information and telecommunication tools and interactive functional modules. This structure reproduces a certain paradigm that conditions the integrity and integration of the information-computer system for automated modeling of systems for automatic orientation of production objects in flexible production systems. In addition, uniformity, extensibility, the possibility of modernization and changeability of software components, protection of information from unauthorized access and preservation of commercial secrets are ensured according to international criteria for evaluating the protection of the computer system. Neuro-fuzzy network information processing and computer vision algorithms have been implemented for automatic identification of production objects and orientation devices, which are components of automatic orientation systems of production objects. The developed information-computer system processes information in real time with high accuracy and speed

The Dorsal Part of the Anterior Tuberal Nucleus Responds to Auditory Stimulation in Zebrafish (Danio rerio).

The zebrafish, a widely used model in neurobiology, relies on hearing in aquatic environments. Unfortunately, its auditory pathways have mainly been studied in larvae. In this study, we examined the involvement of the anterior tuberal nucleus (AT) in auditory processing in adult zebrafish. Our tract-tracing experiments revealed that the dorsal subdivision of AT is strongly bidirectionally connected to the central nucleus of the torus semicircularis (TSc), a major auditory nucleus in fishes. Immunohistochemical visualization of the ribosomal protein S6 (pS6) phosphorylation to map neural activity in response to auditory stimulation substantiated this finding: the dorsal but not the ventral part of AT responded strongly to auditory stimulation. A similar response to auditory stimulation was present in the TSc but not in the nucleus isthmi, a visual region, which we used as a control for testing if the pS6 activation was specific to the auditory stimulation. We also measured the time course of pS6 phosphorylation, which was previously unreported in teleost fish. After auditory stimulation, we found that pS6 phosphorylation peaked between 100 and 130 min and returned to baseline levels after 190 min. This information will be valuable for the design of future pS6 experiments. Our results suggest an anatomical and functional subdivision of AT, where only the dorsal part connects to the auditory network and processes auditory information.

A Paradox of School Social Organization: Positive School Climate, Friendship Network Density, and Adolescent Violence.

Schools are often encouraged to foster a positive climate to reduce adolescent violence, but evidence on the effectiveness of this approach varies significantly. This study investigates the roots of this variation by testing alternative hypotheses about how positive school-level climate and school-level student friendship network density interact to shape adolescent violence perpetration. Research on informal social control and network closure suggests that the violence-reducing association of positive school climate will be enhanced among schools where students are more densely tied through their friendships. Research on youth conflict and subversion of control suggests the opposite. These hypotheses are tested with data from Waves I-II of the National Longitudinal Study of Adolescent to Adult Health (n = 11,771; 49% Female; Age mean = 15.04, SD = 1.60). Consistent with the conflict/subversion hypothesis, analyses indicate that the inverse association between positive school climate and adolescent violence is only evident among schools with a very low density of friendship ties. Strikingly, however, there is evidence that a more positive school climate is associated with increases in violence among youth attending schools with a high density of friendship ties. These findings suggest that efforts to reduce violence by fostering cohesion among youth in their schools and other social contexts can be undermined by youth network processes.

Network-based artificial intelligence approaches for advancing personalized psychiatry.

Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and across data types to increase our etiological understanding and advance personalized psychiatry. Network science offers a conceptual framework for exploring the often complex relationships across different levels of biological organization, from cellular mechanistic to brain-functional and phenotypic networks. Utilizing such network information effectively as part of artificial intelligence approaches is a promising route toward a more in-depth understanding of illness biology, the deciphering of patient heterogeneity, and the identification of signatures that may be sufficiently predictive to be clinically useful. Here, we present examples of how network information has been used as part of artificial intelligence within psychiatry and beyond and outline future perspectives on how personalized psychiatry approaches may profit from a closer integration of psychiatric research, artificial intelligence development, and network science.

DDI Prediction with Heterogeneous Information Network - Meta-Path Based Approach.

Drug-drug interaction (DDI) indicates where a particular drug's desired course of action is modified when taken with other drug (s). DDIs may hamper, enhance, or reduce the expected effect of either drug or, in the worst possible scenario, cause an adverse side effect. While it is crucial to identify drug-drug interactions, it is quite impossible to detect all possible DDIs for a new drug during the clinical trial. Therefore, many computational methods are proposed for this task. This paper presents a novel method based on a heterogeneous information network (HIN), which consists of drugs and other biomedical entities like proteins, pathways, and side effects. Afterward, we extract the rich semantic relationships among these entities using different meta-path-based topological features and facilitate DDI prediction. In addition, we present a heterogeneous graph attention network-based end-to-end model for DDI prediction in the heterogeneous graph. Experimental results show that our proposed method accurately predicts DDIs and outperforms the baselines significantly.

Spillover effects in transit networks: A parameterized weight matrix spatial lagged approach

Public Transit (PT) systems aim to provide social, economic, and environmental benefits to modern cities offering reliable transportation service to users and ultimately reducing the problems related to traffic externalities due to car dependency. With the growing trend of urbanization and the associated phenomenon of cities’ sprawl being increasingly evident, significant attention should be given to understanding PT systems’ performance and then improving their efficiency. Considering the spatial characteristics of the PT system's performance promotes the environmentally friendly transport “character” that every city endeavors. This paper aims to incorporate the spatial spillover effects of a realistic PT system by augmenting information about the service network along with socio-economic variables, in a spatial demand-supply econometric framework. In detail, geographically separated demand and supply information on bus stops and lines was analyzed by a spatial econometric model, namely, the Spatial Lagged X (SLX) model which may be formatted so that can soundly handle, spatial and –in particular- network data like those coming from the General Transit Feed Specification (GTFS) protocol widely used in PT systems. The novelty and the importance of the proposed model rely on the ability to introduce transit network structure within the framework of spatial econometric regression, fostering the explanatory statistical analysis over networked information. The developed modeling approach was applied to the urban and rural PT system of Nicosia (Cyprus), incorporating the spatial spillover effects of the system over more than 1,500 bus stops, 40 lines, and 252 different but spatially connected postcode areas. The results of the SLX model were compared with other demand models of this form typically used, namely the Ordinary Least Square model and standard Spatial Autoregressive models, providing solid evidence of the benefits of incorporating the network structure in spatial demand modeling, giving valuable input for further planning purposes.

Development of an intrusion detection system using mayfly feature selection and artificial neural network algorithms

Protecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of intrusion detection. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study developed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifiers. The model was also tested without a mayfly algorithm. The model's efficacy was determined through a comparison of its accuracy, specificity, precision, sensitivity, and F1 score. The experimental outcomes revealed that the proposed model is more efficient than existing models based on the performance evaluation and the CIC-IDS 2017 dataset employed in this research. Accuracy scores of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. In comparison to existing models, the proposed model yielded better results in terms of accuracy, sensitivity, specificity, and F1-score metrics. The model's sturdiness can be attributed to the use of mayfly techniques, which harness the strength in PSO, GA and FA for selecting optimal feature subsets. The results of this research provide a reliable dimensionality reduction model that may be used in the field of computer networks for intrusion detection and enhancement of security in computer networking environments.

Encoding temporal information in deep convolution neural network.

A recent development in deep learning techniques has attracted attention to the decoding and classification of electroencephalogram (EEG) signals. Despite several efforts to utilize different features in EEG signals, a significant research challenge is using time-dependent features in combination with local and global features. Several attempts have been made to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information. These features are usually either handcrafted features, such as power ratios, or splitting data into smaller-sized windows related to specific properties, such as a peak at 300 ms. However, these approaches partially solve the problem but simultaneously hinder CNNs' capability to learn from unknown information that might be present in the data. Other approaches, like recurrent neural networks, are very suitable for learning time-dependent information from EEG signals in the presence of unrelated sequential data. To solve this, we have proposed an encoding kernel (EnK), a novel time-encoding approach, which uniquely introduces time decomposition information during the vertical convolution operation in CNNs. The encoded information lets CNNs learn time-dependent features in addition to local and global features. We performed extensive experiments on several EEG data sets-physical human-robot collaborations, P300 visual-evoked potentials, motor imagery, movement-related cortical potentials, and the Dataset for Emotion Analysis Using Physiological Signals. The EnK outperforms the state of the art with an up to 6.5% reduction in mean squared error (MSE) and a 9.5% improvement in F1-scores compared to the average for all data sets together compared to base models. These results support our approach and show a high potential to improve the performance of physiological and non-physiological data. Moreover, the EnK can be applied to virtually any deep learning architecture with minimal effort.

Factors affecting parental role adaptation in parents of preterm infants after discharge: a cross-sectional study.

Parenting a preterm infant can be incredibly challenging and stressful, particularly in the first year after discharge. Desirable parental role adaptation leads to appropriate parenting behaviors and parent-infant interaction, which are essential to child health and development. To investigate the level of parental role adaptation and its influencing factors among parents of preterm infants in the first year after hospital discharge according to Belsky's parenting process model among parents of preterm infants in the first year after hospital discharge. A cross-sectional study design was adopted using convenience sampling. Data were collected using the Parental Role Adaptation Scale (PRAS) in parents with preterm infants, the Perceived Social Support Scale (PSSS), the Coping Adaptation Processing Scale (CAPS-15), and a sociodemographic questionnaire. Descriptive statistics, non-parametric tests, Spearman correlation analyses, and multivariate linear regression were used to analyze the data. In total, 300 Chinese parents were included in the analysis. In the multivariate analysis, first-time parent (p = 0.003), master's degree and above (p = 0.042), coping adaptation processing (p = 0.000), residence location (towns: p = 0.019, city: p = 0.028), monthly family income (6000-10,000: p = 0.000, >10,000: p = 0.000), and perceived social support (p = 0.001) were all significant predictors of parental role adaptation and collectively accounted for 56.8% of the variation in parental role adaptation of parents with preterm infants (F = 16.473, p < 0.001). Coping adaptation processing mediated the relationship between perceived social support and parental role adaptation (95% bootstrap CI = 0.022, 0.130). Chinese parents of preterm infants experience a moderate level of parental role adaptation when their child is discharged from the hospital to home. Parents who are not first-time parents, have master's degrees or above, live in towns or cities, have higher coping and adaptation abilities, have high monthly family income, and greater perceived social support have a higher level of parental role adaptation. Healthcare providers should pay more attention to parents with low socioeconomic status and encourage them to improve their coping and adaptation abilities and to utilize their formal and informal social support networks.

Toward Few-Label Vertical Federated Learning

Federated Learning (FL) provides a novel paradigm for privacy-preserving machine learning, enabling multiple clients to collaborate on model training without sharing private data. To handle multi-source heterogeneous data, Vertical Federated Learning (VFL) has been extensively investigated. However, in the context of VFL, the label information tends to be kept in one authoritative client and is very limited. This poses two challenges for model training in the VFL scenario. On the one hand, a small number of labels cannot guarantee to train a well VFL model with informative network parameters, resulting in unclear boundaries for classification decisions. On the other hand, the large amount of unlabeled data is dominant and should not be discounted, and it is worthwhile to focus on how to leverage them to improve representation modeling capabilities. To address the preceding two challenges, we first introduce supervised contrastive loss to enhance the intra-class aggregation and inter-class estrangement, which is to deeply explore label information and improve the effectiveness of downstream classification tasks. Then, for unlabeled data, we introduce a pseudo-label-guided consistency mechanism to induce the classification results coherent across clients, which allows the representations learned by local networks to absorb the knowledge from other clients, and alleviates the disagreement between different clients for classification tasks. We conduct sufficient experiments on four commonly used datasets, and the experimental results demonstrate that our method is superior to the state-of-the-art methods, especially in the low-label rate scenario, and the improvement becomes more significant.

DIPG-71. XPEDITE: A STUDY TO EXPEDITE DIPG AND DMG RESEARCH

Abstract BACKGROUND Diffuse midline glioma (DMG), particularly the subtype known as diffuse intrinsic pontine glioma (DIPG), poses a significant challenge due to its aggressive nature and low survival rates, especially in children. Despite ongoing research and clinical trials, the survival rate remains alarmingly low. The scarcity of cases and siloed nature of current registry efforts have hindered the establishment of a reliable natural disease history for benchmarking studies and led to the need for randomization in clinical trials. METHODS xPedite (NCT06140719) is a fully virtual, patient-driven, nationwide, real-time observational study to collect, annotate, standardize, and report the critical data elements of DMG and DIPG in a regulatory-compliant framework. Patients of any age can join via electronic consent, with recruitment supported by provider referrals and patient advocacy organizations DDRFA and My DIPG Navigator. Medical records are accessed from institutions, sequencing vendors, and health information networks. Notably, data elements are annotated comprehensively and longitudinally in a 21 CFR Part 11-compliant clinical database by xCures remote study staff, eliminating the need for data entry by treating site staff or physicians. Data collected include anti-cancer interventions, non-cancer medications, biomarker results, radiological endpoints, steroid use, vitals, demographics, raw DICOM images, and genomics. In addition to clinical data, patient- and caregiver-reported outcome questionnaires (PROs) and video assessments of mobility and neurological status are collected for clinician review. The objective of the study is to collect these clinical, imaging, biomarker, and assessment data in a manner suitable to provide to health authorities to reduce randomization in future trials. In a commitment to scientific understanding and collaborative research, the data collected through the xPedite study is openly available to academic, government, and non-profit researchers. As of January 2024, 182 patients were enrolled in xPedite. Real-time enrollment and data statistics are available at https://xcures.com/dmgdashboard/.

Effects of Heart Rate Variability Biofeedback Training on Anxiety Reduction and Brain Activity: a Randomized Active-Controlled Study Using EEG.

Heart rate variability biofeedback (HRVBF) is a promising anxiety-reducing intervention that increases vagally-mediated heart rate variability (vmHRV) through slow-paced breathing and feedback of heart rhythm. Several studies have reported the anxiety-reducing effects of HRVBF; however, some studies have reported such training as ineffective. Furthermore, the effects of training and underlying brain activity changes remain unclear. This study examined the anxiety-reducing effects of HRVBF training and related brain activity changes by randomly assigning participants, employing an active control group, and measuring anxiety-related attentional bias using the emotional Stroop task and electroencephalography (EEG). Fifty-five healthy students with anxiety were randomly assigned to the HRVBF or control groups, and 21 in the HRVBF group and 19 in the control group were included in the analysis. Both groups performed 10 training sessions of 20min each within 3 weeks. They were assessed using resting vmHRV, event-related potential (ERP), time-frequency EEG, attentional bias, and the State-Trait Anxiety Inventory-JYZ (STAI-JYZ) before and after training. The results demonstrated increased resting vmHRV in the HRVBF group compared to the control group after training. However, no differences were observed in ERP, time-frequency EEG, attentional bias, and STAI-JYZ. Participants with higher pre-training resting vmHRV achieved higher heart rhythm coherence in HRVBF training and had reduced attentional bias. This study suggests that individuals with higher resting vmHRV are more likely to be proficient in HRVBF training and benefit from its anxiety-reducing effects. The findings contribute to participant selection to benefit from HRVBF training and modification of the training protocols for non-responders.Clinical trial registrationOrganization: University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR), JapanRegistration number: UMIN000047096Registration date: March 6, 2022.

The combined effect of leucine-enriched essential amino acid supplements and locomotion training on physical functions and quality of life in hemodialysis patients

BackgroundLocomotive syndrome (LS) is a condition of reduced mobility (locomotive organ impairment) that is associated with reduced quality of life (QOL) in patients undergoing hemodialysis (HD), and effective interventions for patients with LS undergoing HD are unclear. We conducted a clinical trial to assess the effects of exercise and oral nutritional supplementation (ONS) on QOL in this cohort.MethodsThis study was an open-label, randomized controlled trial. The participants were patients with LS undergoing HD. The intervention period was 3 months. The primary outcome was a change in the physical component summary (PCS) of QOL, and the secondary outcomes were changes in other QOL scores, physical function, body composition, and nutritional status assessed by using the geriatric nutritional risk index. Participants were divided into the locomotion training (LT) and LT + ONS groups. Both groups were instructed on LT that comprised one-leg standing and squats to be carried out four or more times per week. The ONS consisted of 3 g leucin-rich essential amino acids and 800 IU vitamin D per pack, and the participants in the LT + ONS group received one pack of the supplement per day.ResultsIn total, 40 individuals undergoing HD were included in the study; 3 patients in the LT + ONS group were excluded from the analysis because of hospitalization and incomplete assessment. The median age was 73 years (interquartile range: 62–80 years), 23 participants were men (62%), and the duration of dialysis treatment was 6 years (interquartile range: 3–16 years). The change in PCS of the LT + ONS group did not differ from that in the LT group [LT + ONS: −1.6 (−5.3, 6.8) versus LT: −0.1 (−5.3, 6.2), p = 0.94]. In contrast, the LT + ONS group showed maintenance in mental health (MH) and improvement of the two-step value.ConclusionsThe LT + ONS group did not show an effect on PCS in QOL compared with only LT in patients with LS undergoing HD. However, maintenance of MH and improvement of two-step value were confirmed in the LT + ONS group. Future research is need to confirm whether leucine-rich essential amino acids and vitamin D help mental health and physical function in patients with LS undergoing HD with larger sample sizes and long-term interventions.Clinical trial registration University Hospital Medical Information Network Center (UMIN 000032457).

Cross-Social-Network User Identification Based on Bidirectional GCN and MNF-UI Models

Due to the distinct functionalities of various social network platforms, users often register accounts on different platforms, posing significant challenges for unified user management. However, current multi-social-network user identification algorithms heavily rely on user attributes and cannot perform user identification across multiple social networks. To address these issues, this paper proposes two identity recognition models. The first model is a cross-social-network user identification model based on bidirectional GCN. It calculates user intimacy using the Jaccard similarity coefficient and constructs an adjacency matrix to accurately represent user relationships in the social network. It then extracts cross-social-network user information to accomplish user identification tasks. The second model is the multi-network feature user identification (MNF-UI) model, which introduces the concept of network feature vectors. It effectively maps the structural features of different social networks and performs user identification based on the common features of seed nodes in the cross-network environment. Experimental results demonstrate that the bidirectional GCN model significantly outperforms baseline algorithms in cross-social-network user identification tasks. The MNF-UI (multi-network feature user identification) model can operate in situations with two or more networks with inconsistent structures, resulting in improved identification accuracy. These two user identification algorithms provide technical and theoretical support for in-depth research on social network information integration and network security maintenance.

Application of Artificial Intelligence Technology in Vulnerability Analysis of Intelligent Ship Network

The improvement in transportation efficiency, security, safety, and environmental effects may be possible due to the impending advent of autonomous ships. Automatic situational awareness, risk detection, and intelligent decision-making are the key features of the intelligent ship network, differentiating it from conventional ships. There is an immediate need to implement a system for marine information management and network security due to the growing importance of this field, which poses a risk to national and societal stability due to factors, such as the diversity and complexity of marine information types, the challenges associated with data collection, and other similar factors. By recognizing different vulnerabilities and through research cases of the ship systems and Artificial Intelligence (AI) technologies, this paper presents Adaptive Fuzzy Logic-assisted Vulnerability Analysis of Intelligent Ship Networks (AFL-VA-ISN) in various cyberattack scenarios for autonomous ship intrusion detection and information management. Fuzzy logic has been combined with AI, providing a framework for handling uncertainty and imprecision in intelligent ship networks and effective decision-making. This work presents a method for detecting anomalies in risk data based on the collaborative control structure of the Ship Information System. Maintaining the network security of intelligent ships is the primary focus of this research, which mainly employed multi-sensor nodes to evaluate data containing information about malicious attacks and placed self-execution protection organize generating nodes into place to intercept and protect against attacks. The experimental outcomes demonstrate that the suggested AFL-VA-ISN model increases the data transmission rate by 99.2%, attack detection rate by 98.5%, risk assessment rate by 97.5%, and access control rate of 96.3%, and reduces the network latency rate of 11.4% compared to other existing models.

Effect of time pressure on informal advice relations across organizational units: Evidence from a study of collaboration within a Formula One racing team

Informal advice relations across units in an organization are beneficial for knowledge sharing and problem-solving. Prior research suggests that despite their benefits, there are costs to informal advice relations across units. However, the mechanisms by which these costs are mitigated remain unclear. We theorize that this lack of clarity is because work factors have not been sufficiently considered. We examine one such work factor, time pressure, and develop a cost-based explanation for how time pressure influences cross-unit advice relationships. We investigate two time-pressure levels. In the first, work is conducted under lower time pressure, and there is less likelihood of a negative outcome. In the second, work is conducted under higher time-pressure conditions, and there is a greater likelihood of a negative outcome. We theorize that under lower time-pressure conditions, the costs of advice relations across units are mitigated by reciprocal advice relationships. However, under higher time pressure, the cost of informal advice relations across units is higher owing to the need for quick coordination of advice, and these costs are mitigated by reciprocal advice relationships in conjunction with cross-unit formal workflow relationships. To test our hypotheses, we examine the informal advice network and formal workflow network in lower and higher time-pressure conditions among 118 members of the Information Technology and Systems division of a Formula One racing team. Our results indicate that under lower time-pressure conditions, reciprocal advice ties are sufficient to overcome costs. However, under higher time-pressure conditions, cross-unit advice ties are facilitated by reciprocal advice ties embedded in the workflow ties between units. Thus, our findings have implications for how knowledge is managed and how problems are solved in organizations.

Peran Media Sosial dalam Meningkatkan Partisipasi Politik Masyarakat di Desa Candirejo Kecamatan Biru-Biru Kabupaten Deli Serdang

This research discusses the role of social media in increasing community political participation in Candirejo village, Biru Biru subdistrict, Deli Serdang district. Social media is a potential tool for the government to shape public opinion and influence political participation. In the context of a democratic country like Indonesia, public opinion has a crucial role. because it is the basis for government functions, this research uses a qualitative descriptive method, the research stage is carried out by collecting data, interviews, observations and analysis, the results of the research show that social media plays a vital role in political participation and the formation of public opinion in the digital era, with an impact good and bad. Nevertheless, the government needs to monitor political activities in the modern social media era. Social media is not only a communication platform, but also a place for information seeking, discussion and networking. This research aims to find out how social media shapes political participation in society using a qualitative descriptive approach. The research results show that social media has a significant influence in engaging, connecting, and mobilizing political participation, although the receipt of political information via social media may vary between individuals.

Understanding Twitter Usage through Linked Data: An Analysis of Motivations and Online Behavior

Introduction &amp; BackgroundUses and gratification (U&amp;G) theory posits individuals’ engagement with social media is a deliberate effort to fulfill various needs, like information seeking, entertainment, and networking. However, prior studies predominantly addressed whether individuals use social media to satisfy their needs, leaving a gap in understanding how individuals behave online to satisfy needs. This study fills this gap by merging survey responses with actual Twitter activity, to investigate how individuals behave online to satisfy distinctive motivations, including (a) self-expression, (b) seeking entertainment, (c) business and working, (d) staying informed with news, and (e) networking. We also investigated how these online behaviors vary among individuals with different demographic features, including socio-economic classes, gender, and age. Objectives &amp; ApproachOur research addressed questions by linking survey responses with actual Twitter activities within the U.K. Participants were asked to provide survey responses surrounding age, gender, socio-economic class, and motivations for using social media. They were also queried about the existence of Twitter account, willingness to disclose Twitter username, and, if agreeable, the username itself. The survey continued until a total of 2,195 individuals shared Twitter handles. Following the removal of accounts that were either suspended or nonexistent, the study proceeded with a final count of 1,915. We collected each user’s Twitter metadata with Twitter API, including tweet count, follower count, following count, and bio information, and linked each user’s metadata with survey responses. To ensure respondents’ anonymity, survey, Twitter and linked data are stored separately, and can only be accessed by designated researcher. Relevance to Digital FootprintsThe study's approach of linking survey responses with actual Twitter activity offers a detailed insight into the digital footprints left by users as they engage with social media to satisfy their diverse needs. By analyzing the behaviors associated with motivations, this research illuminates the specific ways individuals curate their digital presence. ResultsRegression analysis indicated that individuals motivated by self-expression tend to tweet (b = .28, SE = .06, p &lt; .001), follow account (b = .38, SE = .06, p &lt; .001), gain followers (b = .13, SE = .06, p = .035), and post bio details (b = .89, SE = .13, p &lt; .001). Work and business motivation leads to post bio information (b = .38, SE = .15, p = .012), while networking leads to follow more accounts (b = .28, SE = .06, p &lt; .001). Social-economic class moderated associations between networking motivation and tweet count (b = -.25, SE = .09, p = .004), and between self-expression and tweet count (b = .20, SE = .08, p = .009). For individuals with higher socio-economic, self-expression has a higher effect on tweet count, whereas networking motivation has a less effect on tweet count. Additionally, we found gender moderated the association between self-expression and tweet count (b = .25, SE = .12, p = .04) and between keeping updated with news and tweet count (b = .11, SE = .05, p = .03). Conclusions &amp; ImplicationsThese findings offer a nuanced understanding of social media usage, highlighting how different motivations influence specific online behaviors. The novel approach of linking surveys with actual social media activity provides a more accurate representation of user behavior, contributing insights for academic and practical social media strategy and design.