Influence Of Use Research Articles

웹툰과 웹소설의 댓글 이용 동기에 관한 연구

The purpose of this study is to investigate the influence of webtoon and web novel users' motivation to use comments (reading) among web contents. As a part of this, we examined the effect of comment use on the relationship between satisfaction and influence, and the relationship between motivation and satisfaction, and examined whether there is a difference between platforms (webtoons and web novels). As a result of the analysis, it was composed of two motives for use (communication, leisure pursuit) and three motives for use of comments (fun to read comments, search for public opinion and information, and criticism) through factor analysis. Both communication motivation and leisure motivation were partially mediated by the relationship between communication motivation and leisure motivation -> fun to read comments -> satisfaction. On the other hand, when comparing the influence of the motivation for using comments between webtoons and web novels, it was analyzed that public opinion, information search and criticism had no mediating effect in common, and the difference was the motivation for reading comments.

Detection of opinion leaders: Static vs. dynamic evaluation in online learning communities

Opinion leaders play a critical role in maintaining the functioning of online communities. This study aims to detect opinion leaders in online learning communities by evaluating the influence of users within the community. We use Baidu Post Bar’s Python learning community as an example and employ the catastrophe progression method to statically evaluate the influence of users in three dimensions: user creativity, user posting quality, and user online interaction. Based on this, we introduce the dual-incentive control line to dynamically evaluate users' influence from 2016 to 2020 regarding speed change characteristics, thus scientifically detecting opinion leaders in online learning communities. Compared to the static evaluation method, the results show that our proposed dynamic evaluation method can more effectively reveal the dynamic development trend of users' influence, thus accurately detecting opinion leaders. Moreover, this “invisible” development trend is fully reflected in the setting of the dual-incentive control line.

Pemanfaatan Teknologi Dalam Peningkatan Kualitas Resusitasi Jantung Paru: A Literature Review

Basic life support (BLS) provides basic lifesaving measures after a heart attack and emphasizes what action must be taken. BLS focuses on cardiopulmonary resuscitation (CPR) and how it relates to saving lives after a heart attack. This method used literature review. Data was taken from scientific database on ScienceDirect, PubMed, EBSCOhost, dan Wiley Library Online . This study used criteria and keywords. Using tools and technology can guide consistency in depth, speed and recoil time American Heart Association guidelines CPR. However, this cannot be separated from the influence of several conditions, cost, user and safety factor. The assumption using tools and technology hoped that the consistency of depth, speed and recoil time can improve the quality of CPR and increase the ROSC (Return of Spontaneous Circulation) percentage as well as a good survival rate. Keywords: CPR Technology, high-quality CPR, Training CPR

Capturing Dynamic Interests of Similar Users for POI Recommendation Using Self-Attention Mechanism

The integration of location-based social networks and POI recommendation systems has the potential to enhance the urban experience by facilitating the exploration of new and relevant locales. The deployment of graph neural networks (GNNs) drives the development of POI recommendations, but this approach also brings with it the challenge of over-smoothing, where information propagation between nodes in the graph can lead to an excessive homogenization of the data. In prior works that utilized GNNs for POI recommendation, the bipartite graphs constructed from users and POIs as nodes failed to incorporate temporal dynamics, limiting the scope of the analysis to only spatial structure information. To circumvent this issue, the incorporation of a temporal component can be introduced during the aggregation process of graph convolution. In light of these considerations, the present study proposes a novel regionalized temporal GCN (RST-GCN) recommendation model that leverages self-attention mechanism to capture various levels of temporal information to better reflect the dynamic changes of time. By combining the graph’s spatial structure with geospatial features, similar users are distributed into distinct regional subgraphs, effectively avoiding the influence of non-similar users. The efficacy of the proposed model has been demonstrated through empirical evaluations conducted on two real-world datasets.

The Influence of Personality Traits on User Interaction with Recommendation Interfaces

Users’ personality traits can take an active role in affecting their behavior when they interact with a computer interface. However, in the area of recommender systems (RS) , though personality-based RS has been extensively studied, most works focus on algorithm design, with little attention paid to studying whether and how the personality may influence users’ interaction with the recommendation interface. In this manuscript, we report the results of a user study (with 108 participants) that not only measured the influence of users’ personality traits on their perception and performance when using the recommendation interface but also employed an eye-tracker to in-depth reveal how personality may influence users’ eye-movement behavior. Moreover, being different from related work that has mainly been conducted in a single product domain, our user study was performed in three typical application domains (i.e., electronics like smartphones, entertainment like movies, and tourism like hotels). Our results show that mainly three personality traits, i.e., Openness to experience , Conscientiousness , and Agreeableness , significantly influence users’ perception and eye-movement behavior, but the exact influences vary across the domains. Finally, we provide a set of guidelines that might be constructive for designing a more effective recommendation interface based on user personality.

A Simplified Mobile Advertising Model to Study Advertising Spreading through Personal Social Networks and Branded Apps

Research on mobile advertising has evolved considerably over the last few years. However, several under-researched topics remain, including mobile advertising spreading by leveraging users’ personal social networks. The present research addresses this gap by proposing a model for studying advertising spreading through personal networks using a viral approach and branded apps. The proposed model simulation suggests that the influence of spreader users’ percentage in ad spreading was significant, but increasing it beyond 20% did not impact the spreading growth. Additionally, including user privacy perception in the model positively affected spreading by increasing advertising propagation risk.

How do technology use patterns influence phishing susceptibility? A two-wave study of the role of reformulated locus of control

ABSTRACT Phishing attacks continue to be a concern for academia and practice. Practitioners ranked phishing attacks second to data breaches in a recent industry survey. For scholars, interest in understanding the factors that influence phishing susceptibility, defined as user vulnerability to phishing attacks, continues to grow. While prior research has identified either state (situational cues) or trait (technology use) factors that influence users’ response to phishing attacks, little previous research has investigated simultaneously user control of both state and trait factors on susceptibility to phishing. Additionally, the influence of users’ automatic or routine technology use, user traits, on phishing susceptibility has not been examined. We investigate the effects of users’ control of both state and trait factors on phishing susceptibility. Our results offer several interesting insights. Specifically, while routine technology use trait decreases phishing susceptibility, automatic technology use trait increases phishing susceptibility. Furthermore, while situational cues are related to phishing susceptibility, only users’ automatic technology use is related to susceptibility to phishing under message sender situational cues. Our findings provide practical insights for developing countermeasures that incorporate the level of control into training programs that target trainees with customised training aimed at preventing successful phishing attacks.

Topic Modeling Based on Two-Step Flow Theory: Application to Tweets about Bitcoin

Digital cryptocurrencies such as Bitcoin have exploded in recent years in both popularity and value. By their novelty, cryptocurrencies tend to be both volatile and highly speculative. The capricious nature of these coins is facilitated by social media networks such as Twitter. However, not everyone’s opinion matters equally, with most posts garnering little to no attention. We must determine whose opinion matters and the difference between influential and non-influential users. This study separates these two groups and analyzes the differences between them. It uses the hypertext-induced topic selection algorithm, which segregates the dataset based on influence. Topic modeling is then employed to uncover differences in each group’s speech types and what group may best represent the entire community. We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users.

Research on the flow experience and social influences of users of short online videos. A case study of DouYin

Recently, short online videos have been highly recognized by video market users and have developed rapidly. This study aims to explore why users enjoy watching and sharing short online videos by applying the theory of flow experience. Previous research has extensively examined traditional video arts such as TV and movies and text or image based, while research on short online videos has increased only in recent years. To improve the precision and comprehensiveness of the research, social influence is also used as a variable. This study takes the short video representative platform DouYin as a case study and the Chinese user market as the background. Through questionnaires, 406 users' data about short online video experiences were collected. After statistical analysis, the study finds that flow experience has a significant impact on participative behaviour and sharing behaviour for short online videos. According to further analyses, the flow experience, social norms, perceived critical mass and participative and sharing behaviour constitute three groups of mediating relationships. Finally, the discussion of the research results provides help to broaden the academic scope of the flow experience and video art, improve the short online video platform environment, and upgrade short online video services.

S.U.S. You’re SUS!—Identifying influencer hackers on dark web social networks

Dark web is an obscured part of the Internet, specifically used for sharing exploits, data breaches, and other means of cybercrime. Dark web forums provide opportunities to share such data and exploits and assign user reputation and credibility through participation in discussions and sharing data, exploits, and hacks. Such activities can help develop metrics to enable identification of influential mal-actors facilitating efficient and effective defense against emerging cyber threats, particularly zero-day exploits. This paper proposes an AI-inspired framework to identify influencers on dark web social networks (INSPECT) through intelligent analysis of user-profiles, interactions, and other activities. INSPECT framework leverages Feature Engineering, Social Network Analysis, Semantic Analysis, and K-means clustering and calculates an influencer score representing the users’ significance within these forums. INSPECT has been evaluated using CrimeBB dataset comprising user profiles and activities within dark web forums to assess its effectiveness in identifying influential users on the dark web forums.

Analysis of Public Opinion on the Network of Burst Events based on the PH-LSTM Neural Network

Due to the high-speed development of the Internet today, once a burst event occurs, it will attract the general public‘s attention in a short time and spread rapidly on the Internet, which will lead to the formation of large-scale network public opinion on emergencies. We can accurately judge the development trend of bursts and provide a reference for emergency supervisory departments to deal with public opinion crises by predicting the trend of public opinion on the network of emergencies. To address the issues of a single prediction model's low prediction accuracy and the large influence of social media factors on public opinion trend, an opinion prediction method integrating social media influence and LSTM neural network is proposed, and a PH-LSTM (Post Hot-Long Short-Term Memory) neural network is built. Firstly, the influencing factors of online public opinion are introduced, including the posting hotness and user influence of fused social media influence and the growth rate of events. Secondly, the LSTM neural network is improved to design a PH-LSTM prediction model with three hidden layers, and the PH-LSTM model is used in the quantitative prediction of unexpected events. Model’s validity was verified through experiments, and it was confirmed that the model has a good fitting and prediction effect on the trend of online public opinion of unexpected events.

Explainable AI in big data intelligence of community detection for digitalization e-healthcare services

Recommender Systems are designed to analysis the available data in the system to predict user’s desires and provide appropriate personalized suggestions to each user that suits their interests. In this paper, we have developed an explainable medical recommender system that uses graph concepts to provide an interpretable approach to medical data. The presented approach is based on community detection algorithms. It forms a graph between the users based on their similarity scores. Individuals with common interests are then grouped using graph community detection algorithms. Two community detection algorithms have been applied on the graphs of users and physicians in our medical recommender system. The results of applying two community detection algorithms are then used to address the cold start problem. We have identified the most influential users using a graph-based technique that finds the overlapping communities. We claim that using the overlapping graph of communities to address cold start problem will enhance the accuracy of the recommendations. Weighting or voting systems are also applied on the selected users to give feedback to potential consumers where there are n different options in a cluster. The similarity score of the users in the overlapping communities has been used to weight the final recommendation. The accuracy of recommended services depends on the proper selection of target populations. The proposed approach outperforms the use of each one of the community detections separately. The accuracy and precision of the proposed method are 93.06 and 88.34, which exceed the highest achieved accuracy in the literature.

KC-GCN: A Semi-Supervised Detection Model against Various Group Shilling Attacks in Recommender Systems

Various detection methods have been proposed for defense against group shilling attacks in recommender systems; however, these methods cannot effectively detect attack groups generated based on adversarial attacks (e.g., GOAT) or mixed attack groups. In this study, we propose a two-stage method, called KC-GCN, which is based on k -cliques and graph convolutional networks. First, we construct a user relationship graph, generate suspicious candidate groups, and extract influential users by calculating the user nearest-neighbor similarity. We construct the user relationship graph by calculating the edge weight between any two users through analyzing their similarity over suspicious time intervals on each item. Second, we combine the extracted user initial embeddings and the structural features hidden in the user relationship graph to detect attackers. On the Netflix and sampled Amazon datasets, the detection results of KC-GCN surpass those of the state-of-the-art methods under different types of group shilling attacks. The F1-measure of KC-GCN can reach above 93% and 87% on these two datasets, respectively.

Collective Influence Maximization in Mobile Social Networks

The omnipresence of information cascading process in mobile social networking applications makes the identification of a small set <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> of influential users, which is widely believed to trigger the information outbreak, always an crucial issue in various applications such as the mobile advertising and viral marketing. Formulated as Influence maximization (IM) in 2003, this NP-hard problem has received a multitude of studies with diverse angles. However, these works often unable to provide reliable solutions, due to the loss of an exact metric for evaluating users’ contributions on information cascading in the state-of-the-art sampling based IM schemes. In this paper, we evaluate users in IM based on the collective influence (CI), a metric on the structural features of the users in network graph that reflects the contributions of the users’ neighborhoods on shaping collective dynamics of the users over the whole network. For conducting the influencer identification under probabilistic diffusion model based on the CI, we specify a quantified structural feature of the most influential users from the scope of diffusion over the whole network, and reveal that the structural influence power (CI value) of each user is a weighted cumulation of the diffusion probabilities from neighbors within certain hops. Utilizing CI, we design a novel algorithm which identifies the influencers via iteratively choosing the users with top CI values. Moreover, we point out that directly computing CI values requires to traverse the network which is originally represented by a high-dimensional matrix, and leads to huge complexity of influencer identification. To improve scalability, we further trade precision for efficiency by incorporating network embedding, a dimensionality reduction technology for networks, into algorithm design, and propose a minor variant, where CI is jointly recapitulated by low-dimensional user representations and user degrees. The superiority of our algorithms is empirically validated over 8 datasets, with an increment in influence size up to 50 percent and a comparable or even less running time comparing with existing baselines.

Health information communication technology evaluation frameworks for pharmacist prescribing: A systematic scoping review.

Information communication technology (ICT) is instrumental in pharmacists' current practice and emerging roles. One such role is prescribing, which requires the use of clinical guidelines and documentation of decision-making, commonly via ICT. Development and refinement of ICT should be guided by evaluation frameworks that describe or measure features of ICT and its implementation. In the context of pharmacist prescribing, these evaluation frameworks should be specific to health stakeholders and the pharmacy setting. To identify ICT evaluation frameworks from health-related literature and review frameworks relevant to the development, implementation, and evaluation of pharmacist prescribing. A database search of CINAHL, Cochrane Library, EMBASE, Medline (Ovid), ProQuest, Scopus, Web of Science and grey literature was conducted, using combinations of keywords relating to 'ICT', 'utilization', 'usability', and 'evaluation framework'. Abstracts and titles were screened according to inclusion criteria. Identified evaluation frameworks were critiqued for relevance to pharmacy practice. Twenty-two articles were identified, describing the development or application of 20 evaluation frameworks. None of the frameworks was developed specifically for pharmacy practice. The Technology Acceptance Model (TAM), describing use behavior, behavior intention, perceived usefulness, and perceived ease of use, was the most widely utilized framework. The Information System Success (ISS) and Human-Organization and Technology Fit (HOT-fit) are notable evaluation frameworks that address user and organizational influences in health ICT utility, and factors of both can address the limitation of TAM. The findings call for development of an agile evaluation framework for the system under review; however, this can prove difficult due to the heterogenicity and complexity of the healthcare system, particularly contemporary pharmacy practice. While the TAM appears useful to evaluate user attitudes and intentions towards ICT, its relevance to ICT in contemporary community pharmacy practice requires exploration.

Retweet Predictions Regarding COVID-19 Vaccination Tweets through The Method of Multi Level Stacking

The rapid development of technology from day to day indirectly influences increasing social media use. This can be seen from spreading information that is very easily found on social media, one of which is Twitter. It is one of the most popular platforms for expressing people’s feelings by tweeting and interacting with other users at the same time. Various opinions about the COVID-19 vaccination began to be discussed on the Twitter platform. Moreover, most people take advantage of the feature available on Twitter, namely retweets. Users do retweet because there are many influencing factors. It can be caused by a reason that they have the same opinions and thoughts as the tweet owner, and so on. A retweet feature is also a form of information diffusion on the Twitter platform. The diffusion of information on Twitter has several factors, such as the most influential users, using hashtags or URLs, and others. In this conclusion, retweet predictions have been carried out regarding COVID-19 vaccination tweets using the features user-based and time-based through the Multi-Level Stacking classification method. This method indicates the best results when oversampling with an F1-Score of 96.23%.

Identification of influential users with cost minimization via an improved moth flame optimization

The issue of influence maximization has received great attention due to its application potential. Some traditional models used to solve the influence maximization problem only consider the maximum propagation range that the seed node set can reach but ignore the cost difference between the potential candidate nodes. This is not characteristic of real-world network behavior. Thus, in response to this research gap, a multi-objective optimization model based on maximizing influence spreading while minimizing cost is proposed in this paper. On the basis of maintaining the effective characteristics of non-dominated sorting moth flame optimization, the diversity weight and mutation mechanism are integrated into the algorithm to maintain the population diversity in the exploration stage. The evolutionary rules of the original moth flame optimization are redesigned to meet the desired needs of multi-objective influence optimization. By considering three types of real-world social networks, we show that our proposed method can generate a set of well-distributed Pareto optimal solutions.

PROBLEMS OF THE IMPACT OF MODERNIZATION OF INDONESIA'S PUBLIC TRANSPORTATION EQUIPMENT SYSTEM

The problem with the modernization impact of Indonesia's public transportation system, the author chose this title because until now the modernization of the public transportation system in Indonesia, mainly with regard to the public transformation mode system, is still lacking due to several factors. The main problem in the article is to discuss how developments are, the factors that support the development of the modernization of the transportation system, efforts to overcome the adverse effects of modernization of the transportation system. The conclusion from the discussion is that the modernization of the transportation system makes a large contribution to the convenience of transportation, the factors that support the development of the modernization of the transportation system, namely the economy; geography, politics, defense and security, technology, competition and external (external) factors that affect the transportation mode system in Indonesia, including laws or government regulations, the influence of public service users, efforts to overcome the negative effects of modernizing the transportation system by implementing eco-principles -efficiency in meeting fuel needs, reducing motor vehicle exhaust fumes. The methodology used in this research is literature review research, which will use a descriptive research type with a literature approach, based on the existing reality.

Exploring users' content creation and information dissemination behavior in social media: The moderating effect of social presence

Users' personality traits reveal different social media behavior characteristics. In order to explore the intrinsic relationships between personality traits and social media behavior, this study analyzes the influence of users' personality traits on social media content creation and information dissemination behavior, as well as the moderating effect of social presence. We collect users' personality data via questionnaires, crawl social media behavior data of samples from social media sites, and then establish regression models to test the research hypotheses. The results show that extraversion has a positive impact on content creation and information dissemination behavior, conscientiousness has a negative impact on content creation behavior, openness and agreeableness have no significant impact on social media behavior, and social presence has significant moderating effects on the relationships between personality traits and social media behavior.

A Novel Video Propagation Strategy Fusing User Interests and Social Influences Based on Assistance of Key Nodes in Social Networks

Accurate video launching and propagation is significant for promotion and distribution of videos. In this paper, we propose a novel video propagation strategy that fuses user interests and social influences based on the assistance of key nodes in social networks (VPII). VPII constructs an estimation model for video distribution capacities in the process of video propagation by investigating interest preference and influence of social users: (1) An estimation method of user preferences for video content is designed by integrating a comparative analysis between current popular videos and historical popular videos. (2) An estimation method to determine the distribution capacities of videos is designed according to scale and importance of neighbor nodes covered. VPII further designs a multi-round video propagation strategy with the assistance of the selected key nodes, which enables these nodes to implement accurate video launching by estimating weighted levels based on available bandwidth and node degree centrality. The video propagation can effectively promote the scale and speed of video sharing and efficiently utilize network resources. Simulations-based testing shows how VPII outperforms other state-of-the-art solutions in terms of startup delay, caching hit ratio, caching cost and higher control overhead.