Influence Of Use Research Articles

Prediction of influential nodes in social networks based on local communities and users’ reaction information

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users’ social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

Creating a Video for TikTok Social Media Content Kelirskin with the Theme of Balinese Kecak Dance Local Wisdom

The use of social media, especially TikTok, has become an integral part of digital marketing strategies for many brands, including Kelirskin. In this study, we explore the potential of TikTok as an effective marketing tool to boost Kelirskin product sales with a focus on the theme of Balinese Kecak dance local wisdom. Through pre-production, production, and post-production stages, we designed and created promotional videos that combine the beauty of Bali's nature and culture with the messages inherent in the Kelirskin brand. Despite facing some challenges, such as selecting appropriate settings and proper lighting, the authors successfully overcame these obstacles with creativity and dedication from the production team. By implementing additional strategies, such as researching TikTok user trends, collaborating with influential users, and utilizing TikTok's interactive features, Kelirskin can maximize TikTok's potential as an effective marketing tool to boost their product sales and strengthen their brand position in the digital marketing market.

Exploring stroke discourse on Twitter through content and network analysis among Indian users

The study aimed to understand stroke-related Twitter conversations in India, focusing on topics, message sources, reach, and influential users to provide insights to stakeholders regarding community needs for knowledge, support, and interventions. Geo-tagged Twitter posts focusing on stroke originating from India and, spanning from November 7, 2022, to February 28, 2023, were systematically obtained via the Twitter application programming interface, using keywords and hashtags sourced through Symplur Signals. Preprocessing involved the removal of hashtags, stop words, and URLs. The Latent Dirichlet Allocation (LDA) topic model was used to identify recurring stroke-related topics, while influential users were identified through social network analysis. About half of the tweets about stroke in India were about seeking support and post-stroke bereavement sharing and had the highest reachability. Four out of 10 tweets were from the individual twitter users. Tweets on the topic risk factors, awareness and prevention (14.6%) constituted the least proportion, whereas the topic management, research, and promotion had the least retweet ratio. Twitter demonstrates significant potential as a platform for both disseminating and acquiring stroke-related information within the Indian context. The identified topics and understanding of the content of discussion offer valuable resources to public health professionals and organizations to develop targeted educational and engagement strategies for the relevant audience.

Users' Technology Capabilities and the Growth of Micro and Small Enterprises in Nairobi City County, Kenya

This study sought to establish the influence of establish the influence of user's capabilities on the Growth of Micro and Small Enterprises in Nairobi, Kenya and; To determine the moderating effect of Entrepreneurial Orientation on user's capabilities and the Growth of Micro and Small Enterprises in Nairobi, Kenya. The study was grounded on the Technology Acceptance Model (TAM. The study adopted a descriptive research design since the design enabled the researcher to generalize the findings to a larger population of MSEs. In addition, this design enabled the researcher describe the characteristics of the population being studied as they exist at present hence minimizing biasness and maximizing the reliability of the evidence collected. This study was guided by positivism philosophy. The target population for this study comprised of all the 1835 Owners or managers of the MSEs registered with Nairobi City County and have operated for more than five years. Stratified and Simple Random sampling techniques was used to select a total of 184 respondents for the study. The study found out that user's capabilities and organizational technology policies were the key variable that significantly influencing the Growth of Micro and Small Enterprises in Nairobi County, Kenya. Specifically MSEs Staff were largely found to be un-trained especially on Emerging Technologies and lacked motivation to adopt and use technologies. In addition, study found out that there was low adoption on use of social media platform for business operation. The study recommend that the Management of MSEs needs to make deliberate efforts and invest on training especially on Emerging Technologies such as e-commerce, communication, e-production, e-procurement among others. This will ensure that the MSEs staffs are well equipped with the necessary skills and knowledge needed to effective utilize the emerging technologies in the market. Further, the management of the MSEs needs to be motivated to adopt and use technologies and innovation. Motivation many be inform of incentives and rewards that motivates users to adopt and use technology in their operations. The is also need to invest in a strong and robust social media platforms that are easy to use for most users. Social media networks can be harnessed to boast in product and services marketing and sourcing of market information among other.

The Influence of System Users’ Competencies on The Performance of Human Capital Management Information Systems in Tanzanian

Purpose: Human Capital Management Information System (HCMIS) performance in Tanzanian Local Government Authorities (LGAs) is investigated in this study, with a focus on the effect of user competency. Design/Methodology/Approach: This study used an explanatory cross-sectional methodology. Mwanza, Arusha, Dodoma, Morogoro, Iringa, and Kagera were the six regions of Tanzania that were included in the study, which included 37 LGAs. A total of 201 Human Resource Officers (HROs) were randomly chosen from each of the sampled districts to fill out a questionnaire that provided the bulk of the study's data. Six (6) HRO "approvers" and two (2) directors from the Human Capital Division were among the eight (8) key informants who were in-depth interviewed. Ordered logistic regression and content analysis were used to analyse the data. Findings: The study found that 21% of the HROs had sufficient IT skills, 52% claimed to have a deep understanding of HR, and 56% had 4–7 years of experience. System users' abilities, including their degree of IT skills, commitment, and experience, significantly affect HCMIS performance in terms of completeness, accuracy, and timeliness of information, according to the results of the ordered logistic model. Practical Implications: The study underscores the need for comprehensive and ongoing training programs to improve user competency. By regularly updating the knowledge and skills of employees, LGAs can ensure more efficient and effective use of the HCMIS. Social Implications: Individuals can be empowered to take charge of their professional development, leading to increased job satisfaction, employee motivation, improved HCMIS competencies, and, in turn, fostering diversity and inclusion. Originality and Value: The paper identified, cognitive abilities, and behavioural competencies, tailored to the needs and realities of Tanzanian organisations. This holistic approach to assessing competencies is relatively novel and adds depth to understanding how different aspects of user capabilities contribute to system performance.

Influence Of User’s Perception on Security of Electronic Health Records in Tanzania Public Hospitals

Influence Of User’s Perception on Security of Electronic Health Records in Tanzania Public Hospitals

Determinants of artificial intelligence-assisted diagnostic system adoption intention: A behavioral reasoning theory perspective

Artificial intelligence-assisted diagnostic systems (AIADS) signify a significant medical revolution, reducing environmental burdens and enabling AI-based medical services to catalyze sustainability-oriented positive change. Because users demonstrate almost contradictory attitudes or resistance to adopting medical information provided by AIADS, controversy persists regarding the use of the technology. Based on behavioral reasoning theory (BRT), this study adds empirical insights into the significant influence of users' values (openness to change) toward reasons (for and against) and their intentions to adopt AIADS from the perspective of behavioral reasoning. A scenario questionnaire survey of 604 Chinese users demonstrated that openness to change significantly impacts users' attitudes and reasons (for and against) adopting AIADS. A dual pathway explains the reasons for and against adopting AIADS. The findings contribute to BRT's application in AI-assisted medical service and provide implications to practitioners that will increase users' adoption of AIADS.

Attacking Social Media via Behavior Poisoning

Since social media such as Facebook and X (formerly known as Twitter) have permeated various aspects of daily life, people have strong incentives to influence information dissemination on these platforms and differentiate their content from the fierce competition. Existing dissemination strategies typically employ marketing techniques, such as seeking publicity through renowned actors or targeted advertising placements. Despite their various forms, most simply spread information to strengthen user impressions without conducting formal analyses of specific influence enhancement. And coupled with high costs, most fall short of expectations. To this end, we ingeniously formulate the task of social media dissemination as poisoning attacks, which influence specified content’s dissemination among target users by intervening in some users’ social media behaviors (including retweeting, following, and profile modifying). Correspondingly, we propose a novel poisoning attack, Influence-based Social Media Attack (ISMA) to generate discrete poisoning behaviors, which is difficult to achieve with existing attacks. In ISMA, we first contribute an efficient influence evaluator to quantify the spread influence of poisoning behaviors. Based on the estimated influence, we then present an imperceptible hierarchical selector and a profile modification method ProMix to select influential behaviors to poison. Notably, our attack is driven by custom attack objectives, which allows one to flexibly design different optimization goals to change the information flow, which could solve the blindness of existing influence maximization methods. Besides, behaviors such as retweeting are gentle and simple to implement. These properties make our attack more cost-effective and practical. Extensive experiments on two large-scale real-world datasets demonstrate the superiority of our method as it significantly outperforms baselines, and additionally, the proposed evaluator’s analysis of user influence provides new insights for influence maximization on social media.

Generalized hop‐based approaches for identifying influential nodes in social networks

AbstractLocating a set of influential users within a social network, known as the Influence Maximization (IM) problem, can have significant implications for boosting the spread of positive information/news and curbing the spread of negative elements such as misinformation and disease. However, the traditional simulation‐based spread computations under conventional diffusion models render existing algorithms inefficient in finding optimal solutions. In recent years, hop and path‐based approaches have gained popularity, particularly under the cascade models to address the scalability issue. Nevertheless, these existing functions vary based on the considered hop‐distance and provide no guidance on capturing spread sizes beyond two‐hops. In this paper, we introduce Hop‐based Expected Influence Maximization (HEIM), an approach utilizing generalized functions to compute influence spread across varying hop‐distances in conventional diffusion models. We extend our investigation to the Linear Threshold (LT) model, in addition to the Independent Cascade (IC) and Weighted Cascade (WC) models, filling a gap in current literature. Our theoretical analysis shows that the proposed functions preserve both monotonicity and submodularity, and the proposed HEIM algorithm can achieve an approximation ratio of under a limited hop‐measures, whereas a multiplicative ‐approximation under global measures. Furthermore, we show that expected spread methods can serve as a better benchmark approach than existing simulation‐based methods. The performance of the HEIM algorithm is evaluated through experiments on three real‐world networks, and is compared to six other existing algorithms. Results demonstrate that the three‐hop based HEIM algorithm achieves superior solution quality, ranking first in statistical tests, and is notably faster than existing benchmark approaches. Conversely, the one‐hop‐based HEIM offers faster computation while still delivering competitive solutions, providing decision‐makers with flexibility based on application needs.

The neighborhood advantage: exploring the impact of negotiation costs on transaction satisfaction in local second-hand trading platforms

This study aimed to investigate the determinants of transaction satisfaction and intention to use local second-hand marketplace platforms, focusing on the impact of negotiation costs and the moderating influence of users’ sense of their neighborhood. Building on transaction cost theory, we conceptualized negotiation costs as a multidimensional construct that encompassed economic, performance, time, and psychological costs. Our findings revealed that economic and time costs had a significant negative effect on transaction satisfaction, whereas performance and psychological costs did not exhibit such an impact. Furthermore, transaction satisfaction positively influenced users’ intention to use the platform, thereby mediating the relationship between negotiation costs and intention to use. Additionally, we found that users’ sense of their neighborhood enhanced transaction satisfaction and acted as a moderator, attenuating the negative impact of negotiation costs on satisfaction. For users with a high sense of my neighborhood, the negative impact of economic, performance, and time costs on transaction satisfaction was attenuated. These findings contribute to a deeper understanding of consumer behavior on local second-hand trading platforms and emphasize the importance of social cues in shaping transaction satisfaction and intention to use. The implications of our study offer valuable insights for second-hand platform operators.

How Artificial Intelligence Can Influence Elections: Analyzing the Large Language Models (LLMs) Political Bias

Abstract The rise of large language models (LLMs) such as ChatGPT and Gemini has raised concerns about their potential political biases and the implications for information dissemination and user influence. This study aims to measure the degree of political bias inherent in major LLMs by analyzing their responses to a standardized set of questions rating the quality and bias of popular news websites. Employing a systematic methodology, we queried both free and paid versions of ChatGPT and Gemini to rate news outlets on criteria such as authority, credibility, and objectivity. Results revealed that while all LLMs displayed a tendency to score left-leaning news sources higher, there was a notable difference between free and premium models in their assessment of subjectivity and bias. Furthermore, a comparison between the models indicated that premium versions offered more nuanced responses, suggesting a greater awareness of bias. The findings suggest that LLMs, despite their objective façade, are influenced by biases that can shape public opinion, underlining the necessity for efforts to mitigate these biases. This research highlights the importance of transparency and the potential impact of LLMs on the political landscape.

Identifying Domain-specific Opinion Leaders in Twitter (X): An Optimized Approach

The role of opinion leaders on Twitter to analyze and understand the diffusion of information cannot be overlooked. The coming of the information age and the advent of social networking platforms have not eliminated opinion leadership but rather led to the emergence of its new forms. In this line, this paper deals with organically identifying opinion leaders on Twitter, based on the list feature. The method relies on the meta-data of Twitter lists, containing semantic cues to infer the topical expertise of its members. Based on the studies that have already shown the effectiveness of this method, this paper further illustrates in detail how the method can be employed flexibly to identify highly influential Twitter users in any specific domain. In this regard, the method can be adapted to different research questions, allowing researchers to apply it to suit their specific objectives and data. This paper also presents a novel approach as to how influential Twitter users identified through Twitter lists can be ranked. The ranking index proposed is attentive to both vertical (public perception and engagement) and horizontal (peer perception) dimensions of information diffusion.

Personal branding strategies in online hashtag communities: the case of #AcademicTwitter

Several studies have explored the uses and benefits of social media hashtag communities in higher education… Yet, more research is needed to examine communication structures and strategies for personal branding in educational social media hashtag communities. In otherwords, to understand the dynamics, characteristics, and strategies for faculty, university staff, and graduate students need to brand themselves professionally in social media hashtag communities. This research aims to explore social network structures, dynamics, influencer characteristics, and personal branding strategies of the #AcademicTwitter online community. X (formerly known as Twitter) data on #AcademicTwitter from 2021 were retrieved and analysed using social network and quantitative content analysis. Our study found that most of the recurrent users in the community were scholars (faculty and researchers) predominantly from the STEM field. However, the most influential users were media/fan pages (a profile account created for different purposes such as sharing tips in academia, publishers, etc.) and other professionals (freelancers or university staff). Our research sheds light on the current practice of disclosing specific teaching and research interests or expertise in social media bio profiles for personal branding, especially among scholars. Unique communication contexts such as social media hashtag communities still bring challenges to the dissemination of information, relationship building, and personal branding strategies. Our results also provide recommendations for scholars (faculty and researchers), graduate students, university staff, and practitioners to improve communication practices and personal branding strategies on social media hashtag communities.

Identifying influential users using homophily-based approach in location-based social networks

Identifying influential users using homophily-based approach in location-based social networks

Archetypes of influential users in social question-answering sites

PurposeThis paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?Design/methodology/approachBased on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.FindingsFindings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.Originality/valueThis study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.

Determinants of Accounting Information System Quality and Its Impact on Accounting Information Quality: Study on Hospitality in Semarang City, Indonesia

This research intends to investigate the user capabilities and culture of an organization on the quality of accounting information systems and its impact on information quality. A quantitative approach was adopted, and 82 questionnaires were distributed to hotel employees in Semarang, Indonesia working in the accounting department. Partial least squares are used to achieve numerical results that can explain the phenomenon under study. The research results show that users’ technical abilities and organizational culture have a positive effect on the quality of accounting information systems. The influence of users’ technical capabilities and organizational culture on the quality of accounting information systems has an impact on the quality of accounting information. This research is one of the first to link user capabilities and the culture of the organization to the quality of accounting information systems and its impact on information quality. This contributes to decision usefulness theory, highlighting systems of accounting information systems and quality of information, systems of accounting information are created to produce good quality information that can be used as a basis for corporate decision-making. The results also contribute to the systems of accounting information literature.

Transformative User Credibility Assessment on Twitter: A RNN based Heuristic Approach

Objectives: To construct a comprehensive weighted multi-dimensional model to assess the impact of influence score of Twitter users, considering the credibility based on user profile, their tweets and social interactions aiming to empower users in distinguishing fake news or misinformation. Methods: The credibility evaluation is formulated based on text analysis, user account attributes, and user social engagement. We've gathered around 100,000 tweets from 100 users using Tweepy API over a six-month duration for the purpose of evaluating credibility. The collected tweets spanned diverse professions namely politics, entertainment, business, science, sports, and trending topics. We chose to utilize a self-devised deep active learning model to classify and label the unlabelled data instead of engaging in time-consuming human annotation for the tweets we gathered. Findings: The obtained accuracy for influence score evaluation for Recurrent Neural Network, Random Forest, Naïve Bayes, Decision Tree, and Support Vector Machine are 89.03%, 79.10%, 81.59%, 73.06% and 79.45% respectively. Upon reviewing and analysing the outcomes, RNN surpassed all other models achieving an exceptional accuracy of 89.03%. Novelty: Employing a weighted multi-dimensional framework, it systematically evaluates the influence score by considering the credibility of both users and tweets within the context of Twitter. Weighted features are instrumental in capturing the relative importance of different elements, leading to a more refined and context-aware decision-making process. In contrast to earlier research, which predominantly centred on the credibility of individual tweets, our research work shifts the focus to a broader perspective, encompassing the credibility of users, their tweets and their overall social influence. By incorporating user influence score, the framework not only empower users in discerning fake news or mis-information but also elevates their ability to gauge the reliability of information, offering a nuanced approach to news credibility analysis. Keywords: Active Learning, Credibility Score, User Influence, Twitter, Machine Learning, Recurrent Neural Network

“Not all travel videos are equal.” Examining the effects of short travel videos on travel intentions and electronic word-of-mouth

Although short-form videos are treated as an effective marketing tool for destinations to attract potential tourists’ attention, what characteristics of short travel videos stimulate potential tourists to involve in destinations and the influence of users’ inertia behavior of viewing short travel videos on its promotion efficiency remains poorly studied. Thus, this study put forward a conceptual model to investigate the power of content novelty of short travel videos and parasocial relationships on users’ involvement behavior and explored the moderating role of inertia between involvement and users’ travel intentions. Partial least squares (PLS) path modeling was applied to evaluate the linkage between variables, in which 212 valid questionnaires were collected via private messages to users who commented on or posted relevant short travel videos. Data results revealed that content novelty and parasocial relationships significantly influence users’ involvement behavior, further predicting travel intention and electronic word-of-mouth. Inertia behavior negatively moderates users’ intention to travel; their browsing behavior primarily serves as a means of passing time, but they do nothing significant. The findings provided a novel viewpoint on the existing destination marketing literature in short travel videos by testing the interactive role of inertia with involvement. Further implications will be discussed below.

A cost-effective seed selection model for multi-constraint influence maximization in social networks

The Influence Maximization (IM) problem aims to maximize the diffusion of information or adoption of products among users within a social network by identifying and activating a set of initial users. It is not unrealistic to have a higher activation cost for more influential users in real-life applications. However, existing works on IM focus solely on finding the most influential users as the seed set, without considering either the activation costs of individual nodes and the total budget or the size of the seed set. This oversight may lead to infeasible solutions, particularly from financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation called Multi-Constraint Influence Maximization (MCIM) to achieve a cost-effective solution under both budgetary and cardinality constraints. The MCIM model allows for variable-length solutions, necessitating the exclusion of seed nodes from the influence spread estimation. Consequently, unlike existing IM formulations, the spread function under the MCIM model is no longer monotonic but submodular. As it has also been proven to be an NP-hard problem, we propose a Simple Additive Weighting (SAW)-assisted Differential Evolution (DE) algorithm for solving large-size real-world IM problems. Experimental results on four datasets demonstrate the effectiveness of the proposed formulation and algorithm in finding optimal and cost-effective solutions.

The influence of user generated content jiniso.id on tiktok social media on consumer buying interest

This research project stems from the growing prevalence of user-generated content (UGC) centered around Jiniso.id products within the vibrant landscape of the TikTok platform. Its core aim is to meticulously investigate the intricate relationship between UGC and consumers' purchasing behaviors concerning Jiniso.id products among the expansive user base of TikTok spanning Indonesia. By adopting a quantitative research approach, data collection was facilitated through the dissemination of structured questionnaires among a pool of potential consumers. Employing a purposive sampling technique, a total of 100 questionnaires were methodically distributed to individuals deemed representative of the target demographic. The analytical framework utilized in this study revolves around the application of simple linear regression analysis, which enables a comprehensive examination of the correlation between UGC and consumer purchasing tendencies. The compelling findings derived from this research underscore the statistically significant impact of user-generated content (UGC) in fostering a positive and discernible influence on consumer buying intentions, thereby shedding valuable insights into the dynamics of digital marketing and consumer behavior within the realm of social media platforms like TikTok.