Authority Users Research Articles

ENHANCING USER EXPERIENCE THROUGH SENTIMENT ANALYSIS FOR KATSINA STATE TRANSPORT AGENCY: A TEXTBLOB APPROACH

Katsina State Transport Authority is the state's government-owned transportation provider, operating in all local governments. Because of its extensive reach, it faces a difficult problem in measuring customer satisfaction with the services it delivers. The purpose of this research is to improve the experiences of Katsina State Transport Authority users by using sentiment analysis and the TextBlob library to categorize comments as neutral, negative, or positive. The study begins with meticulous data collecting using Google Forms to provide a representative sample that captures an all-encompassing view of user opinions. The study used feature engineering and model fine-tuning to improve the process, tailoring TextBlob's performance to the complexities of transportation-related feedback. The results reveal that 71% of respondents are usually happy with the agency's services, while 9% offered negative comments with ideas for improvement. This study's findings, which included sentiment analysis and topic modeling, present a road map for enhancing services and planning.

Order from chaos: Deconstructing the interactions of multiple online stakeholders in NIMBY conflicts

The vigorous development of NIMBY (Not-in-my-backyard) projects is inevitable, while online conflicts caused by these projects enter a high incidence period. However, the behavioral patterns of online stakeholders in NIMBY conflicts have not been synthetically quantified. This research took the Maoming PX project as the case, crawled 66,831 related messages on Sina Weibo, combining topic extraction and network analysis to identify interactive networks and multi-stakeholder roles. Results reveal that authoritative users mainly issue event progress and education, while private users are more concerned about project risks, expressing their attitudes and suggestions. As for the user roles, results show a diverse set of stakeholder classifications during different conflict phases. Findings provide a retrospective evaluation in gauging different groups of actors from complex interactions and also provide valuable inputs to evidence-based policymaking to help authorities better understand the nuanced picture of social stability. Practitioners can utilize the findings to improve communication capabilities for future project conflicts.

Autoimmune conditions following mRNA (BNT162b2) and inactivated (CoronaVac) COVID-19 vaccination: A descriptive cohort study among 1.1 million vaccinated people in Hong Kong

BackgroundConcerns regarding the autoimmune safety of COVID-19 vaccines may negatively impact vaccine uptake. We aimed to describe the incidence of autoimmune conditions following BNT162b2 and CoronaVac vaccination and compare these with age-standardized incidence rates in non-vaccinated individuals. MethodsThis is a descriptive cohort study conducted in public healthcare service settings. Territory-wide longitudinal electronic medical records of Hong Kong Hospital Authority users (≥16 years) were linked with COVID-19 vaccination records between February 23, 2021 and June 30, 2021. We classified participants into first/second dose BNT162b2 groups, first/second dose CoronaVac groups and non-vaccinated individuals for incidence comparison. The study outcomes include hospitalized autoimmune diseases (16 types of immune-mediated diseases across six body systems) within 28 days after first and second dose of vaccination. Age-standardized incidence rate ratios (IRRs) with exact 95% confidence intervals (CIs) were estimated using Poisson distribution. ResultsThis study included around 3.9 million Hong Kong residents, of which 1,122,793 received at least one dose of vaccine (BNT162b2: 579,998; CoronaVac: 542,795), and 721,588 completed two doses (BNT162b2: 388,881; CoronaVac: 332,707). Within 28 days following vaccination, cumulative incidences for all autoimmune conditions were below 9 per 100,000 persons, for both vaccines and both doses. None of the age-standardized incidence rates were significantly higher than the non-vaccinated individuals, except for an observed increased incidence of hypersomnia following the first dose of BNT162b2 (standardized IRR: 1.47; 95% CI: 1.10–1.94). ConclusionsAutoimmune conditions requiring hospital care are rare following mRNA and inactivated COVID-19 vaccination with similar incidence to non-vaccinated individuals. The association between first dose BNT162b2 vaccination and immune-related sleeping disorders requires further research. Population-based robust safety surveillance is essential to detect rare and unexpected vaccine safety events.

Gender Role Stereotyping in Indonesian Primary School English Textbooks

This study aims to examine gender role stereotyping represented in Indonesian primary school English textbooks. The categorization of roles are in the form of family roles, occupational roles, and activity. The data were taken from two English textbooks published by a private company commonly used by primary schools. By employing content analysis, the findings reveal dichotomy in family roles, domestic-tendency of occupational roles for female, and low-risk activity depicted in female characters. The implications of the study are discussed discursively to promote gender awareness at school context especially for teachers as authoritative users in the classroom and to recommend language textbook author and designer to dismiss gender role stereotyping.

Hello? Is There Anybody in There?

As social media has become more present in people's day-to-day lives, many turn to these platforms in natural disasters to keep abreast of the ever-evolving crisis situation. Facing the increasing amount of crisis-related information available on the social media platforms, users tend to focus on and reach out to authoritative sources---individuals or organizations that provide authoritative and credible crisis-related information due to their official status or position. As they provide high-quality information, response from the authoritative sources can be especially valuable to social media users directly affected in natural disasters. In this study, we aim to extend the reach of credible information during crisis, and direct the attention of authoritative users to the affected users who need their help. Specifically, we investigate what factors differentiate the tweets by regular users that receive responses from authoritative accounts from those that do not. We find that regular users' popularity and official accounts' level of busyness do not seem to affect the likelihood of tweets receiving a response. We thus explore the linguistic features of the tweets' content. Topic modeling and sentiment analysis results suggest that these linguistic aspects of the tweets may affect the response rate from authoritative sources. Our findings suggest crisis-related policy implications, as well as design implications for social media platforms where such exchanges take place, which can potentially increase the reach of credible information in a crisis and help those affected obtain the safety-critical information they need.

Discovering Hidden Topical Hubs and Authorities Across Multiple Online Social Networks

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users' relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHAT model, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs.

Risk Reduction Privacy Preserving Approach for Accessing Electronic Health Records

In the healthcare field, preserving privacy of the patient's electronic health records has been an elementary issue. Numerous techniques have been emerged to maintain privacy of the susceptible information. Acting as a first line of defence against illegal access, traditional access control schemes fall short of defending against misbehaviour of the already genuine and authoritative users: a risk that can harbour overwhelming consequences upon probable data release or leak. This paper introduces a novel risk reduction strategy for the healthcare domain so that the risk related with an access request is evaluated against the privacy preferences of the patient who is undergoing for the medical procedure. The proposed strategy decides the set of data objects that can be safely uncovered to the healthcare service provider such that unreasonably repeated tests and measures can be avoided and the privacy preferences of the patient are preserved.

Secure Outsourced Medical Data against Unexpected Leakage with Flexible Access Control in a Cloud Storage System

The application of cloud storage system has been deployed widely in recent years. A lot of electronic medical records (EMRs) are collected and uploaded to the cloud for scalable sharing among the authority users. It is necessary to guarantee the confidentiality of EMRs and the privacy of EMR owners. To achieve this target, we summarize a series of attack behaviors in the cloud storage system and present the security model against many types of unexpected privacy leakage. Privacy of unassailed EMRs is guaranteed in this model, and the influence of privacy leakage is controlled in a certain scope. We also propose a role-based access control scheme to achieve flexible access control on these private EMRs. One can access medical records only if his/her role satisfies the defined access policy, which implies a fine-grained access control. Theoretical and experimental analyses show the efficiency of our scheme in terms of computation and communication.

Tsunami Zonal Marking Application using Google Application Protocol Interface

Tsunami Zonal Marking application is part of a project aims to create an integrated application to conduct tsunami evacuation and mitigation process. This integrated application called INTERGIS consists of three main parts or modules, which are back-end, front-end and interoperability modules. Tsunami zonal marking back-end module is a web-based application used by authoritative users, such as Regional Disaster Management Agency and Disaster Operational Control Centre officer, to marking the tsunami impacted area as well as to pointing the assembly safe point in digital maps application. While the front-end module is an Android based application that can assist the user to reach the nearest safe assembly point when the tsunami disaster occurs. This application activated when it is triggered by BMKG's INATEWS tsunami early warning system. The Interoperability module is part of application that organize the data connection to BMKG's INATEWS and Google API web services. This paper explores more on back-end module that built on CodeIgniter PHP framework. This application has been developed and has successfully mapped tsunami zones in several areas of Bali. This app utilizes google Application Programming Interface (API) and can be used by SAR units from each district. The tsunami zone data and the assembly point will be supplied by the Regional Disaster Management Agency (BPBD) of each district.

Finding high-impact interdisciplinary users based on friend discipline distribution in academic social networking sites

Specialized academic social networking sites are gaining popularity in scientific communication. A huge volume of interdisciplinary information is generated when researchers from multiple disciplines participate in scientific communication, which makes it possible to discover interdisciplinary users from a range of disciplines. In this study we analyze ScienceNet, one of the most well-known academic social networking sites in China, to find high-impact interdisciplinary users. We focus on the discipline distribution of friends and adopt phylogenetic species evenness on discipline phylogenetic trees to find 128 high-impact interdisciplinary users. A questionnaire was then sent to these academics to test the accuracy of this method. The questionnaire results show that our approach can determine authority users who span specific disciplines. Thus our approach will be useful for finding interdisciplinary collaborators and academic social networking site-related international peer reviewers.

Level-Change Stackelberg Games Model for the Combined Traffic Assignment–Signal Control Equilibrium on Networks

Combined traffic assignment–signal control equilibrium is usually integrated into a non-cooperative games model between the network authority and road users. Unlike a pure Wardropian equilibrium, in reality there may be both competition and cooperation between authority and users. Authority has always been regarded as the upper level in classical bi-level formulations, but this placement may increase the difficulty of obtaining a global optimal solution between authority and users. This paper proposes a level-change Stackelberg (LC Stackelberg) model that embraces both authority–user and user–authority formulations. The model is calibrated by a model predictive control (MPC) controller. A route-choice probability model is used to estimate flow burden on two parallel routes. Meanwhile, the difference of route-choice probability between the two parallel paths is regarded as the level-change threshold. A generalized autoregressive conditional heteroscedasticity (GJR-GARCH) model is used as a triggering function in the MPC controller to fulfill the level-change procedure. A modified wavelet neural network algorithm is used to seek the global optimal solution. Cournot, Stackelberg, and Monopoly, combined with a fixed-time control policy based on the Webster method, were chosen as benchmarks in a numerical example to test model validity. The results show that the LC Stackelberg model obtains the minimum total travel time compared with other models. Furthermore, the level-change between authority and users could also decrease route choice probability on one specific path, indicating the model’s potential application in urban networks.

A personalized authoritative user-based recommendation for social tagging

Personalized information recommendation based on social tagging is a hot issue in academia nowadays, butthe concept of an authoritative user has not been emphasized in the existing literature. This paper first proposes a method to determine user authority in a social tagging system, in which the quality authority and quantity authority of users are calculated from a user co-occurrence network, which is derived from users’ participation in the social tagging system. Degree centrality is employed for the user authority calculations, which are taken as weights for tag voting. On this basis, a resource model is constructed by summing up the tags from each user and their corresponding weights to represent each resource in the collection. User models are then obtained based on the resource models, and cosine similarity is used for making resource recommendations to users. An experiment was conducted on a dataset crawled from Delicious.com. The results show that the average GP relevance of the authoritative user based algorithm reaches 0.6115 much better than two benchmark algorithms.

Belief Measure of Expertise for Experts Detection in Question Answering Communities: case study Stack Overflow

Online Question Answering Communities (Q& A C) provide a valuable amount of information in several topics. The major challenge with Q& A C is the detection of the authoritative users. When manipulating real world data, we have to deal with imperfections and uncertainty that can occur. In this paper, we propose a belief measure of expertise allowing us to detect users with the highest degree of expertise based on their attributes. Experiments on a dataset from a large online Q&A Community prove that the proposed model can be used to improve the identification of most expert users.

Tweeting with Authority: Identifying Influential Participants in Agriculture-Related Water Quality Twitter Conversations

In August 2014, Toledo, Ohio, experienced a dangerous algae bloom that led to a citywide water ban. News media coverage of the incident was widespread, as was social media conversation. Opinion leadership has been linked to news media agenda setting, and social media users are capable of generating considerable social influence within the sphere of their social networks. To better understand the dynamics of the conversation—especially as it pertained to agriculture—both during and after the water ban, the researchers used a social media analysis platform to identify high-influence Twitter users who participated in water-quality discussions about the Toledo water ban. Narrowing the search to agriculture-related discussion, the researchers revealed a dearth of agriculture-related content and also identified three categories of Twitter users in the conversation, including news sources, activists, and agriculture advocates. The researchers also found that Ohio users in post-ban discussions tended to be more influential than those who participated during the water ban. Identifying these users allows practitioners to monitor influential accounts for emerging issues and to engage with authoritative users in their geographic regions. The researchers also recommend that agriculture advocates exercise restraint in publicly speaking out about the industry’s involvement in environmental issues.

Multivariate Beta Mixture Model for Automatic Identification of Topical Authoritative Users in Community Question Answering Sites

Community question answering (CQA) site is an online community to provide valuable information in wide variety of topics in question–answer form to users’. The major problem with CQA lies in identifying the authoritative users in the domain of the question so as to route the question to right experts and selecting the best answer etc. The existing work suffers from one or more limitations such as: 1) lack of automatic mechanism to distinguish between authoritative and non-authoritative users in specified topics; 2) the high dependence on its training data in supervised learning which is too time-consuming process to obtain labeled samples of data manually; and 3) some approaches rely on using some cutoff parameters to estimate an authority score. In this paper, a parameter less mixture model approach is proposed to identify topical authoritative users to overcome the above-mentioned limitations. The statistical framework based on multivariate beta mixtures is utilized on feature vector of users’ which is composed of information related to user activities on CQA site. The probability density function is therefore devised and the beta mixture component that corresponds to the most authoritative user is identified. The suitability of the proposed approach is illustrated on real data of two CQA sites: StackOverflow and AskUbuntu. The result shows that the proposed model is remarkable in identifying the authoritative users in comparison with conventional classifiers and Gaussian mixture model.

Information management and control systems in the port community: insights and challenges from regulations

This paper aims at investigating the role of information management and control systems in the inter-organisational relationships between the Port Authority (PA) and port users, adopting an information technology platform, as the Port Community Systems (PCSs). We focus on the effects of the PCSs implementation on the information management, and control systems used to support the decision making of the Port Authorities (PAs). We conduct a literature review on regulations and digitalisation of operating processes in the seaports, and the related information management. Through the case study methodology, we used semi-structured interviews to port users and PA in an Italian seaport. Drawing from perspectives of the Balanced Scorecard (BSC), we identify some key performance indicators (KPIs) addressed to measure and control the information and data flows concerning the processes within the inter-organisational relationships in the port community. The study provides interesting theoretical and managerial implications, especially highlighting the positive effects of PCSs.

2-Step Logistic map Chaotic Cryptography using Dynamic Look-up Table

Encryption is to reorganize the message into discrepancy form so that the message is keep secret. The goal of encryption is to give an easy and inexpensive means of encryption and decryption to all authoritative users in possession of the suitable key and difficult and expensive means to estimate the plain text without use of the key. The Baptista proposed a Chaotic Encryption technique, which seems to be much better than traditional encryption methods used today. Chaotic encryption is the new trend of cryptography. It makes use of chaotic system properties such as sensitive to initial condition and lot of information. It is possible to encrypt a message (a text composed by some alphabet) using the Ergodic property of the simple low-dimensional and chaotic logistic equation. But it still has several problems, such as slow in speed and suffering from floating point operations and has less security. This paper is to overcome these problems with dynamic loop up table. General Terms Ergodic property, Security, Chaotic Cryptography.

Identifying Authorities in Online Communities

Several approaches have been proposed for the problem of identifying authoritative actors in online communities. However, the majority of existing methods suffer from one or more of the following limitations: (1) There is a lack of an automatic mechanism to formally discriminate between authoritative and nonauthoritative users. In fact, a common approach to authoritative user identification is to provide a ranked list of users expecting authorities to come first. A major problem of such an approach is the question of where to stop reading the ranked list of users. How many users should be chosen as authoritative? (2) Supervised learning approaches for authoritative user identification suffer from their dependency on the training data. The problem here is that labeled samples are more difficult, expensive, and time consuming to obtain than unlabeled ones. (3) Several approaches rely on some user parameters to estimate an authority score. Detection accuracy of authoritative users can be seriously affected if incorrect values are used. In this article, we propose a parameterless mixture model-based approach that is capable of addressing the three aforementioned issues in a single framework. In our approach, we first represent each user with a feature vector composed of information related to its social behavior and activity in an online community. Next, we propose a statistical framework, based on the multivariate beta mixtures, in order to model the estimated set of feature vectors. The probability density function is therefore estimated and the beta component that corresponds to the most authoritative users is identified. The suitability of the proposed approach is illustrated on real data extracted from the Stack Exchange question-answering network and Twitter.

Study on SINA micro-blog personalized recommendation based on semantic network

In recent years, semantic network is applied to more and more research areas, such as Information Science areas. Differing from traditional users’ recommendations, the tweets’ recommendation in a micro-blog network has two crucial differences. One is high authority users or one’s special friends usually play a very active role in tweet-oriented recommendation. Micro-blog user will put the users his/her very interested into “special attention” group, and the topics discussed more in “special attention” group are more likely to be the user interested topic. The other is that users hope to obtain more relevant tweets about what he/she is interested in. Thus, this paper uses the k-cores analysis method to extract topics that users pay attention to, and employs the method of factor analysis to analyze index, and to extract the tweet heat factor and user authority factor. Besides, this paper intends to use the method of RS and linear regression to determine the parameters for balancing the value of the tweet heat factor and user authority factor. Finally, this paper manages to establish a timely personalized recommendation model based on semantic network for SINA tweets. According to the experimental results, the proposed method in this paper can effectively solve problems existing in micro-blog tweets in a personalized and timely recommendation way.

A Modified Method for User Authority Ranking in Chinese Microblog

Nowadays, microblog has emerged as the most promising social networking service in the Web2.0 age with diverse features for information dissemination, interpersonal communication and many other aspects. Chinese microblog shows great potential owing to the rich marketing source and powerful media influence brought by the huge user population. As the microblog community is largely influenced by some authoritative users, identifying these users and understanding the information dissemination pattern are theoretically and practically significant for us to promote user services, develop further applications and seek commerce opportunities. In this paper, we proposed a modified method for user authority ranking. A user-message graph modified according to Chinese microblog was applied to our study, and an algorithm based on ObjectRank was used to calculate ueser authority scores. We analyzed Sina microblog, as it was the most widely adopted Chinese microblog and more resources for research was accessible.