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The Relationships between Influencers and Followers Who Troll and Proactive-Reactive Trolling

Proceedings of the Association for Information Science and Technology 2024年第1期61卷 1093-1095页

作者： Sun, Honglei Fichman, Pnina School of Information Management Nanjing University China Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Little is known about the differences between trolling by influencers and followers, the difference between proactive and reactive trolling, and the relationship between them. Based on a content analysis of 1,386 comments on 160 posts of 19 influencers during a Sina Weibo trolling event, we found that influencers troll more frequently than their followers and they troll proactively more often than reactively, while followers troll reactively more often than proactively. Influencers generated less but more impactful content compared with their followers. In both proactive and reactive trolling, influencers derailed the discussion while followers provoked through their proactive trolling and insulted through their reactive trolling. These findings extend the research scope of trolling asymmetry and establish theoretical connections between social media roles and proactive-reactive trolling. 87 Annual Meeting of the Association for Information Science & Technology | Oct. 25 – 29, 2024 | Calgary, AB, Canada.

关键词： Proactive-Reactive trolling Sina Weibo Social media influencers

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Predicting Ischemic Heart Disease Using Supervised Learning Techniques on Clinical Data 4

Predicting Ischemic Heart Disease Using Supervised Learning ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bhende, Kabo Clifford Sigwele, Tshiamo Kuthadi, Venumadhav Selvaraj, Rajalakshmi Botswana International University of Science and Technology Department of Computing & Informatics Palapye Botswana

ISBN: (纸本)9798331518981

Ischemic Heart Disease (IHD) continues to be one of the world's leading causes of death, accurate predictive models are fervently needed to enable early intervention. Traditional risk assessment methods often fall short in timely detection and prevention, necessitating the development of advanced machine learning (ML) models. This review critically evaluates recent advancements in ML-based IHD prediction, focusing on their objectives, performance metrics, limitations, and datasets. Our analysis identifies Random Forest and Support Vector Machines as the most frequently adopted models, achieving accuracies often exceeding 90%. However, challenges such as small, imbalanced datasets and data privacy issues persist. Emerging trends highlight the importance of comprehensive datasets and personalized approaches to enhance model reliability and clinical applicability. Additionally, there is a growing emphasis on feature selection and data quality to improve prediction accuracy. This research proposes RF model for accurately predicting IHD. The results show that the proposed model outperforms current works with 0.93 accuracy and 0.95 Area Under the Curve. © 2024 IEEE.

关键词： Prediction models

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Ontology-based Car Recommender System Using Functional Requirements Interaction 5

Ontology-based Car Recommender System Using Functional Requi...

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5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023

作者： Suryadi, Firra Millaty Baizal, Z.K.A. Telkom University Informatics School of Computing Bandung Indonesia

ISBN: (纸本)9798350303414

The automotive industry continues to evolve every year, especially in the field of cars. Manufacturers from various brands are competing to introduce cutting-edge innovations with technological sophistication and unique features. A recommender system for cars has been developed but only based on technical specifications. However, cars have complex specifications, so many users do not familiar with the technical specification. Users find it easier to express their needs in terms of the functional aspects of a product rather than its technical features. To address this issue, we propose a conversational recommender system (CRS) to assist in interacting with users in order to elicit user need preferences. Therefore, we created a conversational recommendation system (CRS) designed to interact with users and capture their specific preferences. CRS engages users in conversations like sales professionals, facilitating natural and efficient interactions to uncover their specific desires. The model combines ontology structures and algorithms to effectively capture user preferences, taking into account explicit product information and temporal data to build domains and user profiles. The evaluation of this system focuses on two main parameters: system performance and user satisfaction. The system performance evaluation yielded an impressive overall accuracy rate of 87.84%, accompanied by positive feedback from users regarding satisfaction. These results demonstrate the effectiveness of the user-centered recommendation system in providing accurate recommendations and assisting users in selecting their desired automotive products. © 2023 IEEE.

关键词： Ontology

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Investigating the Mediating Role of Satisfaction in Student E-Wallet Use 6

Investigating the Mediating Role of Satisfaction in Student ...

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6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Kalo, Isumail Amron, Mohd Talmizie Marini Hussain, Fazlin Ismail, Najiahtul Syafiqah Universiti Teknologi Mara College of Computing Informatics and Mathematics Kuala Terengganu Malaysia

ISBN: (纸本)9798350353853

This study examines the influence of student perceptions on their e-wallet use, focusing on the mediating role of satisfaction. A quantitative approach using a self-administered online survey gathered data from 500 university students. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyse the relationships between perceived usefulness, perceived ease of use, trust in e-wallets, satisfaction, and intention to use. The findings reveal that perceived usefulness, perceived ease of use, and trust (significantly influence students' intention to use e-wallets. Satisfaction was found to mediate these relationships effectively, with indirect effects on perceived usefulness, perceived ease of use, and trust on intention to use. Interestingly, price benefit did not significantly impact intention to use. These findings highlight the importance of student perceptions in driving e-wallet adoption and suggest the need for further exploration of factors influencing student satisfaction. The study offers e-wallet developers insights into designing user-friendly, valuable, and secure platforms that cater to student needs. © 2024 IEEE.

关键词： Fintech

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SMOTE-LOF and Borderline-SMOTE Performance to Overcome Imbalanced Data and Outliers on Classification 3

SMOTE-LOF and Borderline-SMOTE Performance to Overcome Imbal...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Insan, Hanifatul Prasetiyowati, Sri Suryani Sibaroni, Yuliant Telkom University Informatics School of Computing Bandung Indonesia

ISBN: (纸本)9798350394559

Dealing with data imbalance and outliers is an important challenge in data classification. The aim of this study is to improve classification performance by reducing the effects of class imbalance and the presence of outliers in the dataset. SMOTE-LOF combines the SMOTE oversampling method with the Local Outlier Factor (LOF) to create a synthetic sample that also accounts for potential outliers. Meanwhile, Borderline-SMOTE identifies "borderline"samples in the minority class and then creates synthetic samples along the border between the majority and minority classes. In this study, experiments were conducted using classification algorithms such as Naïve Bayes, and Support Vector Machine on datasets that are imbalanced and contain outliers. The datasets used in this research include Pima Indians, Haberman, Glass, and Rainfall. This research scenario includes a comparison with previous research that has been done regarding SMOTE-LOF and Borderline-SMOTE on the Rainfall dataset. The results showed that on the three datasets, Borderline-SMOTE outperformed SMOTE-LOF on all three classifiers with an average accuracy of 4-6%, precision of 2-4%, recall of 5-10%, and F1 score of 5-6%. When the technique was applied to the Rainfall dataset, the results showed a 10-25% increase in accuracy. The outcomes consistently demonstrate that, when applied to the Pima Indians, Haberman, and Glass datasets, Borderline-SMOTE improves the performance of several classification algorithms. Better accuracy, precision, recall, and F1 score are evidence of this when compared to the application of the SMOTE-LOF technique. © 2023 IEEE.

关键词： Support vector machines

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Gold Prices Forecasting Using Recurrent Neural Network with Attention Tuned by Metaheuristics

Gold Prices Forecasting Using Recurrent Neural Network with ...

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2023 IEEE World Conference on Applied Intelligence and computing, AIC 2023

作者： Jovanovic, Andjela Dogandzic, Tea Dobrojevic, Milos Sarac, Marko Bacanin, Nebojsa Zivkovic, Miodrag Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (纸本)9798350310061

A large number of statistical studies have proven that the value of gold is highly valued nowadays, despite the numerous crises that have hit the global market. Due to the great risks that daily affect sudden changes in gold prices on the global market, it is of great benefit to develop tools that will enable automated monitoring and prediction of gold price changes. This can be quite helpful for those countries and major corporations whose economy depends on this. In this paper recurrent neural network with attention layer was used to forecast the gold price, while the hyperparameters of the network were tuned by the novel variant of the particle swarm optimization algorithm. Obtained results in terms of standard regression metrics were compared to those generated by other cutting-edge methods and proposed method proved to be very promising in this area. © 2023 IEEE.

关键词： Forecasting

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An Intelligent Supervised Machine Learning Framework for Diabetes Prediction 4

An Intelligent Supervised Machine Learning Framework for Dia...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Maenge, Aone Sigwele, Tshiamo Kuthadi, Venumadhav Selvaraj, Rajalakshmi Botswana International University of Science and Technology Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

Estimates suggest that 578 million people could be affected by diabetes mellitus in 2030, with an estimated 700 million affected by 2045 worldwide. Diabetes mellitus (DM) has impacted many people worldwide, with over 422 million affected and about 1.5 million deaths annually. Given the significant global burden of diabetes mellitus and alarming mortality rates, early diagnosis is crucial. Machine learning (ML) techniques offer promising ways for reliably predicting diabetes. This research proposes a machine-learning framework utilizing the random forest algorithm for accurate diabetes prediction. The proposed framework was built using the Hospital Frankfurt, Germany dataset, which included eight independent variables and one target variable. The proposed framework was evaluated and compared to other existing frameworks based on accuracy and AUC metrics for Area Under Curve. The findings reveal that the proposed framework outperformed the reviewed frameworks in this study, with a 99.39% score of accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Designing Everyday Family Technologies: Supporting Health and Wellbeing in Family-Food Interactions with Children 25

Designing Everyday Family Technologies: Supporting Health an...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Wu, Yuxing Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States

ISBN: (纸本)9798400711879

Technology is pervasive in everyday family life. However, creating meaningful sociotechnical systems that are useful for (and desired by) families remains a complex and evolving challenge. To design for families in the digital age, HCI research requires holistic understanding and in-depth investigation to unpack social dynamics and situated use beyond the individual. My dissertation applies a family-centered approach to examine how technologies might be better designed to support everyday social, health, and developmental goals and challenges. Focusing on family-food interactions, my work unpacks complexities in navigating various food-related experiences to inform design implications. © 2025 Copyright held by the owner/author(s).

关键词： Food ingredients

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Deep Dive into Hotel Reviews Analysis Using Convolutional Neural Networks Algorithm

Deep Dive into Hotel Reviews Analysis Using Convolutional Ne...

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2023 International Conference on Data Science and Its Applications, ICoDSA 2023

作者： Sufian, Sofea Aini Mohd Bahrin, Ummu Fatihah Mohd Jantan, Hamidah College of Computing Informatics and Mathematics Kuala Terengganu Malaysia

ISBN: (纸本)9798350305197

Tourism industry has been instrumental in the growth of hotels globally, and the rise of online booking systems has led to a surge in online hotel reviews. Currently, travelers are keen to share their experiences, making online hotel reviews a valuable source of information. However, reading and understanding these reviews can be time-consuming and challenging, as human language is complex, and sentiment analysis requires high accuracy. This paper focuses on the development and evaluation of a sentiment analysis model for hotel reviews using Convolutional Neural Networks (CNN). The objective of the model is to assist hotel staff in gaining insights from online reviews. Traditional sentiment analysis algorithms have limitations and have reached a plateau in their performance. The experiment was conducted on six hotel reviews in Langkawi using Word2vec in Python generates word embeddings, and vector representations of words that enable effective natural language processing. The parameters used in the CNN algorithm, such as batch size, filter size, dropout, and data split between training and testing, were varied that need to be experimented with to find the high accuracy of CNN model. The results of the experiments showed that the CNN model achieved high accuracy of more than 90%, making it an effective text classification method. Moreover, the models with a dropout rate of 0.1 consistently outperformed the models with a higher dropout rate of 0.5 in terms of accuracy. The study used the CNN model to create a prototype for predicting the sentiment of hotel reviews. Results showed that the CNN algorithm is a reliable tool for sentiment analysis in the hotel industry, as it provides high accuracy and valuable insights from online reviews. © 2023 IEEE.

关键词： Hotels

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Optimizing Machine Learning for Breast Cancer Detection by Hybrid Metaheuristic Approach 12

Optimizing Machine Learning for Breast Cancer Detection by H...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Bezdan, Timea Strumberger, Ivana Tuba, Milan Informatics and Computing Singidunum Univeristy Belgrade Serbia Singidunum Univeristy Belgrade Serbia

ISBN: (纸本)9798350330366

Breast cancer ranks as one of the top causes of cancer-related deaths among women worldwide, highlighting the need for improved diagnostic methodologies. Machine learning offers promising solutions;however, the models' performance highly relies on optimal hyperparameter configurations. The goal of this research is to improve the precision of machine learning models in diagnosing breast cancer by employing a novel optimization strategy that integrates the metaheuristic algorithm, namely, the Sine Cosine Algorithm with the Opposition-Based Learning mechanism for hyperparameter tuning. We applied five machine learning classifiers: K-Nearest Neighbors, Decision Trees, Random Forest, Logistic Regression, and Support Vector Machines to the Breast Cancer Wisconsin dataset. The proposed optimization method was utilized to fine-tune the hyperparameters of these classifiers to improve the performance. Model performance was rigorously evaluated using fivefold cross-validation approach and it was compared to existing methods. The optimization process led to significant improvements in model performance, with the enhanced classifiers outperforming their baseline configurations and existing works. The findings highlight the success of the suggested method in navigating the hyperparameter search space, leading to more accurate and reliable breast cancer diagnoses. Integrating the Sine Cosine Algorithm with Opposition-Based Learning for hyperparameter tuning presents a powerful approach to refining the machine learning classifiers for breast cancer diagnosis. This study underscores the potential of advanced optimization methods in improving the diagnostic capabilities of machine learning models, contributing to timely diagnosis and treatment of breast cancer. © 2024 IEEE.

关键词： Decision trees

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