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1.
IEEE Access ; 11:46956-46965, 2023.
Article in English | Scopus | ID: covidwho-20241597

ABSTRACT

Knowledge payment is a new method of electronic learning that has developed in the era of social media. With the impact of the COVID-19 pandemic, the market for knowledge payment is rapidly expanding. Exploring the factors that influence users' sustained willingness is beneficial for better communication between knowledge payment platforms and users, and for achieving a healthier and more sustainable development of the knowledge payment industry. The model of unsustainable usage behavior of knowledge payment users was constructed on the basis of expectation inconsistency theory, price equilibrium theory, and perceived value theory, using the 'cognitive-emotional-behavioral' model framework of cognitive emotion theory. The data were collected from 348 users through a web-based questionnaire and analyzed using structural equation modeling. Findings show that expectation inconsistency, price equilibrium, and quality value, emotional value, and social value have significant effects on discontinuous use intentions. Discontinuous use intentions also significantly affect discontinuous use behavior. © 2013 IEEE.

2.
28th International Conference on Intelligent User Interfaces, IUI 2023 ; : 119-122, 2023.
Article in English | Scopus | ID: covidwho-2303596

ABSTRACT

Social support is known to be a critical factor for mental well-being. More specifically, the protective effect of quality social support in times of crisis is well documented in many psychological studies. In this study, we developed a social support matching system that connects people who are going through similar life circumstances to provide peer-based support, allowing them to better cope with their situation together. As a case study, we focused on Japanese students whose lives were impacted by the COVID-19 lock down. To develop the recommendation model used in our system, 50 participants were asked to register their profile and afterwards, 20 users determined whether they would match with each of the profiles resulting in 1000 data points. We then experimented with various collaborative filtering and deep learning approaches and evaluated their effectiveness in recommending profiles to users. Finally, a user experiment study was conducted in which 11 users used the system 2 weeks. The results showed that while there was no significant difference in perceived social support, users reported significantly less anxiety and a borderline reduction in depression. © 2023 Owner/Author.

3.
56th Annual Hawaii International Conference on System Sciences, HICSS 2023 ; 2023-January:3358-3366, 2023.
Article in English | Scopus | ID: covidwho-2303509

ABSTRACT

Telemedicine has drawn noticeable attention due to the advancement of information technology, and it saw a surge in popularity during the COVID-19 pandemic. This study aims to understand telemedicine users' perceptions of their care services and identify the aspects of telemedicine that can be improved to enhance users' experience and satisfaction. Specifically, we utilized a topic modeling approach with Latent Dirichlet Allocation (LDA) to analyze telemedicine-related discussion posts on Reddit to discover the topics and themes that telemedicine service users are interested in, as well as the perceptions that users have of those topics and themes. 11 topics and 6 themes were discovered by the LDA algorithm. Lastly, we provide our suggestions and insights on how telemedicine services and practitioners can implement the themes, as well as directions for future study. © 2023 IEEE Computer Society. All rights reserved.

4.
2nd International Conference on Information Technology, InCITe 2022 ; 968:583-595, 2023.
Article in English | Scopus | ID: covidwho-2298081

ABSTRACT

In the past few years, technology has changed drastically and due to COVID-19 pandemic, people spend more time on screen. The use of social media platforms has also been increased and this affects the human mind and decision taking ability. Online career counseling is largely supported these days and hence this paper proposes an online career prediction system using supervised machine learning based on the user's profile. This research attempted to develop a model for the user which predicts the career path in a precise manner and gives actionable feedback and career recommendations to encourage them to make significant career judgments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

5.
14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022 ; : 504-507, 2022.
Article in English | Scopus | ID: covidwho-2297219

ABSTRACT

Individuals modify their opinions towards a topic based on their social interactions. Opinion evolution models conceptualize the change of opinion as a uni-dimensional continuum, and the effect of influence is built by the group size, the network structures, or the relations among opinions within the group. However, how to model the personal opinion evolution process under the effect of the online social influence as a function remains unclear. Here, we show that the uni-dimensional continuous user opinions can be represented by compressed high-dimensional word embeddings, and its evolution can be accurately modelled by an ordinary differential equation (ODE) that reflects the social network influencer interactions. We perform our analysis on 87 active users with corresponding influencers on the COVID-19 topic from 2020 to 2022. The regression results demonstrate that 99% of the variation in the quantified opinions can be explained by the way we model the connected opinions from their influencers. Our research on the COVID-19 topic and for the account analysed shows that social media users primarily shift their opinion based on influencers they follow (e.g., model explains for 99% variation) and self-evolution of opinion over a long time scale is limited. © 2022 IEEE.

6.
11th International Conference on Computational Data and Social Networks, CSoNet 2022 ; 13831 LNCS:15-26, 2023.
Article in English | Scopus | ID: covidwho-2278507

ABSTRACT

We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users' protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021;7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022;8412 from 11.12.2021;3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

7.
22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 ; 2022-November:1168-1175, 2022.
Article in English | Scopus | ID: covidwho-2253940

ABSTRACT

Online Social Networks (OSN s) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer's fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events. © 2022 IEEE.

8.
Computers and Security ; 125, 2023.
Article in English | Scopus | ID: covidwho-2244120

ABSTRACT

Many researchers have studied non-expert users' perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users' tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results indicate the multi-faceted nature of non-expert users' perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives. © 2022

9.
Online Information Review ; 47(1):41-58, 2023.
Article in English | Scopus | ID: covidwho-2238535

ABSTRACT

Purpose: The study aimed to examine how different communities concerned with dementia engage and interact on Twitter. Design/methodology/approach: A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored. Findings: Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities. Originality/value: The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208. © 2022, Emerald Publishing Limited.

10.
2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; : 3228-3234, 2022.
Article in English | Scopus | ID: covidwho-2237494

ABSTRACT

Medical Frequently Asked Question (FAQ) retrieval aims to find the most relevant question-answer pairs for a given user query, which is of great significance for enhancing people medical health awareness and knowledge. However, due to medical data privacy and labor-intensive labeling, there is a lack of large-scale question-matching training datasets. Previous methods directly use the collected question-answer pairs on search engines to train retrieval models, which achieved poor performance. Inspired by recent advances in contrastive learning, we propose a novel contrastive curriculum learning framework for modeling user medical queries. First, we design different data augmentation methods to generate positive samples and different types of negative samples. Second, we propose a curriculum learning strategy that associates difficulty levels with negative samples. Through a contrastive learning process from easy to hard, our method achieves excellent results on two medical datasets. © 2022 IEEE.

11.
7th International Conference on Informatics and Computing, ICIC 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2233606

ABSTRACT

According to data from covid19.go.id, there is a lot of hoax news about Covid-19 vaccinations spread across various social media in Indonesia. Meanwhile, the ability to monitor and track misinformation and trends regarding Covid-19 and its spread is an important part of the response process by the media and government to dealing with fake news about Covid-19. Twitter is a social media that is actively used in spreading issues. Twitter users in Indonesia reached 18.45 million users as of January 2022. To find out useful information on Twitter social media comments regarding the Covid-19 Vaccination, a method, namely Topic Modeling, can be used. This study aims to obtain the distribution of the Covid-19 Vaccination topic on Twitter Data in Indonesia to assist the government in knowing the trend of topics related to Covid-19 vaccination and the trend of changing the topic. The dataset on Twitter used is 10,140 pieces about Covid-19 vaccinations in Indonesia in the period August 2021 to April 2022. Based on the Latent Dirichlet Association (LDA), the 5 most popular topics were obtained for each model, and spread in the fields of health, religion, society. Based on the alpha and beta hyperparameter tuning, it was found that the topic with K=5, alpha=asymmetric, and beta=0.61 was a relevant LDA topic model for the research dataset because good in topic diversity and have coherence value=0.590. © 2022 IEEE.

12.
2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022 ; : 3228-3234, 2022.
Article in English | Scopus | ID: covidwho-2223083

ABSTRACT

Medical Frequently Asked Question (FAQ) retrieval aims to find the most relevant question-answer pairs for a given user query, which is of great significance for enhancing people medical health awareness and knowledge. However, due to medical data privacy and labor-intensive labeling, there is a lack of large-scale question-matching training datasets. Previous methods directly use the collected question-answer pairs on search engines to train retrieval models, which achieved poor performance. Inspired by recent advances in contrastive learning, we propose a novel contrastive curriculum learning framework for modeling user medical queries. First, we design different data augmentation methods to generate positive samples and different types of negative samples. Second, we propose a curriculum learning strategy that associates difficulty levels with negative samples. Through a contrastive learning process from easy to hard, our method achieves excellent results on two medical datasets. © 2022 IEEE.

13.
5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection, OSACT 2022 ; : 12-22, 2022.
Article in English | Scopus | ID: covidwho-2167442

ABSTRACT

The spread of misinformation has become a major concern to our society, and social media is one of its main culprits. Evidently, health misinformation related to vaccinations has slowed down global efforts to fight the COVID-19 pandemic. Studies have shown that fake news spreads substantially faster than real news on social media networks. One way to limit this fast dissemination is by assessing information sources in a semi-automatic way. To this end, we aim to identify users who are prone to spread fake news in Arabic Twitter. Such users play an important role in spreading misinformation and identifying them has the potential to control the spread. We construct an Arabic dataset on Twitter users, which consists of 1,546 users, of which 541 are prone to spread fake news (based on our definition). We use features extracted from users' recent tweets, e.g., linguistic, statistical, and profile features, to predict whether they are prone to spread fake news or not. To tackle the classification task, multiple learning models are employed and evaluated. Empirical results reveal promising detection performance, where an F1 score of 0.73 was achieved by the logistic regression model. Moreover, when tested on a benchmark English dataset, our approach has outperformed the current state-of-the-art for this task. © European Language Resources Association (ELRA).

14.
25th International Conference on Discovery Science, DS 2022 ; 13601 LNAI:243-252, 2022.
Article in English | Scopus | ID: covidwho-2148602

ABSTRACT

The Covid-19 pandemic, which required more people to work and learn remotely, emphasized the benefits of online learning. However, these online learning environments, which are typically used on an individual basis, can make it difficult for many to finish courses effectively. At the same time, online learning allows for the monitoring of users, which may help to identify learners who are struggling. In this article, we present the results of a set of experiments focusing on the early prediction of user drop out, based on data from the New Heroes Academy, a learning center providing online courses. For measuring the impact of user behavior over time with respect to user drop out, we build a range of random forest classifiers. Each classifier uses all features, but the feature values are calculated from the day a user starts a course up to a particular day. The target describes whether the user will finish the course or not. Our experimental results (using 10-fold cross-validation) show that the classifiers provide good results (over 90% accuracy from day three with somewhat lower results for the classifiers for day one and two). In particular, the time-based and action-based features have a major impact on the performance, whereas the start-based feature is only important early on (i. e., during day one). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

15.
J Med Internet Res ; 24(11): e39662, 2022 11 16.
Article in English | MEDLINE | ID: covidwho-2118080

ABSTRACT

BACKGROUND: Access to mental health treatment across Canada remains a challenge, with many reporting unmet care needs. National and provincial e-Mental health (eMH) programs have been developed over the past decade across Canada, with many more emerging during COVID-19 in an attempt to reduce barriers related to geography, isolation, transportation, physical disability, and availability. OBJECTIVE: The aim of this study was to identify factors associated with the utilization of eMH services across Canada during the COVID-19 pandemic using Andersen and Newman's framework of health service utilization. METHODS: This study used data gathered from the 2021 Canadian Digital Health Survey, a cross-sectional, web-based survey of 12,052 Canadians aged 16 years and older with internet access. Bivariate associations between the use of eMH services and health service utilization factors (predisposing, enabling, illness level) of survey respondents were assessed using χ2 tests for categorical variables and t tests for the continuous variable. Logistic regression was used to predict the probability of using eMH services given the respondents' predisposing, enabling, and illness-level factors while adjusting for respondents' age and gender. RESULTS: The proportion of eMH service users among survey respondents was small (883/12,052, 7.33%). Results from the logistic regression suggest that users of eMH services were likely to be those with regular family physician access (odds ratio [OR] 1.57, P=.02), living in nonrural communities (OR 1.08, P<.001), having undergraduate (OR 1.40, P=.001) or postgraduate (OR 1.48, P=.003) education, and being eHealth literate (OR 1.05, P<.001). Those with lower eMH usage were less likely to speak English at home (OR 0.06, P<.001). CONCLUSIONS: Our study provides empirical evidence on the impact of individual health utilization factors on the use of eMH among Canadians during the COVID-19 pandemic. Given the opportunities and promise of eMH services in increasing access to care, future digital interventions should both tailor themselves toward users of these services and consider awareness campaigns to reach nonusers. Future research should also focus on understanding the reasons behind the use and nonuse of eMH services.


Subject(s)
COVID-19 , Mental Health Services , Humans , Mental Health , Cross-Sectional Studies , COVID-19/epidemiology , Pandemics , Canada/epidemiology
16.
15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022 ; 13558 LNCS:24-34, 2022.
Article in English | Scopus | ID: covidwho-2059737

ABSTRACT

Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence;(ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure;and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

17.
2nd European Symposium on Usable Security, EuroUSEC 2022 ; : 40-52, 2022.
Article in English | Scopus | ID: covidwho-2053366

ABSTRACT

We conducted 22 semi-structured interviews with participants in the early stages of the COVID-19 pandemic when restrictions were in effect, to learn about social media users' privacy behaviors and what influenced changes in behavior since the beginning of the pandemic. We found that participants felt pressured to stay "relevant"online, which led to increased consumption and sharing of content, as well as increased re-posting of older content. Participants also noted increased disclosure of negative emotional states and that they were expected to publicly display their stance in regards to social movements. Participants felt increasingly reliant on social media as a means of connection which led them to download and install additional social apps despite privacy concerns. Each of these activities has potential privacy implications in terms of explicit data sharing and in terms of increased sources of information for online behavioral tracking and profiling. © 2022 ACM.

18.
IEEE Transactions on Computational Social Systems ; : 1-10, 2022.
Article in English | Scopus | ID: covidwho-1992674

ABSTRACT

Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms. IEEE

19.
30th IEEE/ACM International Symposium on Quality of Service, IWQoS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1992650

ABSTRACT

In this paper, we focus on the quality of information service (QoIS) of COVID-19-related information on social media. Our goal is to provide reliable COVID-19 information service by accurately detecting the misleading COVID-19 posts on social media by exploring the community-contributed COVID-19 fact data (CCFD) from different social media platforms. In particular, CCFD refers to the fact-checking reports that are submitted to each social media platform by its users and fact-checking professionals. Our work is motivated by the observation that CCFD often contains useful COVID-19 knowledge facts (e.g., "COVID-19 is not a flu") that can effectively facilitate the identification of misleading COVID-19 social media posts. However, CCFD is often private to the individual social media platform that owns it due to the data privacy concerns such as data copyright of CCFD and user profile information of CCFD contributors. In this paper, we leverage the CCFD from different social media platforms to accurately detect COVID19 misinformation while effectively protecting the privacy of CCFD. Two critical challenges exist in solving our problem: 1) how to generate privacy-aware COVID-19 knowledge facts from the platform-specific CCFD? 2) How to effectively integrate the privacy-aware COVID-19 knowledge facts from different social media platforms to correctly assess the truthfulness of a COVID19 post? To address these challenges, we develop CoviDKG, a COVID-19 distributed knowledge graph framework that constructs a set of CCFD-based knowledge graphs on individual social media platform and exchanges the privacy-aware COVID19 knowledge facts across different platforms to effectively detect misleading COVID-19 posts. We evaluate CoviDKG on two real-world social media datasets and the results show that CoviDKG achieves significant performance gains compared to state-of-the-art baselines in accurately detecting misleading COVID-19 posts on social media. © 2022 IEEE.

20.
30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022 ; : 29-34, 2022.
Article in English | Scopus | ID: covidwho-1986415

ABSTRACT

Even though the Internet and social media are usually safe and enjoyable, communication through social media also bears risks. For more than ten years, there have been concerns regarding the manipulation of public opinion through the social Web. In particular, misinformation spreading has proven effective in influencing people, their beliefs and behaviors, from swaying opinions on elections to having direct consequences on health during the COVID-19 pandemic. Most techniques in the literature focus on identifying the individual pieces of misinformation or fake news based on a set of stylistic, content-derived features, user profiles or sharing statistics. Recently, those methods have been extended to identify spreaders. However, they are not enough to effectively detect either fake content or the users spreading it. In this context, this paper presents an initial proof of concept of a deep learning model for identifying fake news spreaders in social media, focusing not only on the characteristics of the shared content but also on user interactions and the resulting content propagation tree structures. Although preliminary, an experimental evaluation over COVID-related data showed promising results, significantly outperforming other alternatives in the literature. © 2022 Owner/Author.

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