2022 8th International Conference on Web Research (ICWR)最新文献_第4页

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2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/icwr54782.2022.9786226

引用次数: 0

Iranian National COVID-19 Electronic Screening System: A Mixed-Method Usability Evaluation 伊朗国家COVID-19电子筛查系统:混合方法可用性评估

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786231

Zahra Mohahammadzadeh, A. Nickfarjam, Afshin Babajani, F. Jeddi

{"title":"Iranian National COVID-19 Electronic Screening System: A Mixed-Method Usability Evaluation","authors":"Zahra Mohahammadzadeh, A. Nickfarjam, Afshin Babajani, F. Jeddi","doi":"10.1109/ICWR54782.2022.9786231","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786231","url":null,"abstract":"This study was conducted to determine the mixed-method usability evaluation of the Iranian national covid-19 electronic screening system. The cross-sectional study was carried out in partnership with 116 users of the Iranian national covid-19 electronic screening system and five experts. As a result of the experts’ assessment, the Iranian national covid-19 electronic screening system scored 0-2 out of the 10 principles of Nielsen Jacob, which indicates a good approach to the design of this system. To evaluate, the questionnaire for user interaction satisfaction (QUIS) version 7 was used. Data were analyzed by spss version 19. A total of 112 out of 116 questionnaires were obtained. In the Iranian national covid-19 electronic screening system, nine (33.3%) of the 27 sections scored higher than seven. More than half scored over five. There were no factors in the terminology and system information and learning section between 7 and 9; the highest rankings in the section overall responses to the software were 1) terrible-wonderful 2) difficult-easy; in the section overall reactions to the software, all of the factors were highest; also, the highest rankings were in the section “system capability” for 1) system speed 2) system reliability 3) designed for all levels of users.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129303522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Using Supervised Learning Models for Creating a New Fake News Analysis and Classification of a COVID-19 Dataset: A case study on Covid-19 in Iran 使用监督学习模型创建新的假新闻对COVID-19数据集进行分析和分类:以伊朗COVID-19为例

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786244

Mohammadreza Parvizimosaed, M. Esnaashari, A. Damia, Razieh Bahmanyar

{"title":"Using Supervised Learning Models for Creating a New Fake News Analysis and Classification of a COVID-19 Dataset: A case study on Covid-19 in Iran","authors":"Mohammadreza Parvizimosaed, M. Esnaashari, A. Damia, Razieh Bahmanyar","doi":"10.1109/ICWR54782.2022.9786244","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786244","url":null,"abstract":"Today, the growth of the coronavirus as a pandemic and its global expansion is a significant concern in our society and the international community. However, in recent years, many individuals have shifted their major source of news and information to social networks. Consequently, the widespread dissemination of false and misleading information on social media is significant for most politicians. Our effort is not only against COVID-19 but against an “infodemic” as well. To address this, on COVID-19, we have collected and released a labeled dataset of 7,000 social media postings Persian data, and articles of authentic and false news. Covid 19 fake news has been detected in other languages such as Arabic, English, Chinese, and Hindi. We execute a multi-label task (actual vs. fictitious) on the labeled dataset and compare it to six machine learning baselines: Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. On the test set, the support vector machine gives us the best results, with an 89 percent accuracy rate.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132423362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Event Detection in Twitter by Weighting Tweet’s Features 加权推文特征的推特事件检测

2022 8th International Conference on Web Research (ICWR) Pub Date : 2020-10-01 DOI: 10.1109/ICWR54782.2022.9786242

Parinaz Rahimizadeh, M. Shayegan

{"title":"Event Detection in Twitter by Weighting Tweet’s Features","authors":"Parinaz Rahimizadeh, M. Shayegan","doi":"10.1109/ICWR54782.2022.9786242","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786242","url":null,"abstract":"In recent years, people spend much time on social networks. They use social networks as a place to comment on personal or public events. Thus, a large amount of information is generated and shared daily in these networks. Such a massive amount of information help authorities to accurately and timely monitor and react to events. This unique specification prevents further damages, especially when a crisis occurs. Thus, event detection is attracting considerable interest among social networks research. Since Twitter is one of the most popular social networks that potentially prepare an appropriate bed for event detection, this study has been conducted on Twitter. The main idea of this research is to differentiate among tweets based on some of their features. For this purpose, the proposed methodology applies weights to the three features, including the followers’ count, the retweets count, and the user location. The event detection performance is evaluated by scoring potential clusters based on weighting the three mentioned features. The results show that the average execution time and the precision of event detection in the proposed approach have been improved by 27% and 31%, respectively, in comparison to the base method. Another result of this research is detecting more events (including hot events and less important ones) in the presented method.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117300411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0