ABSTRACT
Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach. © 2023 ACM.
ABSTRACT
Almost all educational institutions have shifted their academic activities to digital platforms due to the recent COVID-19 epidemic. Because of this, it is very important to assess how well teachers are performing with this new way of online teaching. Educational Data Mining (EDM) is a new field that emerged from using data mining techniques to analyze educational data and making decision based on findings. EDM can be utilized to gain better understanding about students and their learning processes, assist teachers do their academic tasks, and make judgments about how to manage educational system. The primary objective of this study is to uncover the key factors that influence the quality of teaching in a virtual classroom environment. Data is gathered from the students' evaluation of teaching from computer science students of three online semesters at X University. In total, 27622 students participated in these survey. Weka, sentimental analysis, and word cloud generator are applied in the process of carrying out the research. The decision tree classifies the factors affecting the performance of the teachers, and we find that student-faculty relation is the most prominent factor for improving the teaching quality. The sentimental analysis reveals that around 78% of opinions are positive and "good” is the most frequently used word in the opinions. If the education system is moved online in the future, this research will help figure out what needs to be changed to improve teachers' overall performance and the quality of their teaching.
ABSTRACT
When an individual with confirmed or suspected COVID-19 is quarantined or isolated, the virus can linger for up to an hour in the air. We developed a mathematical model for COVID-19 by adding the point where a person becomes infectious and begins to show symptoms of COVID-19 after being exposed to an infected environment or the surrounding air. It was proven that the proposed stochastic COVID-19 model is biologically well-justifiable by showing the existence, uniqueness, and positivity of the solution. We also explored the model for a unique global solution and derived the necessary conditions for the persistence and extinction of the COVID-19 epidemic. For the persistence of the disease, we observed that Rs0>1, and it was noticed that, for Rs<1, the COVID-19 infection will tend to eliminate itself from the population. Supplementary graphs representing the solutions of the model were produced to justify the obtained results based on the analysis. This study has the potential to establish a strong theoretical basis for the understanding of infectious diseases that re-emerge frequently. Our work was also intended to provide general techniques for developing the Lyapunov functions that will help the readers explore the stationary distribution of stochastic models having perturbations of the nonlinear type in particular.
ABSTRACT
Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%;with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words.
ABSTRACT
This is a comparative study of official diplomatic speeches regarding COVID-19, released by spokespersons for the Ministry of Foreign Affairs of the People's Republic of China (PRC) and documents from the United States Department of State China Archive. It explores how these speeches and documents reflect the US–China relations and the conduct of policies surrounding digital media in the two countries. We focus on the period from the start of the Wuhan lockdown, 20 January 2020, to the city's reopening on 8 April, and use several forms of content analysis to analyse the documents: Latent Dirichlet Allocation (LDA) topic modelling, sentiment network analysis and word clouds. We argue that the diplomatic relationship and political ideologies adopted by different political and media systems can have a major impact upon media policy implementation and guidance.
ABSTRACT
Since Covid-19 there have been many changes in the order of people's lives, there are various new regulations such as the obligation to wear masks when leaving the house, maintaining distance, working from home, or restrictions on leaving the house. These social restrictions have an impact on increasing the volume of plastic waste due to online transactions. This problem causes environmental damage, such as what happened in Gorontalo waters between Hulonthalangi and Dumbo Raya sub-districts, Gorontalo City, polluted with microplastics, the food chain in Gorontalo waters is automatically contaminated with microplastics from the research results of the Nusantara River Expeditionary Team in collaboration with the Institute for Humanities and Development Studies in October 2022. Based on this, campaign actions need to be taken. This Final Project with the title "Designing Motion Graphics as a Zero Waste Lifestyle Campaign Media for the Indonesian People” aims to assist the government's role in preventing environmental damage due to waste and to provide information, education, and an invitation to the target audience to participate. In the success of this campaign. Motion graphic design as a media campaign applies a mixed media art style as a visual representation of graphic elements, typeface, and layouts.
ABSTRACT
We propose a theoretical study to investigate the spread of the SARS-CoV-2 virus, reported in Wuhan, China. We develop a mathematical model based on the characteristic of the disease and then use fractional calculus to fractionalize it. We use the Caputo-Fabrizio operator for this purpose. We prove that the considered model has positive and bounded solutions. We calculate the threshold quantity of the proposed model and discuss its sensitivity analysis to find the role of every epidemic parameter and the relative impact on disease transmission. The threshold quantity (reproductive number) is used to discuss the steady states of the proposed model and to find that the proposed epidemic model is stable asymptotically under some constraints. Both the global and local properties of the proposed model will be performed with the help of the mean value theorem, Barbalat’s lemma, and linearization. To support our analytical findings, we draw some numerical simulations to verify with graphical representations.
ABSTRACT
This work aims to show a theoretical model of community-based tourism, to explain its component subsystems, to provide its theoretical–methodological foundation and to discuss the indications of its practical instrumentation in facing the changes that tourism of the future imposes and will impose. The research was carried out in the tourist context of Ecuador, for which the deductive method was applied, which allowed for examining the problem, and the more general theories related to tourist activity, which allowed for identifying the premises and objectives of the work to reach accurate conclusions on the subject studied. This was a mixed investigation that allowed for integrating the contributions of qualitative and quantitative analyses in the treatment and processing of information. The results included achieving systematization of the theoretical models linked to community-based tourism and, from a practical point of view, obtaining a new model of community-based tourism, a graphic representation of the subsystems that form this model, and its arguments. The findings show the need to update the community-based tourism model as a contribution to the scientific development of tourism as well as the systemic nature of its components from a new perspective of analysis that considers the need for changes as a developmental factor.
ABSTRACT
The current economic environment characterized by the implementation of new ICT technologies, globalization, and the pandemic period has determined the growth of online communication, the development of the e-commerce sector, and the change in online consumer behavior. The research aims to analyze online Romanian consumer behavior trends and perspectives. In order to observe the current position of Romanian online commerce, a comparison was made between the Romanian e-commerce market and three other e-commerce groups: the average for EU-27 countries, the group of four countries with the highest e-commerce values (called 4gc—Denmark, the Netherlands, Germany, and Norway) and the country with the lowest values in e-commerce. A comparison was made using mathematical simulation to predict the potential of e-commerce in the future and identify possible risks. Based on the simulation, the results show that the Romanian e-commerce market can continue to grow, becoming mature, and will provide opportunities for sustainable growth. In order to observe and analyze a possible future for 2021–2026, the regression function, correlation matrix, time series analysis, variable maximization, and QM for the Windows program have been implemented. The graphical representation indicates a positive and growing forecasted future trend for Romanian e-commerce.
ABSTRACT
SARS-CoV-2, or more popularly known COVID-19 has claimed more than 5.5 million lives since it has been declared as a global pandemic. Similar to other viruses, COVID-19 is also undergoing several mutations and has many variants like Alpha, Beta, Gamma, Delta, Omicron and others. With so many variants, social media users are confused and posting their frustrations and angers with Tweets or Posts in public social media platforms. These publicly accessible social media posts provide a wealth of information for a social scientist or political leader or a strategic decision maker. This study demonstrates a feasible approach to extract meaningful critical information from social media posts. By programmatically accessing Twitter database from 11th January 2022 till 20th January 2022, we retrieved almost 9 K Tweet messages on 6 different keywords like “COVID Variants”, “Omicron”, “Alpha Variant”, “Beta Variant”, “Gamma Variant” and “Delta Variant”. Results were compared against metrics like users, posts, engagement, and influence. Omicron was found to be the most popular topic compared to other variants with an influence score of 70.2 million and 2.1 K posts during the monitored period. The most popular sources for influences on COVID-19 Variant related posts were found to be @reuters with 24.2M, @forbes with 17.4M, @timesofindia with 14.2M and @inquirerdotnet with 3.4 followers. This study also found out that the most popular Tweet languages were English followed by French and Dutch. Lastly, this study ranked user mentions, word frequency (with word cloud) and hashtags for COVID-19 Variant related twitter posts during the monitored timeframe.
ABSTRACT
SAR-CoV-2 is now spreading around the world, resulting in increased hospitalization and catastrophic fatality. The genome for coronavirus disease is vulnerable to abnormalities, which leads to genetic distortion and immunity loss. A novel variant of concern (VoC) with a new mutation having Pango lineage B.1.1.529, namely Omicron by WHO, was first found in South Africa at the end of November 2021. As of date, this new variant has already been spread rapidly in more than 58 countries and no doubt including India. In this work, Exploratory Data Analysis (EDA) analysis has been taken on different types of Covid-19 variants to date, where Omicron has demonstrated to be more increased transmissibility and infectious as compared to other variants. EDA offers several graphical representations to a better comprehension of the data and generates statistics for numerical data present in the dataset, as of 6th December 2021. Starting from 2nd December 2021 India has reported 23 new omicron cases within four days, which is a major challenge both for the doctors and government. Moreover, the EDA technique has been carried out for finding a significant correlation with the total number of Omicron cases as of the date in India using a scatter plot. Also, a conceptual design has been configured in this project that describes the whole process of how EDA analysis has been carried out and a Treemap that looks forward to outliers in all countries representing more than twenty-five covid-19 variants. © 2022 IEEE.
ABSTRACT
The development of social media has provided open and convenient platforms for people to express their opinions, which leads to rumors being circulated. Therefore, detecting rumors from massive information becomes particularly essential. Previous methods for rumor detection focused on mining features from content and propagation patterns but neglected the dynamic features with joint content and propagation pattern. In this paper, we propose a novel heterogeneous GCN-based method for dynamic rumor detection (HDGCN), mainly composed of a joint content and propagation module and an ODE-based dynamic module. The joint content and propagation module constructs a content-propagation heterogeneous graph to obtain rumor representations by mining and discovering the interaction between post content and propagation structures in the rumor propagation process. The ODE-based dynamic module leverages a GCN integrated with an ordinary differential system to explore dynamic features of heterogeneous graphs. To evaluate the performance of our proposed HDGCN model, we have conducted extensive experiments on two real-world datasets from Twitter. The results of our proposed model have outperformed the mainstream model.
ABSTRACT
The study aims to demonstrate how health seeking is reflected in users’ posts and comments in Covid-19-themed groups on Facebook as a social media platform, under the conditions of isolation, lockdown and social distance in the Covid-19 process. In this scope, the study examined Facebook groups: The Coronavirus (Covid-19) Information Exchange and Cooperation-Turkey, The Covid-19 Information Sharing Platform, and The Information and Solidarity Platform for Those Diagnosed with Covid-19 Turkey. 600 posts and comments were selected using the random sampling technique and analysed using descriptive method content analysis technique. This was conducted through a coding layout developed by the researchers based on field research (92.95% reliability) and showed that the participants mainly shared emotional support posts and comments that included good wishes/consolation/prayer/thanks (35.3%) and asked questions about the Covid-19 experience/treatment (41.3%) process for information. The posts differed by gender and 86.3% of them received comments. Finally, a word cloud was created using Python, and the overall distribution of the posts and comments was determined. The study demonstrates that digital channels and social networks are used for emotional support and as a source in seeking health information despite having problems such as causing the paradox of fear, issues of confidentiality, and a low confidence level.Alternate : Çalışmanın temel amacı Koronavirüs (Covid-19) sürecinde izolasyon, karantina ve sosyal mesafe şartlarında sağlık arama davranışı gösteren kişilerin, Facebook’un Covid-19 konulu gruplarında yaptıkları paylaşım ve yorumlarına sağlık arayışının nasıl yansıdığını ortaya koymaktır. Bu kapsamda Facebook’ta Corona virüsü (Covid-19) Bilgi Paylaşımı ve Yardımlaşma-Türkiye, Covid-19 Bilgi Paylaşım Platformu ve Covid-19 Yakalananlar Bilgi ve Birlik Platformu Türkiye sosyal medya grupları araştırmaya dâhil edilmiştir. Gruplardan 07 Ekim 2020 ve 07 Ocak 2021 tarihleri arasından basit rastlantısal örneklem tekniğiyle seçilen 600 paylaşım ve yorum betimsel yöntem ile içerik analizi tekniği kullanılarak incelenmiştir. Alanyazın taramasına dayanarak araştırmacılarca oluşturulan kodlama cetveli ile (güvenilirlik %92,95 olarak) paylaşım ve yorumlar analiz edilmiş analiz sonucunda katılımcıların ağırlıklı olarak iyi dilekler/teselli/dua/teşekkür (%35,3) içeren paylaşım ve yorumlarda bulunduğu, Covid-19’un deneyim/tedavi süreci hakkında soru sorarak (%41,3) diğer katılımcılardan bilgi talep ettiği belirlenmiştir. Elde edilen bulgulara göre paylaşım ve yorumlar cinsiyete göre farklılık göstermektedir, bulguların %86,3’ünü yorumlar oluşturmaktadır. Çalışmanın sonunda Python programlama dili kullanılarak kelime bulutu oluşturulmuş paylaşım ve yorumların genel dağılımı belirlenmiştir. Çalışmada bu anlamda sosyal ağların, sağlık bilgisi arama ve korku paradoksu, gizlilik, düşük güven düzeyi gibi sorunsallarına rağmen duygusal destek ve bilgiye ulaşmada bir kaynak olarak kullanıldığı ortaya konmaktadır.
ABSTRACT
Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data.
ABSTRACT
The combat against fake news and disinformation is an ongoing, multi-faceted task for researchers in social media and social networks domains, which comprises not only the detection of false facts in published content but also the detection of accountability mechanisms that keep a record of the trustfulness of sources that generate news and, lately, of the networks that deliberately distribute fake information. In the direction of detecting and handling organized disinformation networks, major social media and social networking sites are currently developing strategies and mechanisms to block such attempts. The role of machine learning techniques, especially neural networks, is crucial in this task. The current work focuses on the popular and promising graph representation techniques and performs a survey of the works that employ Graph Convolutional Networks (GCNs) to the task of detecting fake news, fake accounts and rumors that spread in social networks. It also highlights the available benchmark datasets employed in current research for validating the performance of the proposed methods. This work is a comprehensive survey of the use of GCNs in the combat against fake news and aims to be an ideal starting point for future researchers in the field.
ABSTRACT
Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.
ABSTRACT
The entire world is spreading of coronavirus-COVID-19 has increased exponentially across the globe, and still, no vaccine is available for the treatment of patients. The crowd has grown tremendously in the hospitals where the facilities are minimal. The queue theory is applied for the Single-server system and its self-similarity existence in a queue used to identify the queue time, waiting time, and Hurst parameter by different patient arrivals methods Health care center in our local area located in Hosapete, Ballari district, Karnataka. Due to more arrivals to the health care center for the identification and confirmation of disease covid-19. This study paper presents a sequential queuing model for estimating infections' detection and identification in severe loading conditions. The goal is to offer a simplified probabilistic model to determine the general behavior to predict how long the treatment cycle will diagnose and classify people already tested and get negative or positive results. For this type of Method, there are some graphical representations of the various measurement criteria. The modelling results showed that the patient's waiting period in the course of inquiries, detections, detecting, or treating COVID-19 in the event of imbalances in the system as a whole rise following the logarithm rule.
ABSTRACT
We present the real-world public sentiment expressed on Twitter using the proposed conceptual model (CM) to visualize the communication service providers (CSP) reputation during the Covid-19 pandemic in Malaysia from March 18 until August 18, 2020. The CM is a guideline that entails public tweets directly or indirectly mentioned to the three biggest CSP in Malaysia: Celcom, Maxis, and Digi. A text classifier model optimized for short snippets like tweets is developed to make bilingual sentiment analysis possible. The two languages explored are Bahasa Malaysia and English since they are the two most spoken languages in Malaysia. The classifier model is trained and tested on a huge multidomain dataset pre-labeled with the labels "0" and "1", which resemble "positive" and "negative", respectively. We used the Naive Bayes (NB) technique as the core of the classifier model. Functionality testing has done to ensure no significant error that will render the application useless, and the accuracy testing score of 89% is considered quite impressive. We came out with the visualization through the word clouds and presented -56%, -42%, and -43% of Net Brand Reputation for Celcom, Maxis, and Digi.
ABSTRACT
Novel Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people who fall sick with this disease will experience moderate symptoms and recover without any special treatment but those who are having respiratory and cardio diseases may affect the infections in a serious way. The infection was seen in Kerala by the end of February, by the end of March Kerala Government declared lockdown for keeping the people more safe. Lockdown was started for 7 days and government extended it to end of April 2020. Gradually the government give relaxation on lockdown. Different sectors were affected in this pandemic followed by lockdown. Migrants employees were inattentive group of people those who were belongs to less privileged category moved from different parts of the country for their livelihood ,movement of people from the place of birth to anywhere to reside and settle can be called as 'migration'1. According to IOM2 "migration is the movement of a person or a group of persons either across an international boarder or within a country/state". The process of migration is as old as human history as it pave the way for forming different civilizations and surely, it will continue till human life exists. This study tries to analyze how the migrants face the financial issues in the time of lockdown 2020, it also tries to find out the socio-economic background of the in-migrant workers. Thus to explore the reasons behind their migration to Kerala and their working atmosphere, heterogeneity among them in terms of place of origin, age groups, culture, educational standards, their consumption pattern, improvements in earnings, savings, living standards etc. Therefore this study stands relevant and timely in the light of above mentioned dimensions. The economic conditions of the migrant workers in Ernakulam district at the time of lockdown, sample collected was 134 migrants analysis of statistical data we use SPSS 22 version. Used Excel for graphical representation of the data,mainly carryout statistical test such as: Paired t test& Cross tabulation.Descriptive design with quantitative method is used._
ABSTRACT
The rising concentration of global atmospheric carbon dioxide (CO2) has severely affected our planet’s homeostasis. Efforts are being made worldwide to curb carbon dioxide emissions, but there is still no strategy or technology available to date that is widely accepted. Two basic strategies are employed for reducing CO2 emissions, viz. (i) a decrease in fossil fuel use, and increased use of renewable energy sources;and (ii) carbon sequestration by various biological, chemical, or physical methods. This review has explored microalgae’s role in carbon sequestration, the physiological apparatus, with special emphasis on the carbon concentration mechanism (CCM). A CCM is a specialized mechanism of microalgae. In this process, a sub-cellular organelle known as pyrenoid, containing a high concentration of Ribulose-1,5-bisphosphate carboxylase-oxygenase (Rubisco), helps in the fixation of CO2. One type of carbon concentration mechanism in Chlamydomonas reinhardtii and the association of pyrenoid tubules with thylakoids membrane is represented through a typical graphical model. Various environmental factors influencing carbon sequestration in microalgae and associated techno-economic challenges are analyzed critically.