Twitter Data Warehouse and Business Intelligence Using Dimensional Model and Data Mining

Sentiment analysis is used to analyze data in text format such as tweets from Twitter social media users. Twitter is one of the most popular social media with more than half a billion users and can generate large volumes of data. It is difficult to operate large-scale data, so the data warehouse can be used as a data storage area that allows the operation of large-scale data. The final step of data warehousing is the application of business intelligence. This research uses dimensional model approach to build data warehouse from Twitter and uses lexicon-based approach to analyze public opinion of Twitter users toward covid-19 vaccine in Indonesia. The results show that the creation of a data warehouse and sentiment analysis have been successfully carried out based on the evaluation of the data warehouse and sentiment analysis. The evaluation carried out on the data warehouse is to examine the components of the dimensional model based on indicators and dimensional modeling rules by Kimball. While the evaluation carried out on sentiment analysis is the confusion matrix with the result of the accuracy of sentiment analysis is 74%. © 2022 IEEE.

An Evidence Study of Long-term Impacts on Mobility Patterns Brought by COVID-19

The outbreak of COVID-19 at the end of 2019 has posed an enormous threat to people's physical and psychological health, especially those who are infected during the epidemic. Understanding how the infected people behaved during the pandemic and whether long-term effects are exerted even after they were cured is essential for guiding them to conduct a more comprehensive recovery. Large scale crowd-sourced data provides a chance to investigate their behavior patterns. In this paper, we explore the possible differences in mobility patterns between the infected and the uninfected, relying on a large volume of crowd -sourced location data contributed by smartphone users consisting of 11,414 infected cases and 12,793 uninfected people between Jun. 1, 2019 and Dec 31, 2020 in Wuhan, China. We characterize mobility distinctions of the two groups by introducing five mobility indicators that accurately capture spatio-temporal patterns of human mobility. We reveal that the infected kept higher mobility level during the pandemic. Moreover, the COVID-19 caused lower recovery efficiency on mobility of the infected, including later recovery time, lower speed and worse status. © 2022 IEEE.

Mobility of Indonesian during Early Pandemic: Insights from Mobile Positioning Data

Mobile Positioning Data (MPD) contains information on the location of the mobile phone by approximating mobile phones' location relative to fixed infrastructures (e.g., telecommunication towers that transmit signals). While the data query is technically straightforward, obtaining this dataset requires particular permission to protect customers' privacy. Additionally, the dataset has large volumes of data (i.e, up to 300GB per day), resulting in not many researchers holding this data source to analyze the mobility of people. In this work, we collaborate with one of the biggest telecommunication service providers in Indonesia to collect MPD and prepare the big data infrastructure. We thus analyze mobility patterns during the early phase of COVID-19 in 2020 using actual Mobile Positioning Data in five provinces in Java. We use three metrics, namely, the number of visits, averaged travel distance, and Origin-Destination matrix. The findings indicate that the social restriction in the corresponding provinces has reduced the average traveled distance of the people, but not their number of visits. That is, while the traveled distance has declined more than eight times compared to the baseline, the number of visits may rocket up, up to nine times. It indicates that people are still having shorter trips even though their regular activities (working, schooling, etc.) have been restricted. The data also show that during Ramadhan month, the government has a successful intervention in restricting people for mudik Lebaran, The number of visits dropped to below 30 visits during Ramadhan and only small spikes exist during 'libur lebaran'. © 2022 IEEE.

CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets

Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose a real danger to people's health, particularly when false information is shared, for instance recommendations on how to treat diseases without professional medical advice. Therefore, automatic fact-checking resources and systems developed specifically for the medical domain are crucial. While existing fact-checking resources cover COVID-19-related information in news or quantify the amount of misinformation in tweets, there is no dataset providing fact-checked COVID-19-related Twitter posts with detailed annotations for biomedical entities, relations and relevant evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19-related (mis)information. The corpus consists of 300 tweets, each annotated with medical named entities and relations. We employ a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in moderate inter-annotator agreement. Furthermore, we use the retrieved evidence extracts as part of a fact-checking pipeline, finding that the real-world evidence is more useful than the knowledge indirectly available in pretrained language models. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

Visualization of the Social Atmosphere Using Comments on News Sites

When an emergency such as an infectious disease or natural disaster occurs, a negative atmosphere will usually spread throughout society—increasing people’s dissatisfaction and anxiety. Because of this, it is rather difficult to thoroughly investigate the actual situation. However, people can post sentimental comments on news sites, allowing for their attitudes either for or against the topics to be better observed. This study extracts the positive, negative, and neutral comments by using sentiment analysis. Then, the social atmosphere is visualized by calculating the approval rating of the comments. This methodology is demonstrated in articles regarding COVID-19. The large volume of comments about two topics, Go To campaigns and PCR tests, were analyzed by using ML-Ask to classify the comments into three categories: negative, positive, and neutral. The results indicate that the social atmosphere about the Go To campaigns tended to be negative. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Paper Smart Cities data analysis with Power BI and R

Currently the transformation of a city involves the participation of users through all media and especially through communication networks and social networks. Right now, the pandemic waves of COVID-19, and the phases of confinement, oriented users to communicate through social networks specifically Twitter in which they shared their feelings, and the behavior of their situation in the face of the pandemic. When talking about sharing sentiment information on social networks we are talking about a large volume of information that is posted on the networks that can be processed and analyzed using technological tools including RStudio to collect and process;and Power BI to analyze and visualize. The methodology presented has been the result of a process of investigation of related works focused on the participations of the users of a city in Ecuador downloading data from the Social Network Twitter. The methodology is composed of the phases of collecting, storing, transforming, analyzing and visualizing, and the development and execution of the proposal, leaving solid foundations for the implementation of an intelligent campus prototype that combines technological tools and the analysis of information that supports decision making and information analysis. © 2022 IEEE.

Dynamic Terminology Integration for COVID-19 and other Emerging Domains

The majority of language domains require prudent use of terminology to ensure clarity and adequacy of information conveyed. While the correct use of terminology for some languages and domains can be achieved by adapting general-purpose MT systems on large volumes of in-domain parallel data, such quantities of domain-specific data are seldom available for less-resourced languages and niche domains. Furthermore, as exemplified by COVID-19 recently, no domain-specific parallel data is readily available for emerging domains. However, the gravity of this recent calamity created a high demand for reliable translation of critical information regarding pandemic and infection prevention. This work is part of WMT2021 Shared Task: Machine Translation using Terminologies, where we describe Tilde MT systems that are capable of dynamic terminology integration at the time of translation. Our systems achieve up to 94% COVID-19 term use accuracy on the test set of the EN-FR language pair without having access to any form of in-domain information during system training. We conclude our work with a broader discussion considering the Shared Task itself and terminology translation in MT. © 2021 Association for Computational Linguistics

Privacy Dilemma in Healthcare: A Review on Privacy Preserving Medical Internet of Things

In recent years, the Medical Internet of Things or well known as MIoT has attracted so much attentional from researchers, academia for its potential to alleviate the burden on global healthcare caused by the shortage of medical staff, chronic diseases, and deadly contagious diseases which quickly turned into global pandemics like recent COVID-19. It is believed that soon, MIoT will become the next healthcare enabler contributing to all major aspects of healthcare. Day by day the number of MIoT devices in healthcare growing with a rapid phase and this also will lead to a massive increase of data that the devices are generating. This large volume of data and the devices themselves are posing huge questions about the security and the privacy of the entire MIoT ecosystem. As of now, the cyber-attacks that target healthcare are also increasing with a rapid phase, where comprehensive cyber-attacks that exploit the privacy of MIoT data and devices may even lead to jeopardizing the security of the entire ecosystem and may cause loss of patient lives if no countermeasures are taken. Hence the privacy of MIoT in healthcare is becoming a major threat where no adequate research has not been done so far. With this regard, in this study, we aim to review the privacy aspect of this MIoT and privacy-preserving solutions for MIoT along with the challenges pertaining to the discipline with the anticipated future directions. © 2021 IEEE.

Effects of COVID-19 on individuals in Opioid Addiction Recovery

Opioid Use Disorder (OUD) is one of the most severe health care problems in the USA. People addicted to opioids need various treatments, including Medication-Assisted Treatment (MAT), proper counseling, and behavioral therapies. However, during the peak time of the COVID-19 pandemic, the supply of emergency medications was disrupted seriously. Patients faced severe medical care scarcity since many pharmaceutical companies, drugstores, and local pharmacies were closed. Import-export was also canceled to consent to the government emergency law, i.e., lockdown, quarantine, and isolation. These circumstances and their negative effects on OUD patient's psychology could have led them to a drop out of MAT medications and persuaded to resume illicit opioid use. This project involves collecting and analyzing a large volume of Twitter data related to MAT medications for OUD patients. We discover the Active MAT Medicine Users (AMMUs) on twitter. For this, we build a seed dictionary of words related to OUD and MAT and apply association rules to expand it. Further, AMMUs' tweet posts are studied 'before the pandemic' (BP) and 'during the pandemic' (DP) to understand how the drug behaviors and habits have changed due to COVID-19. We also perform sentiment analysis on Tweets to determine the impact of the COVID-19 pandemic on the psychology of AMMUs. Our analysis shows that the use of MAT medications has decreased around 30.54%, where the use of illicit drugs and other prescription opioids increased 18.06% and 12.12%, respectively, based on AMMUs' tweets posted during the lockdown compared with before the lockdown statistics. The COVID-19 pandemic and lockdown may result in the resumption of illegal and prescription opioid abuse by OUD patients. Necessary steps and precautions should be taken by health care providers to ensure the emergency supply of medicines and also psychological support and thus prevent patients from illicit opioid use. © 2021 IEEE.

Trusted Compliance Enforcement Framework for Sharing Health Big Data

COVID pandemic management via contact tracing and vaccine distribution has resulted in a large volume and high velocity of Health-related data being collected and exchanged among various healthcare providers, regulatory and government agencies, and people. This unprecedented sharing of sensitive health-related Big Data has raised technical challenges of ensuring robust data exchange while adhering to security and privacy regulations. We have developed a semantically rich and trusted Compliance Enforcement Framework for sharing large velocity Health datasets. This framework, built using Semantic Web technologies, defines a Trust Score for each participant in the data exchange process and includes ontologies combined with policy reasoners that ensure data access complies with health regulations, like Health Insurance Portability and Accountability Act (HIPAA). We have validated our framework by applying it to the Centers for Disease Control and Prevention (CDC) Contact Tracing Use case by exchanging over 1 million synthetic contact tracing records. This paper presents our framework in detail, along with the validation results against Contact Tracing data exchange. This framework can be used by all entities who need to exchange high velocity-sensitive data while ensuring real-time compliance with data regulations. © 2021 IEEE.

Using Natural Language Processing to Facilitate Student Feedback Analysis

This research paper compares the results of a novel computer-assisted approach for analyzing a large volume of open-ended responses with those of a more traditional open coding approach. The work is motivated by the observation that in engineering education ecosystems, community members produce text through myriad activities both inside and outside of the classroom in teaching and research settings. In many of these cases, there is an abundance of text available to educators and researchers that could provide insight into various phenomena of interest within the system - student conceptual understanding, student experiences outside the classroom, how instructors can improve their teaching, or even shifts in collective conversations. Unfortunately, while these bodies of text have the potential to provide novel insights to educators and researchers, traditional analysis techniques do not scale well. For example, analyzing larger amounts of text can take one grader or researcher significantly more time than grading a small set of text responses. A larger body of text also creates more challenges for intrarater reliability. Likewise, expanding the size of the grading or research team can create interrater reliability challenges and the possibility of bias. To address this opportunity, we have created a natural language processing system that augments human analysis so as to facilitate and enhance the work of one person (or team). Specifically, we take minimally pre-processed text, embed them using a pre-trained transformer (a specific kind of neural network architecture trained to encode inputs and decode outputs), and perform a sequence of dimension reduction techniques capped with a final clustering step. Such a system can help reduce the amount of time needed to analyze the text by effectively running a first pass on the text to group similar responses together. The human user can utilize these groupings to perform further analysis to fine tune and identify meanings in ways that only a human could. The system also can help improve consistency by analyzing across the entire collection of texts simultaneously and grouping similar items together. This is in contrast with a single person or a team that would have to work in series, analyzing responses sequentially and thereby creating the potential for inconsistencies across time. In this paper we describe the system's architecture and data processing steps. We demonstrate the utility of this approach by applying the method on three questions from an end-of-semester feedback survey in a large, required introductory engineering course. The survey questions were part of a general feedback survey and asked students about their experiences in the transition to online learning subsequent to the SARS-CoV-2 outbreak.. Our results suggest that the pre-analysis text clustering can improve speed and accuracy of coding when compared with unassisted human coding-the system augments what we have traditionally done in coding, grading, or making sense of large quantities of textual data. As natural language processing techniques continue to develop, the engineering education research community should continue to explore potential applications to improve understanding and sensemaking from large volumes of underutilized text data from both within and outside of classroom settings. © American Society for Engineering Education, 2021