Forecasting and Prevention Mechanisms Using Social Media in Health Care

Koukaras, Paraskevas; Rousidis, Dimitrios; Tjortjis, Christos

doi:10.1007/978-3-662-61114-2_8

Cited by 7 publications

(4 citation statements)

References 53 publications

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“…This paper presents ongoing research related with the use of SM as a source for information retrieval and forecasting with the aid of DM techniques [ 14 – 17 ]. The next step of the project is to incorporate the use of graph DB (Neo4j) to provide a forecasting mechanism in healthcare [ 18 ]. The framework to be created will take into account more than 30 different parameters such as population characteristics (gender percentages, life expectancy, density, etc.…”

Section: Discussion – Future Workmentioning

confidence: 99%

Examination of NoSQL Transition and Data Mining Capabilities

Rousidis

Koukaras

Tjortjis

2021

Metadata and Semantic Research

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An estimated 2.5 quintillion bytes of data are created every day. This data explosion, along with new datatypes, objects, and the wide usage of social media networks, with an estimated 3.8 billion users worldwide, make the exploitation and manipulation of data by relational databases, cumbersome and problematic. NoSQL databases introduce new capabilities aiming at improving the functionalities offered by traditional SQL DBMS. This paper elaborates on ongoing research regarding NoSQL, focusing on the background behind their development, their basic characteristics, their categorization and the noticeable increase in popularity. Functional advantages and data mining capabilities that come with the usage of graph databases are also presented. Common data mining tasks with graphs are presented, facilitating implementation, as well as efficiency. The aim is to highlight concepts necessary for incorporating data mining techniques and graph database functionalities, eventually proposing an analytical framework offering a plethora of domain specific analytics. For example, a virus outbreak analytics framework allowing health and government officials to make appropriate decisions.

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Section: Discussion – Future Workmentioning

confidence: 99%

Examination of NoSQL Transition and Data Mining Capabilities

Rousidis

Koukaras

Tjortjis

2021

Metadata and Semantic Research

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“…This work envisioned the generation of a decision support system that takes into consideration SMs' data to decide about healthcare-/medical-related problems [38], such as epidemics. It combined more than 30 different parameters like population characteristics (gender, life expectancy, age distribution, and more), indexes (economic, medical, and more), and healthcare dynamics like Intensive Care Units (ICUs), medical staff per capita, and vaccination programs.…”

Section: Future Workmentioning

confidence: 99%

Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis towards COVID-19 Cases and Deaths

Koukaras,

Rousidis,

Tjortjis

2023

Informatics

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The identification and analysis of sentiment polarity in microblog data has drawn increased attention. Researchers and practitioners attempt to extract knowledge by evaluating public sentiment in response to global events. This study aimed to evaluate public attitudes towards the spread of COVID-19 by performing sentiment analysis on over 2.1 million tweets in English. The implications included the generation of insights for timely disease outbreak prediction and assertions regarding worldwide events, which can help policymakers take suitable actions. We investigated whether there was a correlation between public sentiment and the number of cases and deaths attributed to COVID-19. The research design integrated text preprocessing (regular expression operations, (de)tokenization, stopwords), sentiment polarization analysis via TextBlob, hypothesis formulation (null hypothesis testing), and statistical analysis (Pearson coefficient and p-value) to produce the results. The key findings highlight a correlation between sentiment polarity and deaths, starting at 41 days before and expanding up to 3 days after counting. Twitter users reacted to increased numbers of COVID-19-related deaths after four days by posting tweets with fading sentiment polarization. We also detected a strong correlation between COVID-19 Twitter conversation polarity and reported cases and a weak correlation between polarity and reported deaths.

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“…This paper aims to introduce a coherent set of methods with state-of-the-art concepts that aid understanding of complex IN. It lays the foundation for the development of an approach, successfully and efficiently employing any given DM task, like forecasting [ 5 ], incorporating tools and metrics for processing data from any given domain, retrieving information and presenting results with state-of-the-art visualization tools of the fast growing graph database technologies.…”

Section: Problem and Approachmentioning

confidence: 99%

An Introduction to Information Network Modeling Capabilities, Utilizing Graphs

Koukaras

Rousidis

Tjortjis

2021

Communications in Computer and Information Science

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This paper presents research on Information Network (IN) modeling using graph mining. The theoretical background along with a review of relevant literature is showcased, pertaining the concepts of IN model types, network schemas and graph measures. Ongoing research involves experimentation and evaluation on bipartite and star network schemas, generating test subjects using Social Media, Energy or Healthcare data. Our contribution is showcased by two proof-of-concept simulations we plan to extend.

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Forecasting and Prevention Mechanisms Using Social Media in Health Care

Cited by 7 publications

References 53 publications

Examination of NoSQL Transition and Data Mining Capabilities

Examination of NoSQL Transition and Data Mining Capabilities

Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis towards COVID-19 Cases and Deaths

An Introduction to Information Network Modeling Capabilities, Utilizing Graphs

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