scholarly journals COVID-19 Misinformation Online and Health Literacy: A Brief Overview

2021 ◽  
Vol 18 (15) ◽  
pp. 8091
Author(s):  
Salman Bin Naeem ◽  
Maged N. Kamel Boulos
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Health Literacy ◽  
Information Content ◽  
Digital Health ◽  
Negative Effects ◽  
The World ◽  
Synergistic Approach ◽  
Viral Nature

Low digital health literacy affects large percentages of populations around the world and is a direct contributor to the spread of COVID-19-related online misinformation (together with bots). The ease and ‘viral’ nature of social media sharing further complicate the situation. This paper provides a quick overview of the magnitude of the problem of COVID-19 misinformation on social media, its devastating effects, and its intricate relation to digital health literacy. The main strategies, methods and services that can be used to detect and prevent the spread of COVID-19 misinformation, including machine learning-based approaches, health literacy guidelines, checklists, mythbusters and fact-checkers, are then briefly reviewed. Given the complexity of the COVID-19 infodemic, it is very unlikely that any of these approaches or tools will be fully effective alone in stopping the spread of COVID-19 misinformation. Instead, a mixed, synergistic approach, combining the best of these strategies, methods, and services together, is highly recommended in tackling online health misinformation, and mitigating its negative effects in COVID-19 and future pandemics. Furthermore, techniques and tools should ideally focus on evaluating both the message (information content) and the messenger (information author/source) and not just rely on assessing the latter as a quick and easy proxy for the trustworthiness and truthfulness of the former. Surveying and improving population digital health literacy levels are also essential for future infodemic preparedness.

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

scholarly journals Communication Sentiment Analyzer using Machine Learning with Naive Bayes Bernoullinb

2019 ◽  
Vol 9 (1) ◽  
pp. 5976-5979
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Major Part ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
User Preferences ◽  
Social Media Data ◽  
Machine Learning Model ◽  
The World ◽  
Media Data

In this never-ending social media era it is estimated that over 5 billion people use smartphones. Out of these, there are over 1.5 billion active users in the world. In which we all are a major part and before opening our messages we all are curious about what message we have received. No doubt, we all always hope for a good message to be received. So Sentiment analysis on social media data has been seen by many as an effective tool to monitor user preferences and inclination. Finally, we propose a scalable machine learning model to analyze the polarity of a communicative text using Naive Bayes’ Bernoulli classifier. This paper works on only two polarities that is whether the sentence is positive or negative. Bernoulli classifier is used in this paper because it is best suited for binary inputs which in turn enhances the accuracy of up to 97%.

The Impact of Social Media on Children

2019 ◽  
pp. 1-10
Author(s):  
Tuncay Dilci ◽  
Anıl Kadir Eranıl
Keyword(s):  
Social Media ◽  
Cognitive Skills ◽  
Thinking Skills ◽  
Great Power ◽  
Negative Effects ◽  
Advantages And Disadvantages ◽  
The Social ◽  
The World ◽  
The Impact ◽  
And Training

This chapter examines the impacts of social media on children. Advantages and disadvantages of social media are always available. Positive aspects of social media include allowing children to be brought up as multicultural individuals, enabling education and training environments to design for purposes, using as the main or supplementary source of education, a great power in creating and sharing information. Its negative aspects include leading to a reduction of their academic, social, and cognitive skills in the early periods when children were exposed to the social media, causing the children to develop obesity, mostly bringing up as consumption-centered individuals, perceive the world as a screenshot, and have low critical, creative, and reflective thinking skills. Therefore, one of the most important tasks undertaken to reduce or eliminate the negative effects is to raise and educate media-literate individuals.

scholarly journals All that Glitters Is not Gold: Consumer Health Informatics and Education in the Era of Social Media and Health Apps

2016 ◽  
Vol 25 (01) ◽  
pp. 188-193 ◽  
Author(s):  
P. Staccini ◽  
L. Fernandez-Luque ◽  
Keyword(s):  
Social Media ◽  
Health Literacy ◽  
Health Care Professionals ◽  
Health Informatics ◽  
Health Management ◽  
Digital Health ◽  
Unintended Consequences ◽  
Consumer Health Informatics ◽  
Consumer Health ◽  
Chronic Patients

Summary Objective: To summarize the state of the art published during the year 2015 in the areas related to consumer health informatics and education with a special emphasis on unintended consequences of applying mobile and social media technologies in that domain. Methods: We conducted a systematic review of articles published in PubMed with a predefined set of queries, which lead to the selection of over 700 potential relevant articles. Section editors screened those papers on the title, abstract, and finally complete paper basis, taking into account the papers’ relevance for the section topic. The 15 most representative papers were finally selected by consensus between the two section editors and submitted for full review and scoring to external reviewers and the yearbook editors. Based on the final scoring, section editors selected the best five papers. Results: The five best papers can be grouped in two major areas: 1) Digital health literacy and 2) Quality and safety concerns. Regarding health literacy issues of patients with chronic conditions such as asthma, online interventions should rather focus on changing patient beliefs about the disease than on supporting them in the management of their pathology since personally controlled health management systems do not show expected benefits,. Nevertheless, encouraging and training chronic patients for an active online health information–seeking behaviour substantially decreases state anxiety level. Regarding safety and privacy issues, even recommended health-related apps available on mobile phones do not guarantee personal data protection. Furthermore, the analysis indicated that patients undergoing Internet interventions experienced at least one adverse event that might be related to treatment. At least, predictive factors have been identified in order to credit or not a health rumour. Conclusions: Trusting digital and connected health can be achieved if patients, health care professionals, and industrials build a shared model of health data management integrating ethics rules. Only increasing efforts in education with regards of digital health would help reach this goal., This would not resolve all frauds and security issues but at least improve their detection.

scholarly journals Exploring Children's Computer Literacy Practices Through a Digital Health Literacy Lens: A Pilot Study (Preprint)

2020 ◽  
Author(s):  
Lorie Donelle ◽  
Danica Facca ◽  
Shauna Burke ◽  
Bradley Hiebert ◽  
Emma Bender ◽  
...  
Keyword(s):  
Social Media ◽  
Health Literacy ◽  
Pilot Study ◽  
Young Children ◽  
Computer Literacy ◽  
Literacy Practices ◽  
Personal Information ◽  
Digital Health ◽  
Health Promotion Program ◽  
Digital Devices

BACKGROUND In our digitally driven age it is no surprise that children are becoming regular users of information and communication devices such as tablets, smartphones, and social media. Although a growing body of literature continues to investigate children’s use of these digital devices, attention to elements of children’s digital health literacy is limited. Digital health literacy, a more recent term for eHealth literacy, is the combination of diverse literacies and proficiencies needed to access and critically evaluate information within Web 1.0 and 2.0 contexts. A fundamental component of digital health literacy is computer literacy which involves context-specific elements such as a user’s distribution of personal information and exercise of privacy settings. OBJECTIVE The objective of this pilot study was to explore children’s computer literacy practices through their social media use. METHODS The study used a cross sectional survey with 42 young children aged six to 10 years who were enrolled in an after-school health promotion program in Southwestern Ontario, Canada. RESULTS Results indicated that young children share their personal information online through social media and download applications to the digital devices they use without consistent parental supervision or adult (teacher) oversight. CONCLUSIONS In order to support young children’s self-directed exploration and use of social media, deeper examination of computer literacy, among other aspects of digital health literacy, is warranted so parents, educators, and researchers alike can respect and support children’s learning and wellbeing as independent users of digital devices.

scholarly journals Public Trolling Detection on Twitter Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3097-3100
Author(s):  
Miss. Pooja Dilip Dhotre
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
The People ◽  
The World ◽  
Added Benefit ◽  
Current Popularity

Social media websites are among the internet's most far-reaching digital sites. Billions of social network users exist Users' frequent interactions with social networking sites, like Twitter, have a widespread and sometimes unfortunate effect on day-to-day life. Social networking sites make it easy for large amounts of unwanted and unrelated information to spread around the world. Twitter is a popular micro blogging service where users connect with others with similar interests. Because of the current popularity of Twitter, it is vulnerable to public shaming. Recently, Twitter has emerged as a rich source of human-generated information, with the added benefit of connecting you with customers and enabling two-way communication. It is generally accepted that when someone posts a comment in an occurrence, it is likely to humiliate the victim. The fact that shaming users' follower counts increase faster than that of the people who don't use shame is interesting. Using machine learning algorithms, users will be able to identify disrespectful words, as well as the overall negativity of those words, which is displayed in a percentage.

scholarly journals براہوئی زبان نا شونداری ٹی میڈیا نا کردار

Al-Burz ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 127-130
Author(s):  
Qayyum Bedar
Keyword(s):  
Social Media ◽  
Cultural Values ◽  
Electronic Media ◽  
Vital Role ◽  
Pivotal Role ◽  
Negative Effects ◽  
The World ◽  
Satellite Channels ◽  
Regional Languages

Electronic Media like Radio and Television is an effective tool of communication as for as the democratic or other modern societies are concerned. A Province like Balochistan where population is scattered and distances between human settlements are far away from each other, the pivotal role of distance electronic media cannot be ignored. now, with the emergence of satellite channels which are viewed in every nook and corner of the world, the overlapping of ideas, effects of one society to another and hegemony of stronger nations, their languages and civilizations can affect negatively to the weaker and smaller nations and there is need to counter and defuse the negative effects of these hegemonic designs, Balochistan has a multilingual and multi-cultural society; people speak different languages and have distinct cultural values, traditions, and taboos. As the language is a major source of interaction with each other, then it is necessary to develop and flourish each and every language which is spoken in Balochistan. The Baloch population may by at large, speak Balochi, Brahui and Sindhi languages. Dozens of newspapers, magazines and Electronic media like Radio, Television as well as social media played a vital role in promotion of Brahui       Apart from these as well as other regional languages.

scholarly journals Digital health literacy and online information seeking in times of COVID-19. A cross-sectional survey among university students in Germany (Preprint)

2020 ◽  
Author(s):  
Kevin Dadaczynski ◽  
Orkan Okan ◽  
Melanie Messer ◽  
Angela Y. M. Leung ◽  
Rafaela Rosário ◽  
...  
Keyword(s):  
Social Media ◽  
Health Literacy ◽  
University Students ◽  
Information Seeking ◽  
Digital Health ◽  
Online Information ◽  
Cross Sectional ◽  
Related Information ◽  
Health Related ◽  
Online Information Seeking

BACKGROUND Digital communication technologies play an important role in governments’ and public health authorities’ health communication strategies during the COVID-19 pandemic. The internet and social media have become important sources of health-related information on the coronavirus and on protective behaviours. In addition, the COVID-19 infodemic spreads faster than the coronavirus itself, which interferes with governmental health-related communication efforts. This puts national public health containment strategies in jeopardy. Therefore, digital health literacy is a key competence to navigate coronavirus-related information and service environments. OBJECTIVE This study aimed to investigate university students’ digital health literacy and online information seeking behaviours during the early stages of the coronavirus pandemic in Germany. METHODS A cross-sectional study among N=14,916 university students aged ≥18 from 130 universities across all sixteen federal states of Germany was conducted using an online survey. Along with sociodemographic characteristics (sex, age, subjective social status) measures included five subscales from the Digital Health Literacy Instrument (DHLI), which was adapted to the specific coronavirus context. Online information seeking behaviour was investigated by examining the online sources used by university students and the topics that students search for in connection with the coronavirus. Data were analysed using univariate and bivariate analyses. RESULTS Across digital health literacy dimensions, the greatest difficulties could be found for assessing the reliability of health-related information (42.3%) and the ability to determine whether the information was written with commercial interest (38.9%). Moreover, respondents also indicated that they most frequently have problems finding the information they are looking for (30.4%). When stratified according to sociodemographic characteristics, significant differences were found with female university students reporting a lower DHLI for the dimensions of ‘information searching’ and of ‘evaluating reliability’. Search engines, news portals and public bodies’ websites were most often used by the respondents as sources to search for information on COVID-19 and related issues. Female students were found to use social media and health portals more frequently, while male students used Wikipedia and other online encyclopaedias as well as YouTube more often. The use of social media was associated with a low ability to critically evaluate information, while opposite differences were observed for the use of public websites. CONCLUSIONS Although digital health literacy is, in summary, well developed in university students, a significant proportion of students still face difficulties with certain abilities to deal with information. There is need to strengthen the digital health literacy capacities of university students using tailored interventions. Improving the quality of health-related information on the internet is also key. CLINICALTRIAL

A Comprehensive Review on Online News Popularity Prediction using Machine Learning Approach

2019 ◽  
Vol 5 (1) ◽  
pp. 7
Author(s):  
Priyanka Rathord ◽  
Dr. Anurag Jain ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online News ◽  
Machine Learning Techniques ◽  
News Article ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
The World ◽  
Popularity Prediction

With the help of Internet, the online news can be instantly spread around the world. Most of peoples now have the habit of reading and sharing news online, for instance, using social media like Twitter and Facebook. Typically, the news popularity can be indicated by the number of reads, likes or shares. For the online news stake holders such as content providers or advertisers, it’s very valuable if the popularity of the news articles can be accurately predicted prior to the publication. Thus, it is interesting and meaningful to use the machine learning techniques to predict the popularity of online news articles. Various works have been done in prediction of online news popularity. Popularity of news depends upon various features like sharing of online news on social media, comments of visitors for news, likes for news articles etc. It is necessary to know what makes one online news article more popular than another article. Unpopular articles need to get optimize for further popularity. In this paper, different methodologies are analyzed which predict the popularity of online news articles. These methodologies are compared, their parameters are considered and improvements are suggested. The proposed methodology describes online news popularity predicting system.

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