scholarly journals Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study (Preprint)

2020 ◽  
Author(s):  
Hanyin Wang ◽  
Yikuan Li ◽  
Meghan Hutch ◽  
Andrew Naidech ◽  
Yuan Luo
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Health Beliefs ◽  
Language Processing ◽  
Large Population ◽  
Health Belief ◽  
Perceived Barriers ◽  
Machine Learning Techniques ◽  
Perceived Benefits ◽  
Social Media Platforms

BACKGROUND The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. OBJECTIVE This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. METHODS We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. RESULTS The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number <i>R</i><sub>0</sub> of 7.62 for trends in the number of Twitter users posting health belief–related content over the study period. The fluctuations in the number of health belief–related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians’ speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (<i>P</i>=.78 and <i>P</i>=.92 for perceived benefits and perceived barriers, respectively). CONCLUSIONS As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an <i>R</i><sub>0</sub> greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is “unhealthy” that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians’ speeches, might not be endorsed by substantial evidence and could sometimes be misleading.

scholarly journals Using Tweets to Understand How COVID-19–Related Health Beliefs Are Affected in the Age of Social Media: Twitter Data Analysis Study

10.2196/26302 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e26302 ◽  
Author(s):  
Hanyin Wang ◽  
Yikuan Li ◽  
Meghan Hutch ◽  
Andrew Naidech ◽  
Yuan Luo
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Health Beliefs ◽  
Language Processing ◽  
Large Population ◽  
Health Belief ◽  
Perceived Barriers ◽  
Machine Learning Techniques ◽  
Perceived Benefits ◽  
Social Media Platforms

Background The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. Objective This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. Methods We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. Results The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief–related content over the study period. The fluctuations in the number of health belief–related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians’ speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively). Conclusions As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is “unhealthy” that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians’ speeches, might not be endorsed by substantial evidence and could sometimes be misleading.

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

scholarly journals Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256696
Author(s):  
Anna Keuchenius ◽  
Petter Törnberg ◽  
Justus Uitermark
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Language Processing ◽  
User Interaction ◽  
User Interactions ◽  
Online Interactions ◽  
Network Analyses ◽  
Social Media Platforms ◽  
Signed Network

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis.

scholarly journals Elderly diabetes patients’ health beliefs about care and treatment for diabetes

2018 ◽  
Vol 15 (1) ◽  
pp. 51
Author(s):  
Şenay Şermet Kaya ◽  
Yeter Kitiş
Keyword(s):  
Health Beliefs ◽  
Health Belief ◽  
Health Behaviours ◽  
The Elderly ◽  
Perceived Barriers ◽  
Diabetic Patients ◽  
Perceived Benefits ◽  
Positive Health ◽  
Care And Treatment ◽  
Diabetes Patients

Purpose: This descriptive study aimed to assess elderly diabetes patients’ health beliefs about care and treatment for diabetes.Methods: The universe of the study consists of 1176 diabetic patients aged 65 years and over who are registered to eight family health centers affiliated to Mezitli district of Mersin province. In the sample, it was planned to reach the elderly between 165-330. As a result, 280 elders were reached. After obtaining the necessary permissions from the related institutions, data were collected with Descriptive Characteristics Form and HBMS for Diabetes Patients in 2012 and analyzed with nonparametric tests.Results: Of 280 patients, 55.7% were male and 60% were aged 65-69. The median value for HBMS showed that the patients had a negative health belief. The patients with higher education levels and those receiving information about diabetes had higher median of values for both the scale and its subscales, those checking their blood glucose had high median of values for the scale and the subscale perceived benefits and barriers, those complying with nutrition therapy had higher median of values for perceived barriers and recommended health behaviours, those having regular check-ups had higher median of values for perceived barriers and those doing exercise regularly had higher median of values for perceived benefits (p<0.05).Conclusion: Elderly diabetes patients should be offered education about self management and HBMS for Diabetes Patients should be used to determine educational needs and to evaluate effectiveness of education offered to help diabetes patients to develop positive health beliefs.

scholarly journals Ternion: An Autonomous Model for Fake News Detection

2021 ◽  
Vol 11 (19) ◽  
pp. 9292
Author(s):  
Noman Islam ◽  
Asadullah Shaikh ◽  
Asma Qaiser ◽  
Yousef Asiri ◽  
Sultan Almakdi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Language Processing ◽  
Negative Impact ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Fake News ◽  
Processing Techniques

In recent years, the consumption of social media content to keep up with global news and to verify its authenticity has become a considerable challenge. Social media enables us to easily access news anywhere, anytime, but it also gives rise to the spread of fake news, thereby delivering false information. This also has a negative impact on society. Therefore, it is necessary to determine whether or not news spreading over social media is real. This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development. This paper proposes a novel solution by detecting the authenticity of news through natural language processing techniques. Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity of news. In the last stage of the proposed pipeline, several machine learning techniques are applied, such as decision trees, random forest, logistic regression, and support vector machine (SVM) algorithms. For this study, the fake news dataset was taken from Kaggle. The experimental results show an accuracy of 93.15%, precision of 92.65%, recall of 95.71%, and F1-score of 94.15% for the support vector machine algorithm. The SVM is better than the second best classifier, i.e., logistic regression, by 6.82%.

scholarly journals Sentiment Analysis on Social Media using Machine Learning Approach

2021 ◽  
Author(s):  
Erick Omuya ◽  
George Okeyo ◽  
Michael Kimwele
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Machine Learning Approach

Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

scholarly journals General Purpose Opinion Mining System for Social Media Platform using Machine Learning Based Api

2019 ◽  
Vol 8 (4) ◽  
pp. 9727-9732
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Opinion Mining ◽  
Research Work ◽  
General Purpose ◽  
Machine Learning Techniques ◽  
Mining System ◽  
Social Media Platform ◽  
Social Media Platforms ◽  
Media Platform

With the growth of technology there is lot of data available on the internet. Social media platform like Twitter, FaceBook,Google+,whats app,instagram etc are the platform that allow people to share and express their views, ideas, thoughts and experiences about any topics, post messages across the world. There are mainly two types of textual information available on social media platforms. One is fact and another next one is sentiments or more formally it can also called opinion. The social media is a platform where people gives their opinion regularly. These opinions may contain some factual information. For the analysis of sentiments we required some tools. Mostly text based mining is used for opinion mining. Text mining required lots of different tools and research work. This paper, provides a machine learning techniques for opinion calculation in Twitter..

scholarly journals Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado

2021 ◽  
Author(s):  
Sarbajit Mukherjee ◽  
Shih-Yu Wang ◽  
Daniella Hirschfeld ◽  
Joel Lisonbee ◽  
Liping Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Large Population ◽  
Crisis Response ◽  
Machine Learning Techniques ◽  
Drought Severity ◽  
Support Vector ◽  
Twitter Data ◽  
Learning Techniques ◽  
Use Of Social Media

Abstract The use of social media, such as Twitter, has changed the information landscape for citizens participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise: How the usage of Twitter by a large population may change during the development of a major drought alongside how changing usage promotes drought detection? For this reason, by investigating contemporary procedures, this paper scrutinizes the potential to advance drought depiction. Hence, an analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and furthermore, its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Specifically, 3 different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is maintained that the data integration of resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Furthermore the Twitter-based model was superior in predicting drought severity.

Emotional Tweets Analysis on Social Media with Short Text Classification Using Various Machine Learning Techniques

2020 ◽  
Vol 17 (12) ◽  
pp. 5477-5482
Author(s):  
Shaik Rahamat Basha ◽  
M. Surya Bhupal Rao ◽  
P. Kiran Kumar Reddy ◽  
G. Ravi Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Text Processing ◽  
Human Life ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Short Text ◽  
Online Social Media ◽  
The World

Online Social media are a huge source of regular communication since most people in the world today use these services to stay communicating with each other in their modern lives. Today’s research has been implemented on emotion recognition by message. The majority of the research uses a method of machine learning. In order to extract information from the textual text written by human beings, natural language processing (NLP) techniques were used. The emotion of humans may be expressed when reading or writing a message. Human beings are willing, since human life is filled with a variety of emotions, to feel various emotions. This analysis helps us to realize the use of text processing and text mining methods by social media researchers in order to classify key data themes. Our experiments presented that the two main social networks in the world are conducting text-based mining on Facebook and Twitter. In this proposed study, we categorized the human feelings such as joy, fear, love, anger, surprise, sadness and thankfulness and compared our results using various methods of machine learning.

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