The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate p...The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.展开更多
Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM...Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.展开更多
To explore the relationship between social influence,social comparison,clarity of self-concept,and psychological anxiety among young women during their usage of social networking sites,our study selected 338 young wom...To explore the relationship between social influence,social comparison,clarity of self-concept,and psychological anxiety among young women during their usage of social networking sites,our study selected 338 young women aged 14-34 from the social site platforms of Little Red Book and Weibo for questionnaire surveys.The Passive Social Network Utilization Scale,Social Comparison Scale(SCS),Social Influence Questionnaire,Self-Concept Clarity Scale(SCCS),and Generalized Anxiety Disorder Scale(GAD-7)were employed to measure the subjects.Our results show that the frequency of passive social media use is positively related to the level of psychological anxiety.Social comparison,social influence,and unclear self-concepts under social media use are negatively predictive of psychological anxiety.The chain mediation effects indicate that social comparison and social influence under social media use negatively predict the clarity of self-concept,thus having a negative impact on the psychological health of young women.Therefore,young women should strengthen their self-concepts,control their frequency of social media usage,avoid addiction,and pay special attention to their frequency of passive use,in order to protect their psychological health.Our study provides some practical implications and insights regarding the relationship between young women’s social media use and psychological health.展开更多
In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), s...In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.展开更多
Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for ...Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for pharmacovigilance.Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning,text mining based on deep learning(neural networks)and adverse drug reaction(ADR)terminology.Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine(SVM)algorithm,naive Bayesian(NB)classifier,decision tree,hidden Markov model(HMM)and bidirectional en-coder representations from transformers(BERT).The main neural network text mining based on deep learning are convolution neural network(CNN),recurrent neural network(RNN)and long short-term memory(LSTM).ADR terminology standardization tools mainly include“Medical Dictionary for Regulatory Activities”(MedDRA),“WHODrug”and“Systematized Nomenclature of Medicine-Clinical Terms”(SNOMED CT).展开更多
Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder h...Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival.Therefore,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia carriers.Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs.Exploring individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public.An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main tools.VADER represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social media.The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform Twitter.In this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,respectively.The tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.展开更多
Objective:The objective of this study was to assess the prevalence and level of social media addiction among nursing students in the Faculty of Nursing,Cairo University.Materials and Methods:A descriptive cross‑sectio...Objective:The objective of this study was to assess the prevalence and level of social media addiction among nursing students in the Faculty of Nursing,Cairo University.Materials and Methods:A descriptive cross‑sectional research design was conducted on samples of 340 students at the Faculty of Nursing,Cairo University.Data were collected through demographic background information sheet and Social Networking Addiction Scale.Results:All the students were addicted as 6.76%were severely addicted and 60.59%and 32.65%were moderately and mildly addicted,respectively.Significant relations were found between social media addiction and students’age(χ^(2)=11.331,P=0.003),educational level(χ^(2)=20.239,P=0.003),and grade point average(χ^(2)=19.378,P=0.013).Conclusion:Internet addiction was prevalent among all students but at different levels,so early screening of students for Internet addiction using the Internet Addiction Scale is important to provide early treatment and prevent hazards to health.展开更多
Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of soci...Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a betterway.Irony and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion mining.For the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content.Therefore,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social media.The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media.To attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction.For irony detection and classification,the DBN model is exploited in this work.At last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)algorithm.The experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark dataset.The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.展开更多
Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media analysis.However,traditional polling approaches often strugg...Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media analysis.However,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and understanding.In light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral process.To achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country levels.LEPM predicts the support and opposing strength of each political party/candidate.To determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/posts.The sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case study.By applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state level.Moreover,at the country level,the LEPM model outperforms traditional polling results.With a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other methodologies.Leveraging the extensive social media data,the LEPM model provides nuanced insights into voter behavior,enabling policymakers to make informed decisions and facilitating in-depth analyses of elections.The study emphasizes the importance of using social media data for reliable election prediction and offers implications for enhancing prediction accuracy and understanding voter sentiment and behavior.展开更多
In recent years,the growing popularity of social media platforms has led to several interesting natural language processing(NLP)applications.However,these social media-based NLP applications are subject to different t...In recent years,the growing popularity of social media platforms has led to several interesting natural language processing(NLP)applications.However,these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning(ML)and NLP techniques.This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication.These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form.The intuition of the proposed scheme is to generate adversarial examples influenced by human cognition in text generation on social media platforms while preserving human robustness in text understanding with the fewest possible perturbations.The intentional textual variations introduced by users in online communication motivate us to replicate such trends in attacking text to see the effects of such widely used textual variations on the deep learning classifiers.In this work,the four most commonly used textual variations are chosen to generate adversarial examples.Moreover,this article introduced a word importance ranking-based beam search algorithm as a searching method for the best possible perturbation selection.The effectiveness of the proposed adversarial attacks has been demonstrated on four benchmark datasets in an extensive experimental setup.展开更多
Social media has become increasingly popular and is now a significant tool for daily communication for many people.The use of social media can cause anxiety and have detrimental impacts on mental health.Cognitive impa...Social media has become increasingly popular and is now a significant tool for daily communication for many people.The use of social media can cause anxiety and have detrimental impacts on mental health.Cognitive impairment is more likely to affect individuals with anxiety.Investigating the cognitive abilities and mental health of social media users requires the development of new methodologies.This study employed the AX-Continuous Performance Test(AX-CPT)paradigm and the Stroop paradigm to study the cognitive control characteristics of trait anxiety,drawing on psychological experimental methods.Previous studies on whether trait anxiety impairs cognitive control remain controversial,possibly because cognitive control is viewed as a whole.It may also be due to the motivational effect of anxiety,which compensates for the impairment of cognitive control caused by anxiety through the recruitment of cognitive resources.Understanding the mental health and cognitive control traits of anxious social media users can be improved by using the Dual Mechanisms of Cognitive Control Account,which divides cognitive control into proactive and reactive control.Thefindings demonstrate that trait anxiety has an impact on both proactive and reactive control,while working memory load did not modulate the effect of trait anxiety on cognitive control.These results support the attentional control theory and provide a new approach to studying the mental health of social media users.展开更多
Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classification of this R...Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classification of this RS research is limited and needs to be improved.The previous method did notfind any user reviews within a time,so it gets poor accuracy and doesn’tfilter the irre-levant comments efficiently.The Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s problem.So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately.Thefirst step is to collect the data based on the transactional reviews of social media.The second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the dataset.Extract the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the features.Finally,In the classification method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the system.The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.展开更多
Many graph domination applications can be expanded to achieve complete cototal domination.If every node in a dominating set is regarded as a record server for a PC organization,then each PC affiliated with the organiz...Many graph domination applications can be expanded to achieve complete cototal domination.If every node in a dominating set is regarded as a record server for a PC organization,then each PC affiliated with the organization has direct access to a document server.It is occasionally reasonable to believe that this gateway will remain available even if one of the scrape servers fails.Because every PC has direct access to at least two documents’servers,a complete cototal dominating set provides the required adaptability to non-critical failure in such scenarios.In this paper,we presented a method for calculating a graph’s complete cototal roman domination number.We also examined the properties and determined the bounds for a graph’s complete cototal roman domination number,and its applications are presented.It has been observed that one’s interest fluctuate over time,therefore inferring them just from one’s own behaviour may be inconclusive.However,it may be able to deduce a user’s constant interest to some level if a user’s networking is also watched for similar or related actions.This research proposes a method that considers a user’s and his channel’s activity,as well as common tags,persons,and organizations from their social media posts in order to establish a solid foundation for the required conclusion.展开更多
Objectives:To examine the association between social media addiction and sleep quality among undergraduate nursing students.Methods:This research is an analytical observational study with a cross-sectional approach.Th...Objectives:To examine the association between social media addiction and sleep quality among undergraduate nursing students.Methods:This research is an analytical observational study with a cross-sectional approach.The sampling technique was purposive sampling of 150 nursing students at a private university in Malang,Indonesia.Respondents filled out a questionnaire about self-identity,a sleep quality questionnaire from the Pittsburgh Sleep Quality Index(PSQI),and social media addiction from Social Media Addiction Scale(SMAS).This was a bivariate analysis which was conducted using the Pearson Product Moment Correlation test.Results:The results of this study reported that most nursing students were addicted to social media(76%).As for the parameter of the quality variable,most respondents had poor sleep quality,which were about 106 people(70.3%).The result of the Pearson Product Moment Correlation test obtained a P value of 0.000.The Pearson correlation coefficient was 0.358.Social media addiction(adjusted odds ratio[OR]4.80,95%confidence interval[CI]=2.08-11.02),gender(adjusted OR 3.79,95%CI=1.58-9.12),and using social media for a long time(adjusted OR 4.21,95%CI=1.97-10.48)were associated with sleep quality.Conclusions:We found that there is an association between social media addiction and sleep quality among nursing students.Furthermore,we might be educating nursing students to manage their time to improve their quality of sleep to avoid any health problems.展开更多
Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, le...Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.展开更多
The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known...The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known as Latinization, also employed for many non-latin alphabet languages. The primary aim of this research is to evaluate the effect of Greeklish on reading time. A sample of 732 young Greeks were asked about their habits when communicating through e-mail and social media with their friends and they then participated in an experiment in which they were asked to read and understand two short texts, one written in Greek and the other in Greeklish. The findings of the research show that nearly one third of the participants use Greeklish. The results of the experiment conducted reveal that understanding is not affected by the alphabet used but reading Greeklish is significantly more time consuming than reading Greek independently of the sex and the familiarity of the participants with Greeklish. The findings suggest that amending social and communication media with software utilities related to Latinization such as language identifiers and converters may reduce reading time and thus facilitate written communication among the users.展开更多
This paper aims to determine the mediating effect of social media on the relationship between travel behavior and tourist preference.This paper utilized the mediation analysis design to determine whether social media ...This paper aims to determine the mediating effect of social media on the relationship between travel behavior and tourist preference.This paper utilized the mediation analysis design to determine whether social media significantly affects the independent and dependent variables.For the statistical tools,the proponents of the study utilized descriptive statistics,Sobel’s test,mean and standard deviation.Also,the proponents of the study utilized data from questionnaires gathered through an online google form.Most of the respondents are single females,college-level,and use Facebook most of the time.Therefore,this is the social media platform that the majority utilize.It has been revealed in the study the three sets of correlated variables:Travel behavior vs.tourist behavior,travel preference vs.social media,and social media vs.tourist behavior,have a strong positive linear relationship between the two quantitative variables.Thus,the study proponents concluded that there is an indirect effect between the travel preference and tourist behavior of local tourists in Davao City via social media.Lastly,the study’s findings reveal the fundamentals of how visitors make decisions,which are essential for enhancing the competitive advantages of cultural destinations.Significant findings from the study are beneficial to the growth of cultural tourism in Davao City.展开更多
Social media has become an inseparable part of modern life,but concerns have arisen regarding its influence on mental health.This article aims to delve into the intricate relationship between social media and mental h...Social media has become an inseparable part of modern life,but concerns have arisen regarding its influence on mental health.This article aims to delve into the intricate relationship between social media and mental health,highlighting both the benefits it brings and the risks it poses.Drawing upon existing research,this study reveals that excessive reliance on social media can contribute to mental health issues such as anxiety and depression,social isolation,and the spread of violent or hateful speech.Therefore,individuals need to be mindful of these potential hazards and take measures to protect their mental well-being.Through a comprehensive analysis of the positive and negative effects of social media on mental health,this article puts forward practical solutions to alleviate any adverse consequences.For instance,individuals are encouraged to use social media moderately,cultivate healthy digital habits,and limit their screen time.Moreover,governments and society as a whole should actively promote mental health awareness and reinforce regulations around social media usage to minimize potential negative impacts.In conclusion,while social media brings convenience and various benefits,it cannot be ignored that it also has the potential to impact mental health.By recognizing and addressing the associated risks,individuals,governments,and society can work together to foster a culture of healthy social media practices and safeguard mental well-being.The innovative approaches proposed in this article serve as a helpful guide for further exploration in this intriguing field.展开更多
When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People ...When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People communicate with others on social media through messages and comments. So bullies use social media as a rich environment to bully others, especially on political issues. Fights over Cyberbullying on political and social media posts are common today. Most of the time, it does a lot of damage. However, few works have been done for monitoring Bangla text on social media & no work has been done yet for detecting the bullying Bangla text on political issues due to the lack of annotated corpora and morphologic analyzers. In this work, we used several machine learning classifiers & a model. That will help to detect the Bangla bullying texts on social media. For this work, 11,000 Bangla texts have been collected from the comments section of political Facebook posts to make a new dataset and labelled the data as either bullied or not. This dataset has been used to train the machine learning classifier. The results indicate that Random Forest achieves superior accuracy of 91.08%.展开更多
This study used both the “digital divide” and “attribution theory” to analyze the propensity of social media use by disaster-prone communities. The study focused on the variables that may affect how social media i...This study used both the “digital divide” and “attribution theory” to analyze the propensity of social media use by disaster-prone communities. The study focused on the variables that may affect how social media is used for disaster management. Structural equation modeling (SEM) was utilized in the study to analyze the data and test the hypotheses after using a survey questionnaire to collect the data. The study’s findings show that: 1) communities that are vulnerable to disasters are less likely to use social media for disaster management, 2) personal effort and intention to use social media for disaster management are positively correlated, and 3) task complexity and intention to use social media for disaster management are negatively correlated. The study added to the body of knowledge regarding the role social media plays in disaster management.展开更多
文摘The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.
基金authors are thankful to the Deanship of Scientific Research at Najran University for funding this work,under the Research Groups Funding Program Grant Code(NU/RG/SERC/12/27).
文摘Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.
基金funded by Zhejiang Xi Jinping Research Center for Socialist Thought with Chinese Characteristics in the New Era Project(Grant No.23CCG39).
文摘To explore the relationship between social influence,social comparison,clarity of self-concept,and psychological anxiety among young women during their usage of social networking sites,our study selected 338 young women aged 14-34 from the social site platforms of Little Red Book and Weibo for questionnaire surveys.The Passive Social Network Utilization Scale,Social Comparison Scale(SCS),Social Influence Questionnaire,Self-Concept Clarity Scale(SCCS),and Generalized Anxiety Disorder Scale(GAD-7)were employed to measure the subjects.Our results show that the frequency of passive social media use is positively related to the level of psychological anxiety.Social comparison,social influence,and unclear self-concepts under social media use are negatively predictive of psychological anxiety.The chain mediation effects indicate that social comparison and social influence under social media use negatively predict the clarity of self-concept,thus having a negative impact on the psychological health of young women.Therefore,young women should strengthen their self-concepts,control their frequency of social media usage,avoid addiction,and pay special attention to their frequency of passive use,in order to protect their psychological health.Our study provides some practical implications and insights regarding the relationship between young women’s social media use and psychological health.
文摘In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.
文摘Objective To discuss how to use social media data for post-marketing drug safety monitoring in China as soon as possible by systematically combing the text mining applications,and to provide new ideas and methods for pharmacovigilance.Methods Relevant domestic and foreign literature was used to explore text classification based on machine learning,text mining based on deep learning(neural networks)and adverse drug reaction(ADR)terminology.Results and Conclusion Text classification based on traditional machine learning mainly include support vector machine(SVM)algorithm,naive Bayesian(NB)classifier,decision tree,hidden Markov model(HMM)and bidirectional en-coder representations from transformers(BERT).The main neural network text mining based on deep learning are convolution neural network(CNN),recurrent neural network(RNN)and long short-term memory(LSTM).ADR terminology standardization tools mainly include“Medical Dictionary for Regulatory Activities”(MedDRA),“WHODrug”and“Systematized Nomenclature of Medicine-Clinical Terms”(SNOMED CT).
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program grant coder NU/RC/SERC/11/5.
文摘Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival.Therefore,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia carriers.Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs.Exploring individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public.An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main tools.VADER represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social media.The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform Twitter.In this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,respectively.The tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.
文摘Objective:The objective of this study was to assess the prevalence and level of social media addiction among nursing students in the Faculty of Nursing,Cairo University.Materials and Methods:A descriptive cross‑sectional research design was conducted on samples of 340 students at the Faculty of Nursing,Cairo University.Data were collected through demographic background information sheet and Social Networking Addiction Scale.Results:All the students were addicted as 6.76%were severely addicted and 60.59%and 32.65%were moderately and mildly addicted,respectively.Significant relations were found between social media addiction and students’age(χ^(2)=11.331,P=0.003),educational level(χ^(2)=20.239,P=0.003),and grade point average(χ^(2)=19.378,P=0.013).Conclusion:Internet addiction was prevalent among all students but at different levels,so early screening of students for Internet addiction using the Internet Addiction Scale is important to provide early treatment and prevent hazards to health.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4320484DSR33).
文摘Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a betterway.Irony and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion mining.For the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content.Therefore,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social media.The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media.To attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction.For irony detection and classification,the DBN model is exploited in this work.At last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)algorithm.The experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark dataset.The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.
基金funded by the Beijing Municipal Natural Science Foundation(Grant No.4212026)the Foundation Enhancement Program(Grant No.2021-JCJQ-JJ-0059).
文摘Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media analysis.However,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and understanding.In light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral process.To achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country levels.LEPM predicts the support and opposing strength of each political party/candidate.To determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/posts.The sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case study.By applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state level.Moreover,at the country level,the LEPM model outperforms traditional polling results.With a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other methodologies.Leveraging the extensive social media data,the LEPM model provides nuanced insights into voter behavior,enabling policymakers to make informed decisions and facilitating in-depth analyses of elections.The study emphasizes the importance of using social media data for reliable election prediction and offers implications for enhancing prediction accuracy and understanding voter sentiment and behavior.
基金supported by the National Research Foundation of Korea (NRF)grant funded by the Korea government (MSIT) (No.NRF-2022R1A2C1007434)by the BK21 FOUR Program of the NRF of Korea funded by the Ministry of Education (NRF5199991014091).
文摘In recent years,the growing popularity of social media platforms has led to several interesting natural language processing(NLP)applications.However,these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning(ML)and NLP techniques.This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication.These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form.The intuition of the proposed scheme is to generate adversarial examples influenced by human cognition in text generation on social media platforms while preserving human robustness in text understanding with the fewest possible perturbations.The intentional textual variations introduced by users in online communication motivate us to replicate such trends in attacking text to see the effects of such widely used textual variations on the deep learning classifiers.In this work,the four most commonly used textual variations are chosen to generate adversarial examples.Moreover,this article introduced a word importance ranking-based beam search algorithm as a searching method for the best possible perturbation selection.The effectiveness of the proposed adversarial attacks has been demonstrated on four benchmark datasets in an extensive experimental setup.
文摘Social media has become increasingly popular and is now a significant tool for daily communication for many people.The use of social media can cause anxiety and have detrimental impacts on mental health.Cognitive impairment is more likely to affect individuals with anxiety.Investigating the cognitive abilities and mental health of social media users requires the development of new methodologies.This study employed the AX-Continuous Performance Test(AX-CPT)paradigm and the Stroop paradigm to study the cognitive control characteristics of trait anxiety,drawing on psychological experimental methods.Previous studies on whether trait anxiety impairs cognitive control remain controversial,possibly because cognitive control is viewed as a whole.It may also be due to the motivational effect of anxiety,which compensates for the impairment of cognitive control caused by anxiety through the recruitment of cognitive resources.Understanding the mental health and cognitive control traits of anxious social media users can be improved by using the Dual Mechanisms of Cognitive Control Account,which divides cognitive control into proactive and reactive control.Thefindings demonstrate that trait anxiety has an impact on both proactive and reactive control,while working memory load did not modulate the effect of trait anxiety on cognitive control.These results support the attentional control theory and provide a new approach to studying the mental health of social media users.
文摘Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classification of this RS research is limited and needs to be improved.The previous method did notfind any user reviews within a time,so it gets poor accuracy and doesn’tfilter the irre-levant comments efficiently.The Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s problem.So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately.Thefirst step is to collect the data based on the transactional reviews of social media.The second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the dataset.Extract the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the features.Finally,In the classification method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the system.The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.
文摘Many graph domination applications can be expanded to achieve complete cototal domination.If every node in a dominating set is regarded as a record server for a PC organization,then each PC affiliated with the organization has direct access to a document server.It is occasionally reasonable to believe that this gateway will remain available even if one of the scrape servers fails.Because every PC has direct access to at least two documents’servers,a complete cototal dominating set provides the required adaptability to non-critical failure in such scenarios.In this paper,we presented a method for calculating a graph’s complete cototal roman domination number.We also examined the properties and determined the bounds for a graph’s complete cototal roman domination number,and its applications are presented.It has been observed that one’s interest fluctuate over time,therefore inferring them just from one’s own behaviour may be inconclusive.However,it may be able to deduce a user’s constant interest to some level if a user’s networking is also watched for similar or related actions.This research proposes a method that considers a user’s and his channel’s activity,as well as common tags,persons,and organizations from their social media posts in order to establish a solid foundation for the required conclusion.
文摘Objectives:To examine the association between social media addiction and sleep quality among undergraduate nursing students.Methods:This research is an analytical observational study with a cross-sectional approach.The sampling technique was purposive sampling of 150 nursing students at a private university in Malang,Indonesia.Respondents filled out a questionnaire about self-identity,a sleep quality questionnaire from the Pittsburgh Sleep Quality Index(PSQI),and social media addiction from Social Media Addiction Scale(SMAS).This was a bivariate analysis which was conducted using the Pearson Product Moment Correlation test.Results:The results of this study reported that most nursing students were addicted to social media(76%).As for the parameter of the quality variable,most respondents had poor sleep quality,which were about 106 people(70.3%).The result of the Pearson Product Moment Correlation test obtained a P value of 0.000.The Pearson correlation coefficient was 0.358.Social media addiction(adjusted odds ratio[OR]4.80,95%confidence interval[CI]=2.08-11.02),gender(adjusted OR 3.79,95%CI=1.58-9.12),and using social media for a long time(adjusted OR 4.21,95%CI=1.97-10.48)were associated with sleep quality.Conclusions:We found that there is an association between social media addiction and sleep quality among nursing students.Furthermore,we might be educating nursing students to manage their time to improve their quality of sleep to avoid any health problems.
文摘Social media plays a crucial role in the organization of massive social movements. However, the sheer quantity of data generated by the events as well as the data collection restrictions that researchers encounter, leads to a series of challenges for researchers who want to analyze dynamic public discourse and opinion in response to and in the creation of world events. In this paper we present gatherTweet, a Python package that helps researchers efficiently collect social media data for events that are composed of many decentralized actions (across both space and time). The package is useful for studies that require analysis of the organizational or baseline messaging before an action, the action itself, and the effects of the action on subsequent public discourse. By capturing these aspects of world events gatherTweet enables the study of events and actions like protests, natural disasters, and elections.
文摘The increasing prevalence of technology in society has an impact on young people’s language use and development. Greeklish is the writing of Greek texts using the Latin instead of the Greek alphabet, a practice known as Latinization, also employed for many non-latin alphabet languages. The primary aim of this research is to evaluate the effect of Greeklish on reading time. A sample of 732 young Greeks were asked about their habits when communicating through e-mail and social media with their friends and they then participated in an experiment in which they were asked to read and understand two short texts, one written in Greek and the other in Greeklish. The findings of the research show that nearly one third of the participants use Greeklish. The results of the experiment conducted reveal that understanding is not affected by the alphabet used but reading Greeklish is significantly more time consuming than reading Greek independently of the sex and the familiarity of the participants with Greeklish. The findings suggest that amending social and communication media with software utilities related to Latinization such as language identifiers and converters may reduce reading time and thus facilitate written communication among the users.
文摘This paper aims to determine the mediating effect of social media on the relationship between travel behavior and tourist preference.This paper utilized the mediation analysis design to determine whether social media significantly affects the independent and dependent variables.For the statistical tools,the proponents of the study utilized descriptive statistics,Sobel’s test,mean and standard deviation.Also,the proponents of the study utilized data from questionnaires gathered through an online google form.Most of the respondents are single females,college-level,and use Facebook most of the time.Therefore,this is the social media platform that the majority utilize.It has been revealed in the study the three sets of correlated variables:Travel behavior vs.tourist behavior,travel preference vs.social media,and social media vs.tourist behavior,have a strong positive linear relationship between the two quantitative variables.Thus,the study proponents concluded that there is an indirect effect between the travel preference and tourist behavior of local tourists in Davao City via social media.Lastly,the study’s findings reveal the fundamentals of how visitors make decisions,which are essential for enhancing the competitive advantages of cultural destinations.Significant findings from the study are beneficial to the growth of cultural tourism in Davao City.
文摘Social media has become an inseparable part of modern life,but concerns have arisen regarding its influence on mental health.This article aims to delve into the intricate relationship between social media and mental health,highlighting both the benefits it brings and the risks it poses.Drawing upon existing research,this study reveals that excessive reliance on social media can contribute to mental health issues such as anxiety and depression,social isolation,and the spread of violent or hateful speech.Therefore,individuals need to be mindful of these potential hazards and take measures to protect their mental well-being.Through a comprehensive analysis of the positive and negative effects of social media on mental health,this article puts forward practical solutions to alleviate any adverse consequences.For instance,individuals are encouraged to use social media moderately,cultivate healthy digital habits,and limit their screen time.Moreover,governments and society as a whole should actively promote mental health awareness and reinforce regulations around social media usage to minimize potential negative impacts.In conclusion,while social media brings convenience and various benefits,it cannot be ignored that it also has the potential to impact mental health.By recognizing and addressing the associated risks,individuals,governments,and society can work together to foster a culture of healthy social media practices and safeguard mental well-being.The innovative approaches proposed in this article serve as a helpful guide for further exploration in this intriguing field.
文摘When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People communicate with others on social media through messages and comments. So bullies use social media as a rich environment to bully others, especially on political issues. Fights over Cyberbullying on political and social media posts are common today. Most of the time, it does a lot of damage. However, few works have been done for monitoring Bangla text on social media & no work has been done yet for detecting the bullying Bangla text on political issues due to the lack of annotated corpora and morphologic analyzers. In this work, we used several machine learning classifiers & a model. That will help to detect the Bangla bullying texts on social media. For this work, 11,000 Bangla texts have been collected from the comments section of political Facebook posts to make a new dataset and labelled the data as either bullied or not. This dataset has been used to train the machine learning classifier. The results indicate that Random Forest achieves superior accuracy of 91.08%.
文摘This study used both the “digital divide” and “attribution theory” to analyze the propensity of social media use by disaster-prone communities. The study focused on the variables that may affect how social media is used for disaster management. Structural equation modeling (SEM) was utilized in the study to analyze the data and test the hypotheses after using a survey questionnaire to collect the data. The study’s findings show that: 1) communities that are vulnerable to disasters are less likely to use social media for disaster management, 2) personal effort and intention to use social media for disaster management are positively correlated, and 3) task complexity and intention to use social media for disaster management are negatively correlated. The study added to the body of knowledge regarding the role social media plays in disaster management.
