Social Media Blogs Research Articles

From “Beall” and “Déjà Vu” to “PubPeer,” “Redactionmedicale,” and “Publishing with Integrity:” Social Media Blog Focusing on Academic Commentary – Another Side of the Coin

From “Beall” and “Déjà Vu” to “PubPeer,” “Redactionmedicale,” and “Publishing with Integrity:” Social Media Blog Focusing on Academic Commentary – Another Side of the Coin

Social Media and Cloud Computing Impact on Human Resource Management in Multinational Enterprises

To address the issues of high storage costs for human resource data, resource management of human resource systems, and employee social interaction in human resource management, this study proposes an adaptive intelligent HR data placement strategy based on genetic algorithms. Additionally, a fitness model and an employee social interaction model are presented. The fitness model utilizes genetic algorithms to optimize data storage strategy and server load balancing, resulting in reduced overall costs and efficient resource utilization. This is achieved by comprehensively considering data transmission latency and storage costs. The employee social interaction model analyzes social media blog post topics and employee ranks, and employs a co-training method to predict employee interactions. The experimental results indicated that the placement strategy can improve the HR data transfer by nearly 50% and effectively reduce the standard deviation of server load by about 10-15%. The accuracy, recall, F1 value, and precision of the employee social interaction model were 77.89%, 63.97%, 70.32%, and 71.78%, respectively. The proposed strategy and model demonstrated higher accuracy and robustness in predicting and analyzing employee social interactions, thus providing more reliable decision support for human resource management.

#BecomingYou: Discourses of authenticity, work, and success in South African consumer culture

Authenticity is now being used to describe things as diverse as politicians, wallets and holiday packages, and claims to authenticity have become increasingly marketable. Media sources from magazines to social media blogs are all inundated with a persisting, recurring message: that tapping into the “real you” will be the liberating force allowing you to live a happy and successful life. This paper draws on a social constructionist theoretical framework and discourse analytic method to critically analyse three interconnected discourses of personal authenticity (as success, change and work) explored in 10 marketing campaigns prominent in South African media. The constructions found in these media are important not just as reflections of current subject positions available in a particular context, but also in the (re)construction of these particular identities and the (re)production of particular social systems. More specifically, in this case, by closely tying authenticity to notions of success, work and change, these discourses produce “good neoliberal subjects”- ambitious, self-regulated, proactive and productive citizens committed to personal progress and contributing to a “functioning” society. In other words, through these discourses, individuals’ personal aspirations (e.g. self-improvement or happiness) become inextricably linked with capitalist modes of being (work and consumption).

Empowering ESL Students: Leveraging Social Media Blogs for Effective Opinion Writing Skills Development

This study aims to investigate the impact of integrating social media blogs into ESL education, with a specific focus on enhancing opinion writing skills and overall language proficiency. The research was conducted among two teachers and six students enrolled in the BS English programme at the Institute of Southern Punjab, Multan. A qualitative method was employed, utilizing in-depth interviews with the teachers and students, cross case analysis, and classroom observations to gather rich insights into the phenomenon. The study revealed positive perceptions from both teachers and students regarding the effectiveness of integrating social media blogs. Teachers reported a significant positive impact on opinion writing skills, and students emphasized improvements not only in opinion writing but also in overall language skills. Cross case analysis supported these perceptions, showcasing consistent patterns of success across diverse contexts. Classroom observations further validated these findings, highlighting active student participation and dynamic teacher student engagements during opinion writing sessions using social media blogs. Regarding language exposure, social media blogs were identified as a valuable tool for providing authentic language experiences. The interactive nature of these platforms immersed learners in genuine communicative situations, exposing them to diverse writing styles and cultural nuances. Innovative teaching strategies derived from the interactive nature of social media blogs included online debates, multimedia integration, and collaborative writing environments. The study holds implications for ESL educators, curriculum developers, and policymakers. Integrating social media blogs into ESL curricula is suggested as a strategic move to enhance opinion writing skills and overall language proficiency. The identified innovative teaching strategies offer practical insights for educators seeking to leverage the interactive features of social media blogs. This study contributes valuable insights into the dynamic intersection of social media, opinion writing skills, and language proficiency within the context of ESL education, offering a foundation for further research and informed pedagogical practices.

Stress Detection Based on social media Blogs

Major psychological health issues like stress, anxiety, etc. are a direct outcome of technology development and a huge increase in social media usage. It is possible to examine these problems and develop prevention plans. As a result of people's great desire to support SMEs, social media monitoring of blogs is essential to resolving these serious issues (Social Media Environments). Psychologists used conventional techniques like surveys and interviews, but these procedures are time-consuming and hysterical. In this paper, we reviewed a variety of stress detection techniques and discovered that they were unsuccessful for identifying stress in social media. In this study, we presented a method for effectively detecting stress in people by utilising the ontology. Using the ontology for stress detection among people and recommending the essential safety measures to stop users from committing suicide is what we presented as an effective stress detection approach in this study. The term matching search method known as ontology is utilised in social media to more accurately detect communications shared by users that are connected to stress.

Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing

People’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis.

MeXicana Fashions: Politics, Self-Adornment, and Identity Construction

MeXicana Fashions: Politics, Self-Adornment, and Identity Construction

Can the Screen Speak?: Museum of Material Memory and the Politics of Display

What if a museum is not built out of cement and stone, but codes on a virtual space? In what ways do virtual platforms operationalise the concept of a museum? The paper explores these questions based on the empirical case study of Museum of Material Memory (MMM) and how it operates on Instagram. To this end, the objectives of this paper are twin fold. Firstly, it investigates the characteristics that make MMM a museum as distinct from a social media blog or a website. And secondly, it explores the politics of curation in relation to conventional physical museums in conjunction with issues thrown up by Instagram as a medium of display. The paper argues that MMM’s virtual mode of operation challenges the methods of heritage conservation envisaged by museums by demonstrating how tangible heritage can be museumised through virtual means. In doing so, MMM lays emphasis not only on the object per se, but also on the relationship that the object may have with its setting and the people around it. Such a model of display democratises expertise by obfuscating the difference between producers and consumers of heritage.

Four-Class Emotion Classification Problem using Deep Learning Classifiers

Social media sites and blogs generate a vast amount of emotionally rich data in the form of tweets, status updates, blog posts etc. Such textual data are a good representative of emotions expressed by an individual or a group of people on any given topic. By analyzing the emotions within these textual data, we can get an idea about how an individual or a community expresses their views. Analytical techniques are widely used for analyzing emotions within these texts. However, due to the imbalanced nature of the training datasets the supervised classifiers fail to clearly classify the different emotion classes. As a result, these classifiers demonstrate a poor performance in identifying emotions within the texts. Here, using a constructed heterogeneous training dataset from well-known training datasets we have trained two deep learning models namely the Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) to address a four-class emotion (Anger, Sadness, Happy, Surprise) classification problem. By appropriately tuning the hyper parameters of the deep learning classifiers our study reveals that the CNN classifier has a slightly better performance (77%) than the RNN classifier (76%) for a four-class emotion classification problem.

Can Mental Tricks Effect Social Change?

Can Mental Tricks Effect Social Change?

Integration of Social Media into Daily Activity of Adolescents and Self-Regulation Characteristics

Introduction. Diverse domains of life become more digitalized, social media (social networks, blogs, and information sharing services) have become an intrinsic element of daily life and activities such as education. Positive and negative consequences of social media use were discovered, which increased the awareness of their involvement in adolescents’ and young people’s psychological well-being and socializing. However, the characteristics of self-regulation in the context of increased social media usage remain unexplored. The study examined the association between self-regulation characteristics and the degree to which social media is integrated into adolescents’ everyday activities. Materials and Methods. The study included 336 teenagers (12–17 years old). Self-regulation features (the SSPM-2020 V.I. Morosanova questionnaire) and social network activity (The scale of integration of social media into daily activity, time spent on it, the numbers of social networks) were assessed. Correlation, comparative, and cluster analyses were used to process the data. Results. The integration of social media into an adolescent’s everyday activities varies according to their level of self-regulation. However, formal factors (time and quantity of social networks) do not change between self-regulation levels. Throughout adolescence, social media use is relatively consistent. Girls showed a stronger behavioral and emotional attachment to social media and deeper integration into daily routines. Discussion and Conclusion. Self-regulation development can lead to academic performance and increased self-control in the digital world. When examining the effects of distance learning and developing programs to enhance online learning, meaningful parameters for measuring adolescentsʼ social media integration into their everyday activities may be required.

A Location Prediction Methods: state of art

The rapid use of social media made location prediction is the key to research studies based on-location services like; advertising, recommendations, climatological forecast, and security system. Locations are the center of information for these applications that helps to gain profit from this information user location. According to millions of users who post tweets every day, Twitter is known as one of the most important and familiar social media blogs. Depending on the importance of catching the location of the users and the rapid usage of Twitter, Location prediction on Twitter has been a point of research in many research studies. This survey provides a comprehensive overview picture of the prediction of the user's location on Twitter. that focuses on tweet location prediction of the user. This occurs by; reviewing existing location-prediction techniques and the latest challenges. Finally, the conclusion of the survey and a list of the future research directions.

The critical role of Digital Health in Public Health

Digitalization in itself is not simply a means to an end but involves an operational change in workflows that is on the critical-path to the digital transformation of public health. The COVID-19 pandemic has been a unique opportunity to put a shining light on all the different technologies that were available and also consider a large number of new and innovative technologies that were developed in a breath-taking place over a period of one year. These different initiatives also brought relevant advances in public health needs such as epidemiological surveillance, rapid case identification, interruption of community transmission and communication with different audiences. Moreover, social media channels, blogs and websites confirmed to be a fertile ground to find data for research, but also to spread misinformation - and public health professionals should be ready to counter that threat effectively. This presentation aims to provide a comprehensive background on digital tools and strategies that can be used in national and local public health services and how stakeholder alignment across different sectors could propel forward the implementation of digital solutions.

Using the Viable System Model to Understand the Roles of Non-Human Actors in Online Communities

While myriad studies have explored the role of humans in online social media-based communities, fewer studies have examined the roles of non-human actors in those online places. This study uses the actor-network theory and the viable system model to analyze the roles of hashtags communities to gain a better understanding of the larger cybernetic ecosystem in which non-human actors operate. Actor-network theory provides the theoretical understanding that frames this study. The viable system model is a five-tier system based on methods of communication and control in both living and non-living entities. Data sources included fieldnotes, social media profiles, blogs, personal websites, personal communications, interviews, and demographic surveys. Findings suggest that non-human actors have an ability to affect change within the ecosystem, but non-human actors cannot be viewed as viable standalone systems. Viability is only possible when humans participate within the ecosystem at large.

Mining User Generated Contents in Online Healthcare forum using Text Mining Techniques

The development of online health care forum patients and health seekers can access the Internet and get health related information through their social media blogs and health care forums. These forum users are involved to post emotional based messages and health related posts. In this forum enormous amount of emotional based messages posted by different stakeholders that are patients, caregivers and healthcare experts. In this online healthcare forum patients /healthseekers post a question. Peers who reply to the questions asked that replies are based on more emotions oriented messages. For analyzing these messages to find the adoption based answers , Stakeholders and Topic identification in the messages and Sentiments (emotions) to the messages. For this Framework we use various text mining methods as preprocessing , NLP methods ,and Machine learning algorithms as SVM- RBF(radial Basis Function) Classification , K-medoid clustering method and Lexicon Data Analysis (LDA) is used .In this paper Medhelp online healthcare forum messages were taken up for analysis.

Patient perspective of tardive dyskinesia: results from a social media listening study

BackgroundTardive dyskinesia (TD) is a persistent and potentially disabling movement disorder associated with prolonged exposure to dopamine receptor blocking agents such as antipsychotics. With the expanding use of antipsychotics, research is needed to better understand patient perspectives of TD, which clinical assessments may fail to capture. Social media listening (SML), which is recognized by the US FDA as a method that can advance ongoing efforts for more patient-focused drug development, has been used to understand patient experiences in other disease states. This is the first study to use SML analysis of unsolicited patient and caregiver insights to help clinicians understand how patients describe their symptoms, the emotional distress associated with TD, and the impact on caregivers.MethodsIn this pilot study, a comprehensive search was performed for publicly available, English-language, online content posted between March 2017 and November 2019 on social media platforms, blogs, and forums. An analytics platform (NetBase™) identified posts containing patient or caregiver experiences of assumed TD using predefined search terms. All posts were manually curated and reviewed to ensure quality and validity of the post and to further classify key symptoms, sentiments, and themes.ResultsA total of 261 posts from patients/caregivers (“patient insights”) were identified using predefined search terms; 107 posts were used for these analyses. Posts were primarily from forums (47%) and Twitter (33%). Analysis of the most common sentiment-related terms (e.g. “feel” [n = 31], “worse” [n = 17], “symptom” [n = 14], “better” [n = 12]) indicated that 64% were negative, 33% were neutral, and 3% were positive. Theme analysis revealed that patients often felt angry about having TD from a medication used to treat a different condition. In addition, patients felt insecure, including feeling unaccepted by society and fear of being judged by others.ConclusionAlthough this study was limited by inherent methodological constraints (e.g., small sample size, reliance on patient self-report), the perspectives generated from analyzing social media may help convey the unmet needs of patients with TD. This analysis indicated that movement-related symptoms are the most common patient concern, resulting in strong feelings of anger and insecurity.

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this article, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore, we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">88.26</i> percent over the baselines of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.60</i> in experiment 1 and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">96.1</i> percent over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns.

PATIENTS’ JOURNEY THROUGH INFLAMMATORY BOWEL DISEASE (IBD): A QUALITATIVE STUDY

Abstract Background Inflammatory bowel disease (IBD) is a debilitating intestinal condition, manifesting as Crohn’s disease (CD), ulcerative colitis (UC) or indeterminate colitis (IC). The patient experience is impacted by a lack of awareness from other stakeholders despite growing global disease prevalence. To gain deeper insight of the patient experience, promote quality care, and enhance quality of life, we performed a qualitative study of the patient journey starting from pre-diagnosis through treatment. Methods U.S. patients with IBD were recruited via UC/CD support groups and organizations, social media platforms, blog followers, and personal networks. Participants were screened via an emailed survey and asked to self-identify as medically diagnosed on the basis of reported diagnostic testing. Interviews were conducted by qualitative researchers by phone or web conferencing. Open-ended questions were developed to support and gather information about our learning objectives—primarily, our desire to understand the unique experiences of UC/CD patients in their journey from symptom onset through diagnosis, treatment and maintenance (e.g. “Upon diagnosis, what were your immediate thoughts about the condition?”). This qualitative data were analyzed using Human-Centered Design methodology, including patient typologies (personas), forced temporal zoom (journey maps), forced semantic zoom (stakeholder system mapping), and affinity mapping for pattern recognition of unmet needs. Results A total of 32 patients were interviewed: N=17 CD patients, N=13 UC patients and N=2 IC patients. The interviewed population reflected regional, demographic, and disease-related diversity (Table 1). Five unique, mutually exclusive journeys were identified to understand and classify patient experiences: (1) Journey of Independence, (2) Journey of Acceptance, (3) Journey of Recognition, (4) Journey of Passion and (5) Journey of Determination (Figure 1). Patients with IBD expressed a need for increased awareness, education, and training for providers to shorten the path to diagnosis. Mental health support was found to be a critical gap in care, particularly for major treatment decisions (e.g., surgery). The inclusion of emotional support into the treatment paradigm was perceived as essential to long-term wellness. Patient attitudes and self-advocacy varied on their individual journeys; understanding these journeys may accelerate time to diagnosis and treatment. Conclusion Better understanding of patient journeys can help healthcare providers improve their approach to patient care and coordination.

The impact of COVID-19 on Consumer Behaviour on spending pattern with respect to Mobile Phones and the impact of Anti-Chinese Sentiment towards buying of foreign brands

COVID-19 pandemic has brought the world to a standstill. People are locked in their houses having a very low option to move out during the lockdown period. The objective of the study is to focus on the impact of COVID-19 on consumer behaviour towards the use of mobile phones and their view towards Made in India products. The consumption pattern of individuals might be altered due to COVID-19 as there is no knowledge of when everything will be resumed back to normal. A survey sheet was floated to collect individual data via responses using social media platforms, blogs and calls. These responses would provide data for further analysis to determine the outcome of the study. Hypothesis testing method is used for the data analysis and to interpret the results&#x0D;

Sentiment Analysis Techniques and Application-Survey and Taxonomy

Nowadays, social media platforms, blogs, and e-commerce are commonly use to express opinion on politics, movies, products, education respectively; for election forecasting, business boosting and improvement of teaching and learning. As a result, data generation becomes easier; producing big data which requires appropriate techniques and tools to analyse easily, accurately and timely. Thus, making sentiment analysis very demanding research area. This study will investigate on what basis (sentiment classification level) or area of application (data source) do supervised machine learning approaches particularly Support Vector Machine (SVM), Naïve Bayes, and Maximum Entropy algorithms, and other technique-lexicon-based approach give the best result in sentiment analysis. Based on the review of the literature there is a contradiction on the point that SVM generated the best result in analyzing student sentiment on document level. This study also discovers that sentiment analysis differs from system to system based on polarity (types of the classes to predict: positive or negative, subjective or objective), different levels of classification (sentence, phrase, or document level) and language that is processed. This research produces a taxonomy which serves as a guide for the choice of techniques in sentiment analysis. The taxonomy explores the sentiment classification levels and data preprocessing stages. It also explores that sentiment analysis techniques were organised in to three (3) groups; Machine learning, Lexicon and hybrid or combination. The machine learning techniques were sub-grouped in to two (2) namely; supervised and unsupervised. The supervised were organized in to two (2): Classification and Regression. un-supervised machine learning techniques includes clustering and association. The clustering technique consist of k-means. Decision tree which is a classification based under supervised type of machine learning technique consist of random forest,(Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion. The commonly used tools are Weka, Python compiler, and R programming tool.