Social Media Reviews Research Articles

PCR97 Patient and Caregiver Insight Into the Impact of Non-Muscle Invasive Bladder Cancer and Its Treatments: A Social Media Review

To gain insights into patient/caregiver experience of non-muscle invasive bladder cancer (NMIBC) and its treatments (e.g., intra-vesical Bacillus Calmette-Guerin [BCG]/cystectomy) using social media (SM) data.

Effects of Travel Website Quality and Perceived Value on Travel Intention with eWOM in Social Media and Website Reviews as Moderators

ABSTRACT This study aims to investigate how perceived value mediates the relationship between travel website quality and travel intention and how electronic word-of-mouth (eWOM) in social media and website reviews interact with the relationship between perceived value and travel intention. Structural equation modeling was applied to analyze data gathered from 371 valid questionnaires. The results revealed the following: (1) travel website quality positively affects utilitarian and hedonic values; (2) utilitarian and hedonic values directly influence travel intention and also function as mediators of the association between travel website quality and travel intention; and (3) eWOM in social media and website reviews moderate the association between perceived value and travel intention. This study contributes to research on the antecedents and consequences of perceived value in the context of e-commerce. Furthermore, our results offer insights into how eWOM in social media and website reviews contribute to travel intention. Theoretical and practical implications are discussed in detail.

Opinion mining of social media reviews using fuzzy inference and intent prediction using deep learning

Sentiment Analysis (SA) or opinion mining has emerged with the quick elevation in internet users, and the increasing power of Social Medias (SM) and also online review sites. SA aids in finding what other people think and comment on. Recently, to capture the sentiment information on the feedback, numerous SA techniques are developed. The major downside of such techniques is lower accuracy and higher training time. This paper proposes a proficient SA technique in SM reviews to trounce these downsides. Originally, utilizing tokenization, lemmatization, Stop Word (SW) removal, together with URL removal, the input dataset is preprocessed. Then, from that preprocessed data, the emoticon and non-emoticon features are extracted. Subsequently, those resulting features are ranked grounded on their meaning. After that, the classification is done by utilizing Bell Shaped Gaussian Kernel Membership Function which is the Enhanced system of Adaptive Neuro Fuzzy Inference System and is called GKMFANFIS. Lastly, for identifying the user's reason behind the feedback, the Emotional Classification (EC) is performed utilizing the Modified Deep Learning Neural Networks (MDLNN). In MDLNN, Elephant Swarms Water Search Optimization (ESWSO) is applied to optimize the weight values. The proposed methodology achieves better performance and efficiency when weighed against prevailing methodologies regarding the accuracy, recall, precision, together with an F-measure. The accuracy of the proposed GKMFANFIS along with ANFIS is 92% and it shows better result when compared with the existing BERT system.

Journey from Electronics to Healthcare Technology – Philips, Healthcare Product Maker

Background/Purpose: In 1891, Gerard Philips and his father Frederik created the Dutch multinational corporation Philips in Eindhoven. Its headquarters are in Amsterdam. Having divesting off its consumer electronics division, Philips is now focused completely on the health technology industry. The company has extensive experience in a wide range of healthcare-related fields, including cardiology, health technology, oncology, respiratory medicine, fertility and pregnancy. To make people's lives better through innovation, and to contribute to the creation of a world that is both more sustainable and healthier. Objective: This paper provides a case study of Philips' transformation from an electronics firm to a leading healthcare product producer. This paper also looks at the healthcare business as a whole, as well as the many technological advancement components of it. Design/Methodology/Approach: Secondary sources were used in this investigation, including journals and conference publications, annual reports, Philips Company websites, the internet, scholarly articles, and social media reviews. On the company, a SWOT analysis was performed. Findings/Results: The 131-year-old company’s growth as an electrical and electronic goods manufacturer has been meritorious. The company has ventured into the healthcare sector after 2010 and has a road ahead to become a pioneer in this sector. Conclusion: Philips Healthcare is a global player in the manufacture of healthcare equipment. The company has a robust R&D division which can aid in building more innovative healthcare products. By being more innovative the company can achieve its mission of improving global health and sustainability through technological advancements. Paper Type: Company analysis as a Research Case Study

Multimodal Sentiment Sensing and Emotion Recognition Based on Cognitive Computing Using Hidden Markov Model with Extreme Learning Machine

In today's competitive business environment, exponential increase of multimodal content results in a massive amount of shapeless data. Big data that is unstructured has no specific format or organisation and can take any form, including text, audio, photos, and video. Many assumptions and algorithms are generally required to recognize different emotions as per literature survey, and the main focus for emotion recognition is based on single modality, such as voice, facial expression and bio signals. This paper proposed the novel technique in multimodal sentiment sensing with emotion recognition using artificial intelligence technique. Here the audio and visual data has been collected based on social media review and classified using hidden Markov model based extreme learning machine (HMM_ExLM). The features are trained using this method. Simultaneously, these speech emotional traits are suitably maximised. The strategy of splitting areas is employed in the research for expression photographs and various weights are provided to each area to extract information. Speech as well as facial expression data are then merged using decision level fusion and speech properties of each expression in region of face are utilized to categorize. Findings of experiments show that combining features of speech and expression boosts effect greatly when compared to using either speech or expression alone. In terms of accuracy, recall, precision, and optimization level, a parametric comparison was made.

At a Basic Level, Academic Review of social media, the Cost of Connecting, Communicating, and Sharing Information

At a Basic Level, Academic Review of social media, the Cost of Connecting, Communicating, and Sharing Information

Sexual Harassment through Social Media Review from Electronic Transaction Information Law

<p><span lang="IN">The purpose of this study is to find out: first, the modus operandi of the perpetrator in committing sexual harassment on social media. Second, to find out the formulation of the offense in Article 27 paragraph (1) of Law Number 19 of 2016 concerning amendments to Law Number 11 of 2008 concerning Electronic Transaction Information (ITE) reaching out to forms of sexual harassment on social media. The research uses normative research with a statutory approach. The results of the study conclude, First, there are at least 3 (three) modus operandi in general: a) by sending text or images with negative content to the victim; b) spamming or writing inappropriate comments in the victim's comment field or social media; c) by approaching the opposite sex who is the target (victim). Approaching the opposite sex who is being targeted is generally accompanied by threats or rewards. Second, in relation to Article 27 paragraph (1) of the ITE Law, it cannot reach various forms of sexual harassment through social media and prevent the birth of new forms of similar crimes through social media. This is because these provisions do not meet the elements of lex certa and lex scripta, namely that a law must contain elements of both actions, conditions and consequences. In addition, a law must be strict, firm, clear and not contain various interpretations</span></p>

Critical Review of Sustainable Peace, Mediative Dialogue and Social Media

This paper attempts a critical review of the interplay between social media, sustainable peace and mediative dialogue. The paper is grounded in efforts to achieve Goal 16 of the United Nations Sustainable Development Goals: Peace Justice and Strong Institutions. It is the aim of Goal 16 of the United Nations Sustainable Development Goals to promote peaceful and inclusive societies for sustainable development, give justice to all and make possible effective, responsible and all-encompassing institutions at all levels. The paper traced the history of the Sustainable Development Goals to the United Nations (UN) Conference on the Human Environment held in 1972 in Stockholm, Sweden. The Sustainable Development Goals of the United Nations are developed from the Millennium Development Goals (MDGs) and other earlier initiatives of the United Nations. Peace is singled out among these goals, and mediative dialogue is a means to achieving this goal. The advent of the Internet, among other technologies, has significantly changed the ways people communicate globally. Social media in particular is taking a significant role in the ways people, groups and nations resolve conflicts. Through philosophical reflections and qualitative analysis, the paper recommends a framework for deploying mediative dialogue, through social media, for sustainable peace. It is, therefore, concluded that harnessing the strength of the philosophies of the phenomena of social media, sustainable peace and mediative dialogue is a veritable tool for conflict resolution and also for actualizing the aspirations for global peace as inspired by Goal 16 of the Sustainable Development Goals of the United Nations.

Ranking of Urban Brand Influence Based on Social Media Comment Mining

Social media reviews play an increasingly important role in ranking the influence of urban brands. In this study, the public social media review database is mined, and a regression model of urban influence is established. Firstly, this study introduces text mining and data collection. Comment data are collected from static websites and dynamic websites, and ICTCLAS word segmentation tool is used to preprocess the comment data. The algorithm of urban influence level is established, and finally the regression model of urban brand influence is established. A database of 10000 city-related reviews was used in the experiment. The gender thesaurus is established to further improve the accuracy of the experimental results. The feasibility of the model is verified from two aspects: expectation calculation and standardization. This paper analyzes the composition of citizens who comment on social media and the proportion of comment content and puts forward some suggestions to enhance the influence of the city. Finally, it summarizes the popular comments in different months in December and obtains the force points to enhance the influence of the city in different time periods from the structure.

A study on consumer preference on social media reviews and online ads towards electronic gadgets with special reference to Chengalpattu

Social media plays significant role in the modern era and it serves as reviews provider to the consumers by way of video reviews, blogs etc. The aim of this study is to understand consumer reviews on social media and online ads for electronic gadgets. For this purpose researcher has selected 110 respondents as sample size and it was surveyed through online questionnaire. Based on questionnaire, a hypothesis was framed. The data were analyzed and interpreted by applying various statistical tools. Findings of the study were that most of the consumers were aware of the new products by the way of social media and also through social media consumers are getting knowledge about the unique features, latest trends, comparison of other products, and usage of products.

Transformer-based deep learning models for the sentiment analysis of social media data

Sentiment analysis (SA) is a widely used contextual mining technique for extracting useful and subjective information from text-based data. It applies on Natural Language Processing (NLP), text analysis, biometrics, and computational linguistics to identify, analyse, and extract responses, states, or emotions from the data. The features analysis technique plays a significant role in the development and improvement of a SA model. Recently, GloVe and Word2vec embedding models have been widely used for feature extractions. However, they overlook sentimental and contextual information of the text and need a large corpus of text data for training and generating exact vectors. These techniques generate vectors for just those words that are included in their vocabulary and ignore Out of Vocabulary Words (OOV), which can lead to information loss. Another challenge for the classification of sentiments is that of the lack of readily available annotated data. Sometimes, there is a contradiction between the review and their label that may cause misclassification. The aim of this paper is to propose a generalized SA model that can handle noisy data, OOV words, sentimental and contextual loss of reviews data. In this research, an effective Bi-directional Encoder Representation from Transformers (BERT) based Convolution Bi-directional Recurrent Neural Network (CBRNN) model is proposed with for exploring the syntactic and semantic information along with the sentimental and contextual analysis of the data. Initially, the zero-shot classification is used for labelling the reviews by calculating their polarity scores. After that, a pre-trained BERT model is employed for obtaining sentence-level semantics and contextual features from that data and generate embeddings. The obtained contextual embedded vectors were then passed to the neural network, comprised of dilated convolution and Bi-LSTM. The proposed model uses dilated convolution instead of classical convolution to extract local and global contextual semantic features from the embedded data. Bi-directional Long Short-Term Memory (Bi-LSTM) is used for the entire sequencing of the sentences. The CBRNN model is evaluated across four diverse domain text datasets based on accuracy, precision, recall, f1-score and AUC values. Thus, CBRNN can be efficiently used for performing SA tasks on social media reviews, without any information loss.

Role of Social Media Reviews in Capturing Brand Trust & Brand Loyalty: A Case of Burger Joints in Karachi

Purpose: The purpose of this research study is to gain an insight into the role played by Social Media Reviews in capturing Brand Trust and Brand Loyalty. The study has been conducted in the fast food industry (local burger joints) available in Karachi, Pakistan. This research study is quantitative in nature.
 Design/Methodology/Approach: This research study is quantitative in nature. It is an explanatory research under which deductive hypotheses testing has been used. The research instrument used is questionnaire and data has been analyzed through Correlation and Regression Analysis, using SPSS software
 Findings: The findings of this research study suggest that there is a significant and positive relationship between Social Media Reviews (independent variable) and Brand Trust and Brand Loyalty (dependent variables). The findings also suggest that if consumers are more inclined towards the online reviews given by other consumers, their trust and loyalty towards the specific brand in question will tend to increase.
 Implications/Originality/Value: This research paper adds to the existing body of knowledge on the role of Social Media Reviews given by the consumers on different social media platforms. This research paper will add value by deciphering and explaining the relationship that exists between social media reviews and brand relationship outcomes like Brand Trust and Brand Loyalty

Mapping review of interventions to reduce the use of restrictive practices in children and young people's institutional settings: The CONTRAST study.

Restrictive practices are often used harmfully with children in institutional settings. Interventions to reduce their use do not appear to have been mapped systematically. Using environmental scanning, we conducted a broad-scope mapping review of English language academic databases, websites and social media, using systematic methods. Included records (N= 121) were mostly from the United States and contained details of 82 different interventions. Children's participation was limited. Reporting quality was inconsistent, which undermined claims of effectiveness. Overall, despite a multitude of interventions, evidence is limited. Leaders should consider the evidence, including children's perspectives, before introducing poorly understood interventions into children's settings.

Understanding tourist behaviour towards destination selection based on social media information: an evaluation using unsupervised clustering algorithms

PurposeThe study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to traveller's choice. The study also aims to understand the behaviour of clusters of the travellers towards destination selection and accordingly make the tour packages in order to improve tourists' satisfaction and gain viable benefits.Design/methodology/approachAgglomerative hierarchical clustering with Ward's minimum variance linkage algorithm and model-based clustering with parameterized finite Gaussian mixture models has been implemented to achieve the respective goals. The dimension reduction (DR) technique was introduced for better visualizing clustering structure obtained from a finite mixture of Gaussian densities.FindingsA total of 980 travellers have been clustered into 8 different interest groups according to their tourism destinations selection across East Asia based on individual social media feedback. For selecting the optimal number of clusters as well as the behaviour of the interested travellers groups, both these proposed methods have shown remarkable similarities. DR technique ensures the reduction in dimensionality with seven directions, of which the first two directions explained 95% of total variability.Practical implicationsTourism organizations focus on marketing efforts to promote the most attractive benefits to the clusters of travellers. By segmenting travellers of East Asia into homogeneous groups, it is feasible to choose a similar area to test different marketing techniques. Finally, it can be identified to which segments, new respondents or potential clients belong; consequently, the tourism organizations can design the tour packages.Originality/valueThe study has uniqueness in two aspects. Firstly, the study empirically revealed tourists' experience and behavioural intention to select tourism destinations and secondly, it finds quantifiable insights into the tourism phenomenon in East Asia, which helps tourism organizations to understand the buying behaviours of tourists' segments. Finally, the application of clustering algorithms to achieve the purpose of this study and the findings are very new in the literature on tourism, to understand the tourist behaviour towards destination selection based on social media reviews.

Beyond the “good story” and sales history: where is the reader in the publishing process?

ABSTRACT In recent years, critics’ attention has been drawn to a persistent lack of diversity in the publishing industry. These claims are based on ethnic and cultural homogeneity amongst authors, publishing company staff and content. While scholars have pointed to the role that publishers’ decision-making processes have played in reinforcing a lack of diversity, this article focuses on how such processes position the audience, and how they contribute to an absence of knowledge of or attention to the audience, which then serves as a factor contributing to homogeneity. It identifies practices within the industry that allow for this absence of knowledge, including a focus on the book as a cultural form and the industry’s use of proxies, such as sales data and social media reviews and discussion, in place of audience data. The article ends by suggesting ways in which the industry may be supported to increase its knowledge of actual and potential audiences without jeopardizing its objective of publishing “good stories”.

RealSelf Social Media Analysis of Rhinoplasty Patient Reviews

BackgroundSocial media has become increasingly important for patients when deciding whether they should undergo rhinoplasty. The purpose of this study is to analyze patient satisfaction of rhinoplasty procedures through RealSelf social media reviews. MethodsWe collected data from 583 rhinoplasty reviews published on the RealSelf portal. In posts dated between 2016 and 2020, we included those which were labeled as “Worth It” and “Not Worth It.” Posts that were labeled as “Unsure” or were left unlabeled were excluded from the study. In addition, posts not including the cost of their rhinoplasty were excluded. Taking into account patient demographics and cost of the procedure, we analyzed reasons for choosing to undergo surgery, reasons for choosing surgeons, and reasons for liking or disliking their procedure. ResultsOf the 583 reviews analyzed, most (45.4%) were categorized from the 18-24 years age group and there was an overall 93.8% satisfaction rate. While there was no statistically significant difference in the cost of rhinoplasty surgeries between “Worth It” and “Not Worth It” groups, the average cost of recorded rhinoplasties was US$ 8043 with a standard deviation ± $3296. According to our analysis, younger patients aged 18-24 years relied more on social media to choose their surgeons and desired a more natural appearance to their nose while older ones preferred compatible physician personalities and increased self-esteem for rhinoplasty. ConclusionThis study offers a unique perspective into the distinguishing characteristics of different age groups and the values they place in pursuing rhinoplasty, choosing their surgeons, and why they like/dislike their surgical outcomes.

“Pray to all four directions”: a qualitative study of syncretic care seeking by Vietnamese families for their children with autism spectrum disorder

Purpose The aim is to understand what therapies and interventions families in a low and middle income (LMIC) country, such as Vietnam resort to in their attempts to seek care for their children with ASD and why they choose these therapies. Methods We undertook semi-structured qualitative interviews with 27 parents of children with autism and an online survey of 112 parents as part of a broader ethnographic study over one year augmented with recent interactional observations and a review of social media. Results There is limited access to formal interventions for families with children with ASD in Vietnam. Rather, families resort to syncretic care using an average of 6.8 different interventions per child. These included: speech therapy; physical therapy; prescribed medicines; and ABA as well as geomancy, special dietary regimes, biochemical testing, stem cell therapies and religious and cultural practices. Despite having low incomes, the families surveyed spent an average USD 345 per month on interventions, many of which are not evidence-based. Desire to care and potentially “cure” their children within a context of stigma associated with ASD drives parents to seek all possible interventions for their children. Conclusions There remains a large gap in access to appropriate evidence-based interventions or trained therapists for families. Further information for parents, culturally appropriate guidelines for effective interventions, more trained rehabilitation professionals and regulation is required. IMPLICATIONS FOR REHABILITATION Within low and middle income countries (LMIC), such as Vietnam there is a lack of intervention services, difficulties in accessing appropriate specialists, poor quality care and the costs of care. Families seek a range of formal interventions and costly informal therapies as well as culturally specific practices to care for their child/ren and require information on effective therapies. In LMIC training programs to build capacity for allied health professionals need to be implemented on the latest evidence-based therapies. It is important to develop culturally appropriate Vietnamese guidelines to support interventions for children with ASD.

Global Medicinal Use of Bats: A Systematic Literature and Social Media Review

The hunting of bats for food and medicine is one of the greatest threats to bat conservation. While hunting for consumption is the focus of increased attention, the specific medicinal uses of bats are poorly documented, limiting mitigation efforts. Here, we determine the distribution of bat hunting for food and medicinal use and characterize medicinal use practices. We systematically surveyed English-language scientific literature and social media platforms utilizing keywords and hashtags in 27 languages. We found 198 papers and 1063 social media posts from 83 countries and territories. Although use for food was more common, with 1284 unique reports from 71 countries, bats were used to treat 42 ailments of 11 human body systems across 37 countries (453 reports). Asthma was the most common ailment, distantly followed by kidney conditions. Ten organs or body parts of bats were used medicinally, with bat meat (flesh) and fluids (blood, bile, and oil) the most common. Understanding the effects and drivers of specific bat hunting practices will help guide conservation and public health efforts in the communities where bats are hunted. By pinpointing the ailments bats are being used for, outreach and alternative treatments can be introduced to replace the use of bats.

Different Techniques of Sentimental Analysis using Deep Learning

World Wide Web like social media forums, review sites and blogs that generate a lot of data the type of users views, feelings, ideas and arguments about various social events, products, products, and politics.

Social Media Use and Mental Health: A Global Analysis.

Research indicates that excessive use of social media can be related to depression and anxiety. This study conducted a systematic review of social media and mental health, focusing on Facebook, Twitter, and Instagram. Based on inclusion criteria from the systematic review, a meta-analysis was conducted to explore and summarize studies from the empirical literature on the relationship between social media and mental health. Using PRISMA guidelines on PubMed and Google Scholar, a literature search from January 2010 to June 2020 was conducted to identify studies addressing the relationship between social media sites and mental health. Of the 39 studies identified, 20 were included in the meta-analysis. Results indicate that while social media can create a sense of community for the user, excessive and increased use of social media, particularly among those who are vulnerable, is correlated with depression and other mental health disorders.