Website and Social Media Search Terms

2016 ◽  
Vol 4 ◽  
pp. 46-48
Author(s):  
Brian Van Brunt ◽  
◽  
Peter Langman ◽  
Keyword(s):  
Social Media ◽  
Search Terms

Beyond Disciplinary Boundaries: Mapping Educational Science in the Discourse on Social Media

2019 ◽  
Vol 121 (14) ◽  
pp. 1-24 ◽  
Author(s):  
Martin Rehm ◽  
Stefania Manca ◽  
Diana L. Brandon ◽  
Christine Greenhow
Keyword(s):  
Social Media ◽  
Previous Literature ◽  
Academic Disciplines ◽  
Comprehensive Overview ◽  
Disciplinary Boundaries ◽  
Search Terms ◽  
Current State ◽  
Literature Reviews ◽  
Number Of Publications ◽  
Media Integration

Social media has attracted considerable scholarly interest. Previous research has demonstrated the need for a more comprehensive overview of social media research across diverse disciplines. However, there is a lack of research that identifies the scope of social media integration across educational settings and how it relates to research in other academic disciplines. Harnessing the search terms of previous literature reviews, this study collected data on 80,267 articles from the Web of Science Core Collection database using search terms that were based on previous literature reviews. The data were analyzed using a combination of co-citation and bibliometric analyses via a mixed-methods approach. Our results show that there has been a constant increase in the number of publications concerned with social media, both as a transversal topic and within the educational sector. We are also able to show a range of topical domains in which the vast majority of research on social media is conducted. Our findings have practical implications for scholars and practitioners alike. Scholars can benefit from these types of analyses to identify authors and topic clusters that might otherwise have been unrecognized. Similarly, practitioners can benefit from this overview of the current “state-of-the-art” on social media.

scholarly journals Social Media Vaccine Websites: A Comparative Analysis of Public and Moderated Websites

2018 ◽  
Vol 46 (3) ◽  
pp. 454-462 ◽  
Author(s):  
Jo Ann Shoup ◽  
Komal J. Narwaney ◽  
Nicole M. Wagner ◽  
Courtney R. Kraus ◽  
Kathy S. Gleason ◽  
...  
Keyword(s):  
Social Media ◽  
The Internet ◽  
Discussion Boards ◽  
Discussion Forums ◽  
Chi Square ◽  
Inaccurate Information ◽  
Search Terms ◽  
Quantitative Results ◽  
Vaccine Information ◽  
Observation Period

The internet is an important source of vaccine information for parents. We evaluated and compared the interactive content on an expert moderated vaccine social media (VSM) website developed for parents of children 24 months of age or younger and enrolled in a health care system to a random sample of interactions extracted from publicly available parenting and vaccine-focused blogs and discussion forums. The study observation period was September 2013 through July 2016. Three hundred sixty-seven eligible websites were located using search terms related to vaccines. Seventy-nine samples of interactions about vaccines on public blogs and discussion boards and 61 interactions from the expert moderated VSM website were coded for tone, vaccine stance, and accuracy of information. If information was inaccurate, it was coded as corrected, partially corrected or uncorrected. Using chi-square or Fisher’s exact tests, we compared coded interactions from the VSM website with coded interactions from the sample of publicly available websites. We then identified representative quotes to illustrate the quantitative results. Tone, vaccine stance, and accuracy of information were significantly different (all p < .05). Publicly available vaccine websites tended to be more contentious and have a negative stance toward vaccines. These websites also had inaccurate and uncorrected information. In contrast, the expert moderated website had a more civil tone, minimal posting of inaccurate information, with very little participant-to-participant interaction. An expert moderated, interactive vaccine website appears to provide a platform for parents to gather accurate vaccine information, express their vaccine concerns and ask questions of vaccine experts.

A content analysis of Arabic YouTube videos for cancer treatment

2019 ◽  
Vol 24 (4) ◽  
pp. 267-273
Author(s):  
Ajayeb S. Abu Daabes ◽  
Faten F. Kharbat
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Cancer Treatment ◽  
Evidence Based ◽  
Objective Data ◽  
Video Content ◽  
Subjective Data ◽  
Content Type ◽  
Search Terms ◽  
Youtube Videos

Purpose The purpose of this paper is to describe and assess Arabic videos related to cancer treatment to gain insights about the nature of health information as it is shared on YouTube. Accordingly, future strategies for different bodies are suggested to promote effective communication. Design/methodology/approach The approach is to select a representative sample of YouTube videos for certain search terms related to cancer treatment in the Arabic language. In order to identify the search terms, Google Trends is utilized. To retrieve the most relevant videos, a simple python tool is developed using YouTube API V3. For this study, the first 150 relevant videos are quantitatively and qualitatively analyzed. Objective data and subjective data are collected for each video and analyzed. Objective data include video title, URL, length, view count, like count, dislike count, comment count and the associated tags. For content analysis, coding themes are defined for the subjective data as follows: video format, video authorship and video content. Video content includes three categories: types of treatments, targeted part and evidence-based indicators. Findings The study included 150 videos, from which 30 videos were not content related; therefore, 120 videos remain in the analysis. Using rounding values, it can be observed that the average video lasted 10 min, had 184,966 views, was commented on 263 times, was liked by 2,295 users and disliked by 148 users. Non-professional individuals (46 percent) posted less than half of the videos, whereas public institutions posted only 18 percent of videos. More than half of videos (56 percent) promoted using herbal, botanical, and other natural products for cancer treatment. The majority of YouTube video formats were videos (52 percent), followed by audio with captions (30 percent). News and stories were the dominant videos, with (16 percent), and other types of videos were mostly testimonials and private centers promotions. Only 6 and 9 percent of videos targeted the genetic and immune systems, respectively. Out of the 120 analyzed videos, 86 percent did not mention any risk factor for the recommended treatment, and 73 percent did not offer the details of their usage direction. Research limitations/implications Researchers need to understand the information that is currently available on social media platforms related to the high-risk diseases in order to design initiatives, tools, and actions to allow an easy effective transfer of knowledge. Practical implications Recounting in-depth knowledge of YouTube cancer treatment contents will allow policy makers, YouTube management, medical organizations, and government agencies to understand the viewers’ behavior of YouTube and their needs to provide accurate and trustworthy information to adopt evidence-based resources. Social implications Creating the suitable content, in terms of health promotion strategies, associated with the appropriate format and understandable language that people need will be one of the major responsibilities of YouTube management, government and professional bodies. The well-designed health messages will enhance users’ engagement and attention to health issues from trusted sources. Originality/value There is very less information about Arabic messages in social media, YouTube in particular, specifically regarding cancer treatment. Thus, this study is one of the first studies to explore how Arabic messages are presented on YouTube. The aim of the assessment is to extract the current status and suggest future strategies for different bodies to have effective communication toward the Arabic communities.

scholarly journals In Silico Method for Sentiment and Emotion Analysis in Medicine Using Social Media (Preprint)

2020 ◽  
Author(s):  
Anne Xuan-Lan Nguyen ◽  
Xuan-Vi Trinh ◽  
Sophia Y. Wang ◽  
Albert Y. Wu
Keyword(s):  
Social Media ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Free Text ◽  
Emotion Analysis ◽  
Search Terms ◽  
Patient Values ◽  
Ibm Watson ◽  
Medical Subspecialty

BACKGROUND Clinical data present in social media is an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes and preferences. OBJECTIVE We describe a novel and broadly applicable method for sentiment analysis and emotion detection to free text from online medical health forums and the factors to consider during its application. METHODS We mined the full discussion and user information of all posts containing search terms related to a specific medical subspecialty (oculoplastics) from MedHelp, the largest online platform for patient health forums. We employed a variety of data cleaning and processing to define the relevant subset of results and prepare those results for sentiment analysis. We executed sentiment and emotion analysis through IBM Watson Natural Language Understanding service to generate sentiment and emotion scores for the posts and their associated keywords. Keywords were aggregated using natural language processing tools. RESULTS 39 oculoplastics-related search terms resulted in 46,381 eligible posts within 14,329 threads, written by 18,319 users (117 doctors; 18,202 patients) and 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify most prominent topics, including specific symptoms, medication and complications. The sentiment and emotion scores of these keywords and eligible posts were further analyzed to provide concrete examples of the methodology’s potential to allow better understanding of patients’ attitudes. CONCLUSIONS This comprehensive report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during application include evaluating the scope of the search, selecting search terms and understanding their different linguistic usages, and establishing robust selection, filtering and processing criteria for posts and keywords tailored to the results.

The Importance of Trending Topics in the Gatekeeping of Social Media News Engagement: A Natural Experiment on Weibo

2020 ◽  
pp. 009365022093372
Author(s):  
Tian Yang ◽  
Yilang Peng
Keyword(s):  
Social Media ◽  
Natural Experiment ◽  
User Engagement ◽  
The Public ◽  
News Consumption ◽  
Search Terms ◽  
Trending Topics ◽  
News Outlet

Digital gatekeepers have greatly shaped the gatekeeping process of news consumption and news engagement, but how digital gatekeepers work is understudied. This study focuses on one example of digital gatekeepers, trending topics on social media, which aggregate the most popular search terms and present them to the public. We utilize a natural experiment on Weibo by analyzing user engagement data of 36,239 posts in three consecutive weeks, during which trending topics were removed for 1 week. We show that trending topics implemented two layers of gatekeeping: trending topics increased user engagement with top news posts within each news outlet and widened the engagement gap between popular posts and less popular ones (intra-outlet gatekeeping), and the increases in engagement with top news items were most salient among the least popular news outlets, thus reducing the inequality among outlets (inter-outlet gatekeeping).

scholarly journals A Algorithm for Emotion Prediction using Twitter Dataset

2019 ◽  
Vol 8 (4) ◽  
pp. 7243-7246
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Weight Matrix ◽  
Edge Weight ◽  
Learning Approach ◽  
Huge Amount ◽  
A Algorithm ◽  
Search Terms ◽  
Machine Learning Approach ◽  
Novel Algorithm

In today's internet world almost each and everyone uses Smartphone and they are all also active in various social media. In general social media contains a huge amount of data that can be extracted and utilized to find various data insights including polarity emotion etc... This research paper mainly investigates in emotion predection using a machine learning approach . Here a novel algorithm was introduced to predict the emotion of tweets . The algorithm mainly deals with emotion Prediction by utilizing various parameters like unigram , bigram , edge weight matrix , frequency matrix and so on . Finally , the result was predicted with the emotions of the tweets . While testing with various search terms this algorithm performs well in Predicting the emotion like anger, happiness and so on .

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

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mallory Farrar ◽  
Leslie Lundt ◽  
Ericha Franey ◽  
Chuck Yonan
Keyword(s):  
Social Media ◽  
Tardive Dyskinesia ◽  
English Language ◽  
Prolonged Exposure ◽  
Small Sample Size ◽  
Small Sample ◽  
Self Report ◽  
Search Terms ◽  
Social Media Platforms ◽  
The Impact

Abstract Background Tardive 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. Methods In 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. Results A 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. Conclusion Although 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.

scholarly journals Social Media Validation for Pre-Screened Ontologies As a Driver for Biomolecular Interaction Significance in COVID-19 and Hematological Malignancies

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4936-4936
Author(s):  
Yan Leyfman ◽  
Samarth Sandeep ◽  
Peter Rizk ◽  
Carlo Khoury ◽  
Chandler Howard Park
Keyword(s):  
Social Media ◽  
Hematological Malignancies ◽  
Human Phenotype Ontology ◽  
Biomolecular Interactions ◽  
Phenotype Ontology ◽  
Human Phenotype ◽  
Blood Cancer ◽  
Search Terms ◽  
Or Genes ◽  
Embl Database

Abstract With the rise of social media use during the COVID-19 pandemic, impressions from online content can affect behavioral changes resulting in exacerbating disparities in care. Thus, there exists a need to utilize social media platforms, like Twitter, to help augment preparedness, especially at the intersection between oncology and COVID-19, where tweets could help hint at potential biomolecular interactions. To address this, a study was developed to assess relationship and ontologies on the interaction between hematological malignancies and COVID-19 on Twitter. Ontologies are groupings of terms and related identifiers, such as genes, for general search terms, such as "Blood Cancer", were found utilizing the Human Phenotype Ontology. These were combined with the term "COVID-19" and used as search terms for Twitter's Standard Search API. The resulting tweets were cross-checked to assess if they included any of the other terms or genes related to the starting ontologies to then determine how many terms or genes each tweet was associated with. Once the most associated tweets to the ontologies were found, the genes related to those ontologies were utilized to find biological structures within the AlphaFold EMBL database, before being used in binding using HEX Docking software's shape based binding tool in 3D. Finally, Root Mean Square (RMS) Deviations were performed between the top 2000 conformations for each bound structure to determine if the binding was statistically significant. Results showed strong clustering of top tweets around keyword combinations. In the case of the starting entry, "Blood COVID-19", the ontologies that were found were linked to 45 terms that each had 100 or more tweets linked to them (Figure 1a). One such term of significance was Acute Myeloid Leukemia, which was linked to the gene BRCA1. The biological significance of the molecular interaction between BRCA1 and SARS CoV-2 was determined using the predicted protein structure from the AlphaFold-EMBL database for BRCA1 and the RCSB Protein Bank structure for the SARS CoV-2 spike (PDB# 6VSB), which can be found in Figure 1b. This interaction was found to be significant based on the average RMS Deviation of 82.97 Angstroms that ranged across the top 2000 conformation. Each model had an average RMS of 85.05 Angstroms between BRCA1 and the COVID-19 spike, with binding occurring on the spike's carbohydrate recognition domain within its S1 segment that is typically used for cell entry. Thus, human phenotype ontology was effective in classifying tweets to specific biomolecular interactions. Therefore, this approach could be utilized to proactively influence treatment designs for blood cancer patients infected with COVID-19, as well as in other areas where medical illnesses are already well defined by ontologies or other literature data. Forward looking, future studies will help to ensure that terms that are not well characterized by ontologies can still be utilized in this type of analysis by employing de novo ontology production methods. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals Tweeting Neurology: Boon or Bane (Preprint)

2020 ◽  
Author(s):  
Biswamohan Mishra ◽  
Monica Saini ◽  
Carolynne Doherty ◽  
Robert Pitceathly ◽  
Roopa Rajan ◽  
...  
Keyword(s):  
Health Care ◽  
Social Media ◽  
Inappropriate Use ◽  
Journal Clubs ◽  
Search Terms ◽  
Social Media Platforms ◽  
Potential Risks ◽  
It Applications ◽  
Channel Interactions ◽  
Share Information

BACKGROUND Twitter is a free, open-access social media provides an opportunity for advocacy, education, and collaboration. However, it is likely not utilized to full advantage by many disciplines in medicine and pitfalls exist in its use. In particular, there has not been a review of Twitter use and it applications in the field of neurology. OBJECTIVE This review seeks to provide an understanding of the current use of Twitter in the field of neurology to assist neurologists in engaging with this potentially powerful application to support their work. METHODS References for this Review were identified by searches of PubMed, Google Scholar, Embase, Medline and the Twitter site between March 2006 and July 2020 and references from relevant articles. The search terms “Twitter”, “neurology”, “journal clubs”, “tweetorials”, “Tweet chats”, “misuse”, “unprofessional”, “social media”, “health care”, were used. RESULTS Neurologists have taken to Twitter to educate, promote research, share information rapidly, and reach a broader potential global audience. Twitter has added a new dimension to learning and education in neurology in a practical and interactive manner. However, the “pros” of the Twitterverse must be balanced with the potential risks, which are common to all social media platforms (a graphical rendering is provided in Figure S1). CONCLUSIONS Twitter has opened up new scopes for neurologists through multi-channel interactions particularly in promoting, furthering and communicating research, patient care, learning and sharing information and recent advancements. However, guidelines should be formulated to prevent its unregulated and inappropriate use.

scholarly journals Fragmentation in the Future of Work: A Horizon Scan Examining the Impact of the Changing Nature of Work on Vulnerable Workers

2020 ◽  
Author(s):  
Arif Jetha ◽  
Ali Shamaee ◽  
Silvia Bonaccio ◽  
Monique AM. Gignac ◽  
Lori Tucker ◽  
...  
Keyword(s):  
Social Media ◽  
Research Team ◽  
Green Economy ◽  
Job Displacement ◽  
Future Of Work ◽  
Gray Literature ◽  
Search Terms ◽  
The Future ◽  
The Impact ◽  
Vulnerable Workers

Abstract Background. The future of work is characterized by social, technological, economic, environmental and political changes that are expected to disrupt all aspects of the working world. Our study aims to understand how the future of work impacts vulnerable workers. Methods. We conducted a horizon scan to systematically identify and synthesize diverse sources of evidence including academic research, gray literature and social media. Search terms were generated by members of the multidisciplinary research team, and combined with work outcome, future- and change-related and vulnerable worker search terms. Six search portals were used to uncover peer reviewed and gray literature across diverse disciplines. Search terms were also entered into Twitter’s standard search interface to identify social media resources. Literature was screened for eligibility (i.e., English language, documented a change in the nature of work, industrialized context and description of impact to vulnerable workers). Each relevant article was synthesized, and trend categories were developed by through iterative discussions among the research team. Results. An initial search yielded 4,800 articles after removing duplicates. Following a title and abstract relevancy screen, 3,195 articles were excluded. A total of 342 articles were fully reviewed. A synthesis of articles found nine trend categories which included digital transformation of the economy, artificial intelligence (AI)/machine learning (ML)-enhanced automation, AI-enabled human resource management systems, skill requirements for the future of work; globalization 2.0, climate change and the green economy, Gen Zs and the work environment; populism and the future of work, and external shocks to accelerate the changing nature of work (The COVID-19 example). Some workers may be more likely to experience vulnerability in the future of work including greater exposure to job displacement or wage depression. However, some potentially positive future of work trends also existed and could be beneficial for the labor market engagement of certain groups. Discussion. The changing nature of work can be fragmented for different groups of workers. Our research offers an important step towards understanding and supporting the involvement of vulnerable workers in the future of work.

Sign in / Sign up

Export Citation Format

Share Document