scholarly journals Classifying Patient and Professional Voice in Social Media Health Posts

2021 ◽  
Author(s):  
Beatrice Alex ◽  
Donald Whyte ◽  
Daniel Duma ◽  
Roma English Owen ◽  
Elizabeth A.L. Fairley
Keyword(s):  
Social Media ◽  
Skin Diseases ◽  
Document Classification ◽  
Research Field ◽  
Training Data ◽  
Combined Training ◽  
Patient Voice ◽  
Starting Point ◽  
Data Source ◽  
Professional Voice

Abstract Background: Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of socialmedia data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a Convolutional Neural Network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases). Results: We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreedroughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.Conclusion: The main conclusion resulting from this work is that using more data for training a classifier does not necessarily result in best possible performance. In the context of classifying social media posts by patient and professional voice, we showed that it is best to train separate models per data source (Reddit andTwitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

scholarly journals Classifying patient and professional voice in social media health posts

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Beatrice Alex ◽  
Donald Whyte ◽  
Daniel Duma ◽  
Roma English Owen ◽  
Elizabeth A. L. Fairley
Keyword(s):  
Social Media ◽  
Skin Diseases ◽  
Document Classification ◽  
Training Data ◽  
Social Media Data ◽  
Patient Voice ◽  
Starting Point ◽  
Data Source ◽  
Professional Voice ◽  
Media Data

Abstract Background Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases). Results We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only. Conclusion The main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

A Note on Document Classification with Small Training Data

2011 ◽  
Vol 131 (8) ◽  
pp. 1459-1466
Author(s):  
Yasunari Maeda ◽  
Hideki Yoshida ◽  
Masakiyo Suzuki ◽  
Toshiyasu Matsushima
Keyword(s):  
Document Classification ◽  
Training Data

scholarly journals The Promoter, Celebrity, and Joker Roles in Journalists’ Social Media Performance

2021 ◽  
Vol 7 (1) ◽  
pp. 205630512199064
Author(s):  
Claudia Mellado ◽  
Alfred Hermida
Keyword(s):  
Social Media ◽  
Public Spheres ◽  
Professional Norms ◽  
Starting Point ◽  
Private And Public ◽  
Media Performance

One of the main challenges of studying journalistic roles in social media practice is that the profession’s conceptual boundaries have become increasingly blurred. Social media has developed as a space used by audiences to consume, share, and discuss news and information, offering novel locations for journalists to intervene at professional and personal levels and in private and public spheres. This article takes the “journalistic ego” domain as its starting point to examine how journalists perform three specific roles on social media: the promoter, the celebrity, and the joker. To investigate these roles in journalistic performance, the article situates their emergence and operationalization in a broader epistemological context, examining how journalists engage with, contest, and/or diverge from different professional norms and practices, as well as the conflict between traditional and social media-specific roles of journalists.

scholarly journals Health and longevity studies in C. elegans: the “healthy worm database” reveals strengths, weaknesses and gaps of test compound-based studies

2021 ◽  
Vol 22 (2) ◽  
pp. 215-236
Author(s):  
Nadine Saul ◽  
Steffen Möller ◽  
Francesca Cirulli ◽  
Alessandra Berry ◽  
Walter Luyten ◽  
...  
Keyword(s):  
Healthy Aging ◽  
Model Organism ◽  
Research Field ◽  
Aging Research ◽  
Genetic Manipulations ◽  
C Elegans ◽  
Starting Point ◽  
Health Related ◽  
Phenotype Measurement ◽  
Or Gene

AbstractSeveral biogerontology databases exist that focus on genetic or gene expression data linked to health as well as survival, subsequent to compound treatments or genetic manipulations in animal models. However, none of these has yet collected experimental results of compound-related health changes. Since quality of life is often regarded as more valuable than length of life, we aim to fill this gap with the “Healthy Worm Database” (http://healthy-worm-database.eu). Literature describing health-related compound studies in the aging model Caenorhabditis elegans was screened, and data for 440 compounds collected. The database considers 189 publications describing 89 different phenotypes measured in 2995 different conditions. Besides enabling a targeted search for promising compounds for further investigations, this database also offers insights into the research field of studies on healthy aging based on a frequently used model organism. Some weaknesses of C. elegans-based aging studies, like underrepresented phenotypes, especially concerning cognitive functions, as well as the convenience-based use of young worms as the starting point for compound treatment or phenotype measurement are discussed. In conclusion, the database provides an anchor for the search for compounds affecting health, with a link to public databases, and it further highlights some potential shortcomings in current aging research.

scholarly journals Reinforcement Learning Approaches in Social Robotics

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1292
Author(s):  
Neziha Akalin ◽  
Amy Loutfi
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Social Robotics ◽  
Research Field ◽  
Social Robots ◽  
Learning Approaches ◽  
Reward Function ◽  
Optimal Behavior ◽  
Learning Challenges ◽  
Starting Point

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

scholarly journals Quality and Operations Management in Food Supply Chains: A Literature Review

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hongfu Huang ◽  
Dong Li ◽  
Chunming Shi ◽  
Sarah J. Wu
Keyword(s):  
Literature Review ◽  
Supply Chains ◽  
Operations Research ◽  
Operations Management ◽  
Food Supply ◽  
Research Field ◽  
Food Supply Chains ◽  
The Past ◽  
Starting Point

We present a literature review on quality and operations management problems in food supply chains. In food industry, the quality of the food products declines over time and should be addressed in the supply chain operations management. Managing food supply chains with operations management methods not only generates economic benefit, but also contributes to environmental and social benefits. The literature on this topic has been burgeoning in the past few years. Since 2005, more than 100 articles have been published on this topic in major operations research and management science journals. In this literature review, we concentrate on the quantitative models in this research field and classify the related articles into four categories, that is, storage problems, distribution problems, marketing problems, and food traceability and safety problems. We hope that this review serves as a reference for interested researchers and a starting point for those who wish to explore it further.

The emergence of entrepreneurial team as a research field – way forward

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Willy Das ◽  
Satyasiba Das ◽  
Manojit Chattopadhyay
Keyword(s):  
Research Agenda ◽  
Research Field ◽  
Academic Disciplines ◽  
Future Research ◽  
Cross Sectional ◽  
Content Type ◽  
Future Research Agenda ◽  
Starting Point ◽  
Entrepreneurial Team ◽  
Research Stream

PurposeThe purpose of this paper is to review and critique the existing literature on entrepreneurial teams (ET) by taking a multi-disciplinary viewpoint and provide a future research agenda based on the identified themes and trends.Design/methodology/approachA systematic literature review (SLR) was undertaken using “business source complete”. Further scrutiny and application of exclusion criteria led to a final sample consisting of 139 papers from 27 different journals belonging to not just entrepreneurship and strategic management but also other disciplines like OB, finance, sociology, psychology, etc. Using qualitative thematic analysis, the authors identified 11 major themes.FindingsThe paper reviews both the eleven themes and the linkages between the themes. Thereby identifying areas that have been understudied and those that have received comparatively more attention. The review revealed that the research stream possesses certain conceptual and methodological concerns apart from its cross-sectional and primarily bivariate nature. Five such main concerns have been identified and discussed in detail. Other elements of the resulting research agenda include calls for more clinical process-oriented research, further attention to context, shifting the level of analysis, and a need to integrate across disciplines.Originality/valueThis paper incorporates a broad insight of ET across academic disciplines to show how future contributions could benefit by incorporating research from other fields. In doing so, provides a starting point for more nuanced discussions around the interrelationships between the different conversations that are taking place in the ET literature.

How social media advertising features influence consumption and sharing intentions: the mediation of customer engagement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chunli Ji ◽  
Susana Mieiro ◽  
Guihai Huang
Keyword(s):  
Social Media ◽  
Behavioral Intention ◽  
Marketing Research ◽  
Research Field ◽  
Mediating Effect ◽  
Customer Engagement ◽  
Interactive Marketing ◽  
Media Dependency ◽  
Content Type ◽  
Social Media Advertising

PurposeSocial media advertising (SMA) has become overly critical in the interactive marketing research field. This paper aimed to construct a research architecture model and to investigate the mediating effect of customer engagement between SMA and consumer behavioral intention in the context of Macao's casino integrated resorts.Design/methodology/approachThe authors collected data from 300 Chinese-speaking visitors of Macao's casino integrated resorts through a face-to-face survey. The hypotheses derived from the conceptual model were tested through two-stage structural equation modeling. The authors considered age and gender as control variables.FindingsThis study found that entertainment and promotional rewards had significant direct effects on consumption intention. Social media dependency did affect directly not only consumption intention but also sharing intention. Customer engagement on SMA mediated the effects of two SMA features (entertainment and promotional rewards) and one feature of SMA viewers (social media dependency) on consumption intention. As to extraneous variables, neither age nor gender significantly influenced consumer behavioral intention.Practical implicationsThe casino integrated resort managers should enhance the entertainment elements and provide reasonable promotional rewards to increase SMA's effectiveness. Managers should also consider the social media usage habits of the targeted customers. Further academic research on casino integrated resorts in other regions may use this study as a basis for investigating the mediation of customer engagement on SMA.Originality/valueThis study contributed to understanding the mediating mechanism of customer engagement on SMA by conceptualizing customer engagement on SMA as a unique idea and provided a conceptual framework for further theoretical and empirical research in the interactive marketing research field.

A sentiment analysis system for social media using machine learning techniques: Social enablement

2018 ◽  
Vol 34 (3) ◽  
pp. 569-581 ◽  
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Tool ◽  
Data Set ◽  
Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

Subjective Text Mining for Arabic Social Media

2017 ◽  
Vol 13 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Nourah F. Bin Hathlian ◽  
Alaaeldin M. Hafez
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Research Area ◽  
Arabic Text ◽  
Classification Methods ◽  
Learning Mechanisms ◽  
Subjective Analysis ◽  
Related Information ◽  
Data Source ◽  
Rich Content

The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

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