Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases (Preprint)

2018 ◽  
Author(s):  
Albert Moreira ◽  
Raul Alonso-Calvo ◽  
Alberto Muñoz ◽  
Jose Crespo
Keyword(s):  
Social Media ◽  
Social Network ◽  
Relevant Information ◽  
Online Discussions ◽  
Biomedical Knowledge ◽  
Expert Opinions ◽  
Health Related ◽  
Health Social Network ◽  
Discussion System ◽  
Clinical Cases

BACKGROUND Internet and Social media is an enormous source of information. Health Social Networks and online collaborative environments enable users to create shared content that afterwards can be discussed. While social media discussions for health related matters constitute a potential source of knowledge, characterizing the relevance of participations from different users is a challenging task. OBJECTIVE The aim of this paper is to present a methodology designed for quantifying relevant information provided by different participants in clinical online discussions. METHODS A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. These indicators make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. RESULTS Proposed indicators have been applied to two discussions extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. CONCLUSIONS The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a Health Social Network.

scholarly journals Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases

2018 ◽  
Vol 15 (12) ◽  
pp. 2787
Author(s):  
Albert Moreira ◽  
Raul Alonso-Calvo ◽  
Alberto Muñoz ◽  
José Crespo
Keyword(s):  
Social Network ◽  
Clinical Case ◽  
Relevant Information ◽  
Online Discussions ◽  
Biomedical Knowledge ◽  
Expert Opinions ◽  
Health Social Network ◽  
Discussion System ◽  
Key Indicators ◽  
Clinical Cases

The Internet and social media is an enormous source of information. Health social networks and online collaborative environments enable users to create shared content that afterwards can be discussed. The aim of this paper is to present a novel methodology designed for quantifying relevant information provided by different participants in clinical online discussions. The main goal of the methodology is to facilitate the comparison of participant interactions in clinical conversations. A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. Particularly, three new indicators have been proposed to make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. Proposed indicators have been applied to one discussion extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a health social network. This work can be applied to assess collaborative diagnoses, discussions among patients, and the participation of students in clinical case discussions. It permits moderators and educators to obtain a quantitatively measure of the contribution of each participant.

scholarly journals INTERNET USE AND MENTAL HEALTH IN AFRICA AND INDIA

2021 ◽  
Author(s):  
Wesley Monroe Shrum ◽  
Jonathan Teye Yevuyibor ◽  
Shriya Thakkar
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Social Network ◽  
Network Size ◽  
Personal Networks ◽  
Older Individuals ◽  
Media Usage ◽  
Social Network Size ◽  
Health Related ◽  
Internet And Social Media

Prior research has shown that mental health in urban slums is associated with the a share number of older individuals in personal networks. This presentation will examine the extent to which that association is mediated through Internet and social media use. We conducted face to face interviews with residents (minimum 18 years) in two high density, low income areas of Accra (West Africa) and Trivandrum (Kerala, India), where local teams have conducted repeated studies of personal networks since 1994. Our preliminary results show that mobile phones are primary way in which respondents communicate with members of their core networks. Further preliminary results show that while research in high income areas has generally shown the importance of larger networks for positive mental health, our analysis of urban slums reveals a different pattern. First, there is no general association of larger networks with reduced symptoms of anxiety and depression. Second, one particular group of relationships is strongly associated with depression and anxiety: ties with older individuals. The questions we explore are: (1) To what extent are indicators of mental health related to indicators of Internet and social media usage? (2) To what extent are indicators of mental health related to indicators of social network size and composition? (3) To what extent are indicators of Internet and social media usage related to indicators of indicators of social network size and composition?

scholarly journals Perception of Social Media as a Source of Relevant Information

2019 ◽  
Vol 22 (1) ◽  
pp. 133-144
Author(s):  
Tihomir Vranešević ◽  
Nenad Perić ◽  
Tajana Marušić
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Social Networking ◽  
Social Networking Sites ◽  
Relevant Information ◽  
Global Community

Abstract Social media today represent a global community of different nationalities - the size of China in terms of population, and social networking sites are online venues where users can create and post content. Social networks have also become one of the most popular ways for people to socialize, connect with friends and family, purchase items and gather relevant information about current and political topics and views. The most popular and biggest social network is Facebook and its influence in every pore of our society is evident, e.g. potential misuse of its user’s data in different purpose including manipulation in political purposes. This paper will also cover the findings of a survey conducted in Croatia and Serbia about the perception of social media and social networks as a source of gathering relevant information.

Covid Vaccine and Certificate: Opened or Closed Debate?

2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Francisco Medina Suarez
Keyword(s):  
Social Media ◽  
Control Agency ◽  
Relevant Information ◽  
Public Institutions ◽  
Expert Opinions ◽  
The World ◽  
Opposite Position ◽  
Closed Position ◽  
Information Media ◽  
Legal Guidelines

This article aims to show the basic aspects and legal guidelines over the debate of Covid vaccine and certificate adding some relevant information from vaccine control agency and expert opinions. The reason of this approach steeps in the closed position of many citizens including public institutions, information media, social media, etc. in the world over the need to get vaccine and to marginalize people with opposite position. For that reason this manuscript proposes to make a modest and succinct exposition over the situation to understand both views. All it without being able to address the abundant legal, medical, etc... Information on this matter that would force to elaborate an article of equidistant dimensions to the present manuscript.

scholarly journals A taxonomy for classifying user groups in location-based social media

2020 ◽  
Vol 1 ◽  
pp. 1-27
Author(s):  
Thomas Gründemann ◽  
Dirk Burghardt
Keyword(s):  
Social Media ◽  
Relevant Information ◽  
Media Content ◽  
Five Dimensions ◽  
The Social ◽  
Health Related ◽  
User Groups ◽  
German Parliament ◽  
Representative Samples ◽  
Selection Of

Abstract. Location-based social media provide great opportunities to monitor and map social, natural or health-related events. Due to the vast amount of data, it is appropriate for many researchers to use a judiciously selected sample of data. However, many of the datasets from social media sources do not consist of representative samples of the overall population because they do not take into account the users who generate the social media content. The consequences can be a bias of particular user groups and a misinterpretation of the analysis results. To overcome these shortcomings, this paper develops a taxonomy of user groups in social media based on a thorough literature analysis. The different approaches can be summarized to the five dimensions: character, connectivity, communication, content and coordinates. The expected use of the taxonomy is to support the selection of social media datasets by choosing only those user groups that provide relevant information and to improve the analysis by identifying significant groups. Both application areas are illustrated by using a dataset that includes the members of the German parliament who registered on Twitter.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

Trend of Parental Refusal or Delay of Childhood Vaccinations

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Meagan Marie Daoust
Keyword(s):  
Public Health ◽  
Social Media ◽  
Healthcare System ◽  
Educational Context ◽  
Health Consequences ◽  
Missed Opportunities ◽  
The Public ◽  
Health Related ◽  
Parental Refusal

The healthcare trend of parental refusal or delay of childhood vaccinations will be investigated through a complex Cynefin Framework component in an economic and educational context, allowing patterns to emerge that suggest recommendations of change for the RN role and healthcare system. As a major contributing factor adding complexity to this trend, social media is heavily used for health related knowledge, making it is difficult to determine which information is most trustworthy. Missed opportunities for immunization can result, leading to economic and health consequences for the healthcare system and population. Through analysis of the powerful impact social media has on this evolving trend and public health, an upstream recommendation for RNs to respond with is to utilize reliable social media to the parents’ advantage within practice. The healthcare system should focus on incorporating vaccine-related education into existing programs and classes offered to parents, and implementing new vaccine classes for the public.

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

New Democracy in Digital Era through Social Media and News Online

Humaniora ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Abitassha Az Zahra ◽  
Eko Priyo Purnomo ◽  
Aulia Nur Kasiwi
Keyword(s):  
Social Media ◽  
Social Network ◽  
Communication Networks ◽  
Social Communication ◽  
Development Policy ◽  
Online News ◽  
Digital Era ◽  
The Social ◽  
New Democracy

The research aimed to explain the pattern of social communication on the issue of rejection of the PLTU Batang development policy. It used data on Twitter accounts involved in the rejection of the PLTU Batang development policy. In analyzing existing data, qualitative methods and social analysis networks were used. To see social networks in the rejection of the PLTU Batang development policy, the research used the NodeXL application to find out the patterns of social communication networks in #TolakPLTUBatang. From the results, it can be seen that in the dissemination of social networking information, the @praditya_wibby account is the most central account in the social network and has a strong influence on the social network. The @praditya_wibby account has a role in moving the community through Twitter to make a critical social movement. This means that in the current digital era, democracy enters a new form through the movement of public opinion delivery through social media. Besides, by encouraging the role of online news, the distribution of information becomes faster to form new perceptions of an issue. This is evident from the correlation network where the @praditya_wibby account has correlations with several compass online media accounts, tirto.id, okezonenews, vice, antaranews, BBCIndonesia, and CNN Indonesia.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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