scholarly journals Tweeting back: predicting new cases of back pain with mass social media data

2015 ◽  
Vol 23 (3) ◽  
pp. 644-648 ◽  
Author(s):  
Hopin Lee ◽  
James H McAuley ◽  
Markus Hübscher ◽  
Heidi G Allen ◽  
Steven J Kamper ◽  
...  
Keyword(s):  
Risk Factors ◽  
Social Media ◽  
Back Pain ◽  
Health Interventions ◽  
Future Research ◽  
Preventive Interventions ◽  
Public Health Interventions ◽  
Social Media Data ◽  
Media Data ◽  
Global Health Problem

Background Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide online interventions. Methods We used a case-crossover study design for a sample of 742 028 tweets about back pain to quantify the risks associated with a new tweet about back pain. Results The odds of tweeting about back pain just after tweeting about selected physical, psychological, and general health factors were 1.83 (95% confidence interval [CI], 1.80-1.85), 1.85 (95% CI: 1.83-1.88), and 1.29 (95% CI, 1.27-1.30), respectively. Conclusion These findings give directions for future research that could use social media for innovative public health interventions.

Who is Tweeting? A Scoping Review of Methods to Establish Race and Ethnicity from Twitter Datasets (Preprint)

2021 ◽  
Author(s):  
Su Golder ◽  
Robin Stevens ◽  
Karen O'Conor ◽  
Richard James ◽  
Graciela Gonzalez-Hernandez
Keyword(s):  
Social Media ◽  
Scoping Review ◽  
Race And Ethnicity ◽  
Best Practice ◽  
English Language ◽  
Census Data ◽  
Future Research ◽  
Social Media Data ◽  
Lower Accuracy ◽  
Media Data

BACKGROUND Background: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population, but the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. OBJECTIVE Objectives: We sought to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. METHODS Methods: We present a scoping review to identify the methods used to extract race or ethnicity from Twitter datasets. We searched 17 electronic databases and carried out reference checking and handsearching in order to identify relevant articles. Sifting of each record was undertaken independently by at least two researchers with any disagreement discussed. The included studies could be categorized by the methods the authors applied to extract race or ethnicity. RESULTS Results: From 1249 records we identified 67 that met our inclusion criteria. The majority focus on US based users and English language tweets. A range of types of data were used including Twitter profile -pictures or information from bios (such as names or self-declarations), or location and/or content in the tweets themselves. A range of methodologies were used including using manual inference, linkage to census data, commercial software, language/dialect recognition and machine learning. Not all studies evaluated their methods. Those that did found accuracy to vary from 45% to 93% with significantly lower accuracy identifying non-white race categories. The inference of race/ethnicity raises important ethical questions which can be exacerbated by the data and methods used. The comparative accuracy of different methods is also largely unknown. CONCLUSIONS Conclusion: There is no standard accepted approach or current guidelines for extracting or inferring race or ethnicity of Twitter users. Social media researchers must use careful interpretation of race or ethnicity and not over-promise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers, and be guided by concerns of equity and social justice.

A mobile visualization platform for exploring social media data

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Yonghong Tong ◽  
Muhammet Bakan
Keyword(s):  
Social Media ◽  
Data Visualization ◽  
Mobile Device ◽  
Large Scale ◽  
Future Research ◽  
Social Media Data ◽  
Human Behaviors ◽  
Big Picture ◽  
Analysis Platform ◽  
Media Data

With the increasing application of using mobile device and social media, large amount of continuous information about human behaviors is available. Data visualization provides an insightful presentation for the large-scale social media datasets. The focus of this paper is on the development of a mobile-device based visualization and analysis platform for social media data for the purpose of retrieving and visualizing visitors’ information for a specific region. This developed platform allows users to view the “big picture” of the visitors’ locations information. The result shows that the developed platform 1) performs a satisfied data collection and data visualization on a mobile device, 2) assists users to understand the varieties of human behaviors while visiting a place, and 3) offers a feasible role in imaging immediate information from social media and leading to further policy-making in related sectors and areas. Future research opportunities and challenges for social media data visualization are discussed.Keywords: Social media, data visualization, mobile device

scholarly journals From FAIR data to fair data use: Methodological data fairness in health-related social media research

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110103
Author(s):  
Sabina Leonelli ◽  
Rebecca Lovell ◽  
Benedict W Wheeler ◽  
Lora Fleming ◽  
Hywel Williams
Keyword(s):  
Social Media ◽  
Well Being ◽  
Research Process ◽  
Future Research ◽  
Social Media Data ◽  
Data Practices ◽  
Health Related Research ◽  
Health Related ◽  
Use Of Social Media ◽  
Media Data

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced.

scholarly journals Consensus study of risk factors and symptoms of SARS-CoV-2 (COVID-19) using biomedical literature and social media data

2020 ◽  
Author(s):  
Jouhyun Jeon ◽  
Gaurav Baruah ◽  
Sarah Sarabadani ◽  
Adam Palanica
Keyword(s):  
Risk Factors ◽  
Social Media ◽  
Semantic Analysis ◽  
Demographic Variables ◽  
Biomedical Literature ◽  
Social Media Data ◽  
Consensus Analysis ◽  
Individual Outcomes ◽  
Retrospective Cohort Studies ◽  
Media Data

Background In December 2019, Coronavirus disease 2019 (COVID-19) outbreak started in China and rapidly spread around the world. Lack of any vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of COVID-19 patients. Methods We systematically integrated and analyzed published biomedical literature and public social media data to expand our landscape of clinical and demographic variables of COVID-19. Through semantic analysis, 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of COVID-19, and 84,140 tweet posts from 1,036 COVID-19 positive users were collected. In total, 59 symptoms were identified across both datasets. Findings Approximately 90% of clinical and demographic variables showed inconsistency across outcomes of COVID-19. From the consensus analysis, we identified clinical and demographic variables that were specific for individual outcomes of COVID-19. Also, 25 novel symptoms that have been not previously well characterized, but were mentioned in social media. Furthermore, we observed that there were certain combinations of symptoms that were frequently mentioned together among COVID-19 patients. Interpretation Identified outcome-specific clinical and demographic variables, symptoms, and combinations of symptoms may serve as surrogate indicators to identify COVID-19 patients and predict their clinical outcomes providing appropriate treatments.

scholarly journals Who is Tweeting? A Scoping Review of Methods to Establish Race and Ethnicity from Twitter Datasets

2021 ◽  
Author(s):  
su golder ◽  
Robin Stevens ◽  
Karen O'Connor ◽  
Richard James ◽  
Graciela Gonzalez-Hernandez
Keyword(s):  
Social Media ◽  
Scoping Review ◽  
Race And Ethnicity ◽  
Best Practice ◽  
Census Data ◽  
Future Research ◽  
Social Media Data ◽  
Lower Accuracy ◽  
Reference Checking ◽  
Media Data

Background: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population. Identifying the demographics of social media users enables us to measure the representativeness. Extracting race or ethnicity from social media data can be difficult and researchers may choose from a multitude of different approaches. Methods: We present a scoping review to identify the methods used to extract race or ethnicity from Twitter datasets. We searched 16 electronic databases and carried out reference checking in order to identify relevant articles. Sifting of each record was undertaken independently by at least two researchers with any disagreement discussed. The research could be grouped by the methods applied to extract race or ethnicity.Results: From 1093 records we identified 56 that met our inclusion criteria. The majority focus on Twitter users based in the US. A range of types of data were used including Twitter profile -pictures, bios, and/or location, and the content in the tweets themselves. The methods used were wide ranging and included using manual inference, linkage to census data, commercial software, language/dialect recognition and machine learning. Not all studies evaluated their methods. Those that did found accuracy to vary from 45% to 93% with significantly lower accuracy identifying non-white race categories. There may be some ethical questions over some of the methods used, particularly using photos or dialect, as well as questions surrounding accuracy.Conclusion: There is no standard approach or guidelines for extracting race or ethnicity from Twitter or other social media. Social media researchers must use careful interpretation of race or ethnicity and not over-promise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers, and be guided by concerns of equity and social justice.

scholarly journals Dashboarding The Online Strategic Communications of Anti-slavery Organizations During COVID-19

2020 ◽  
Author(s):  
Benjamin Lucas ◽  
Liana Bravo-Balsa ◽  
Vicky Brotherton ◽  
Nicola Wright ◽  
Todd Landman
Keyword(s):  
Social Media ◽  
Preliminary Evidence ◽  
Future Research ◽  
Strategic Communications ◽  
Social Media Data ◽  
Working Paper ◽  
Modern Slavery ◽  
Use Of Social Media ◽  
High Level ◽  
Media Data

In this working paper, we investigate high-level changes in the online strategic communications of organizations engaged with SDG 8.7 (ending modern slavery) during the COVID-19 crisis. We present preliminary evidence of important semantic and thematic shifts based on data from Twitter during this time, with an emphasis on developing the SOLACE (Social Listening and Communications Engagement) dashboard, and with recommendations for important future research involving the use of social media data as a basis for distilling organizational-agenda proxies based on digital campaigns and activism during times of crisis.

scholarly journals The Relationship Between Social Media Data and Crime Rates in the United States

2019 ◽  
Vol 5 (1) ◽  
pp. 205630511983458
Author(s):  
Yan Wang ◽  
Wenchao Yu ◽  
Sam Liu ◽  
Sean D. Young
Keyword(s):  
Public Health ◽  
United States ◽  
Social Media ◽  
The United States ◽  
Initial Study ◽  
Future Research ◽  
Social Media Data ◽  
Crime Data ◽  
Targeted Interventions ◽  
Media Data

Crime monitoring tools are needed for public health and law enforcement officials to deploy appropriate resources and develop targeted interventions. Social media, such as Twitter, has been shown to be a feasible tool for monitoring and predicting public health events such as disease outbreaks. Social media might also serve as a feasible tool for crime surveillance. In this study, we collected Twitter data between May and December 2012 and crime data for the years 2012 and 2013 in the United States. We examined the association between crime data and drug-related tweets. We found that tweets from 2012 were strongly associated with county-level crime data in both 2012 and 2013. This study presents preliminary evidence that social media data can be used to help predict future crimes. We discuss how future research can build upon this initial study to further examine the feasibility and effectiveness of this approach.

An Overview on Protecting User Private-Attribute Information on Social Networks

2021 ◽  
pp. 102-117
Author(s):  
Walaa Alnasser ◽  
Ghazaleh Beigi ◽  
Huan Liu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Differential Privacy ◽  
Future Research ◽  
Online Publishing ◽  
Social Media Data ◽  
User Data ◽  
Inference Attacks ◽  
Media Data

Online social networks enable users to participate in different activities, such as connecting with each other and sharing different contents online. These activities lead to the generation of vast amounts of user data online. Publishing user-generated data causes the problem of user privacy as this data includes information about users' private and sensitive attributes. This privacy issue mandates social media data publishers to protect users' privacy by anonymizing user-generated social media data. Existing private-attribute inference attacks can be classified into two classes: friend-based private-attribute attacks and behavior-based private-attribute attacks. Consequently, various privacy protection models are proposed to protect users against private-attribute inference attacks such as k-anonymity and differential privacy. This chapter will overview and compare recent state-of-the-art researches in terms of private-attribute inference attacks and corresponding anonymization techniques. In addition, open problems and future research directions will be discussed.

scholarly journals Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data

10.2196/20509 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e20509
Author(s):  
Jouhyun Jeon ◽  
Gaurav Baruah ◽  
Sarah Sarabadani ◽  
Adam Palanica
Keyword(s):  
Risk Factors ◽  
Social Media ◽  
Semantic Analysis ◽  
Demographic Variables ◽  
Biomedical Literature ◽  
Social Media Data ◽  
Consensus Analysis ◽  
Individual Outcomes ◽  
Retrospective Cohort Studies ◽  
Media Data

Background In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Lack of a vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of patients with COVID-19. Objective This study aims to investigate and analyze biomedical literature and public social media data to understand the association of risk factors and symptoms with the various outcomes observed in patients with COVID-19. Methods Through semantic analysis, we collected 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of patients with COVID-19, and 84,140 Twitter posts from 1036 COVID-19–positive users. Machine learning tools to extract biomedical information were introduced to identify mentions of uncommon or novel symptoms in tweets. We then examined and compared two data sets to expand our landscape of risk factors and symptoms related to COVID-19. Results From the biomedical literature, approximately 90% of clinical and demographic variables showed inconsistent associations with COVID-19 outcomes. Consensus analysis identified 72 risk factors that were specifically associated with individual outcomes. From the social media data, 51 symptoms were characterized and analyzed. By comparing social media data with biomedical literature, we identified 25 novel symptoms that were specifically mentioned in tweets but have been not previously well characterized. Furthermore, there were certain combinations of symptoms that were frequently mentioned together in social media. Conclusions Identified outcome-specific risk factors, symptoms, and combinations of symptoms may serve as surrogate indicators to identify patients with COVID-19 and predict their clinical outcomes in order to provide appropriate treatments.

scholarly journals Pattern Mining Approaches Used in Social Media Data

2020 ◽  
Vol 28 (Supp02) ◽  
pp. 123-152
Author(s):  
Jyotismita Chaki ◽  
Nilanjan Dey ◽  
B. K. Panigrahi ◽  
Fuqian Shi ◽  
Simon James Fong ◽  
...  
Keyword(s):  
Social Media ◽  
Pattern Mining ◽  
Strong Dependence ◽  
Future Research ◽  
Data Generation ◽  
Grammatical Structure ◽  
Social Media Data ◽  
Social Media Networks ◽  
Research Guidelines ◽  
Media Data

Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

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