Using Internet-Derived Data to Measure Religion: Understanding How Google Can Provide Insight into Cross-National Religious Differences

2021 ◽  
Author(s):  
Amy Adamczyk ◽  
Jacqueline Scott ◽  
Steven Hitlin
Keyword(s):  
Best Practices ◽  
Social Issues ◽  
Sources Of Information ◽  
Social Media Data ◽  
Religious Buildings ◽  
Derived Data ◽  
Media Data ◽  
Insight Into ◽  
Existing Data ◽  
Cross National

Abstract Internet and social media data provide new sources of information for examining social issues, but their potential for scholars interested in religion remains unclear. Focusing on cross-national religion data, we test the validity of measures drawn from Google and Twitter against well-known existing data. We find that Google Trend (GT) searches for the dominant religions’ major holidays, along with “Buddhism,” can be validated against traditional sources. We also find that GT and traditional measures account for similar amounts of variation, and the GT measures do not differ substantially from established ones for explaining several cross-national outcomes (e.g., fertility, circumcision, and alcohol use), as well as new ones (e.g., interest in religious buildings and sex). The Twitter measures do not perform as well. Our study provides insight into best practices for generating and using these measures, and offers evidence that internet-generated data can replicate existing measures that are less accessible and more expensive.

scholarly journals Innovation diffusion in tourism: how information about blockchain is exchanged and characterized on twitter

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Francesco Bolici ◽  
Chiara Acciarini ◽  
Lucia Marchegiani ◽  
Luca Pirolo
Keyword(s):  
Social Media ◽  
Technological Innovation ◽  
Technology Diffusion ◽  
Information Exchange ◽  
Public Discourse ◽  
New Technologies ◽  
Sources Of Information ◽  
Social Media Data ◽  
Content Type ◽  
Media Data

PurposeTechnological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.Design/methodology/approachTo capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.FindingsOur analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).Research limitations/implicationsThe paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.Practical implicationsThis paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.Social implicationsThe present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.Originality/valueWhile previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.

scholarly journals Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing (Preprint)

2020 ◽  
Author(s):  
Oladapo Oyebode ◽  
Chinenye Ndulue ◽  
Ashfaq Adib ◽  
Dinesh Mulchandani ◽  
Banuchitra Suruliraj ◽  
...  
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Language Processing ◽  
Social Issues ◽  
Public Perceptions ◽  
Psychosocial Issues ◽  
Social Media Data ◽  
Health Related ◽  
The Impact ◽  
Media Data

BACKGROUND The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. OBJECTIVE This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data. METHODS We apply natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collect over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we perform data preprocessing which involves applying NLP techniques to clean and prepare the data for automated theme extraction. Third, we apply context-aware NLP approach to extract meaningful keyphrases or themes from over 1 million randomly-selected comments, as well as compute sentiment scores for each theme and assign sentiment polarity (i.e., positive, negative, or neutral) based on the scores using lexicon-based technique. Fourth, we categorize related themes into broader themes. RESULTS A total of 34 negative themes emerged, out of which 15 are health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues are increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues include frustrations due to life disruptions, panic shopping, and expression of fear. Social issues include harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes include public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS We uncover various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommend interventions that can help address the health, psychosocial, and social issues based on the positive themes and other remedial ideas rooted in research. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, as well as in reacting to any future pandemics.

scholarly journals Views on social media and its linkage to longitudinal data from two generations of a UK cohort study

2020 ◽  
Vol 5 ◽  
pp. 44
Author(s):  
Nina H. Di Cara ◽  
Andy Boyd ◽  
Alastair R. Tanner ◽  
Tarek Al Baghal ◽  
Lisa Calderwood ◽  
...  
Keyword(s):  
Social Media ◽  
Cohort Study ◽  
Longitudinal Data ◽  
Cohort Studies ◽  
Focus Groups ◽  
Best Practice ◽  
Social Research ◽  
Sources Of Information ◽  
Social Media Data ◽  
Media Data

Background: Cohort studies gather huge volumes of information about a range of phenotypes but new sources of information such as social media data are yet to be integrated. Participant’s long-term engagement with cohort studies, as well as the potential for their social media data to be linked to other longitudinal data, could provide novel advances but may also give participants a unique perspective on the acceptability of this growing research area. Methods: Two focus groups explored participant views towards the acceptability and best practice for the collection of social media data for research purposes. Participants were drawn from the Avon Longitudinal Study of Parents and Children cohort; individuals from the index cohort of young people (N=9) and from the parent generation (N=5) took part in two separate 90-minute focus groups. The discussions were audio recorded and subjected to qualitative analysis. Results: Participants were generally supportive of the collection of social media data to facilitate health and social research. They felt that their trust in the cohort study would encourage them to do so. Concern was expressed about the collection of data from friends or connections who had not consented. In terms of best practice for collecting the data, participants generally preferred the use of anonymous data derived from social media to be shared with researchers. Conclusion: Cohort studies have trusting relationships with their participants; for this relationship to extend to linking their social media data with longitudinal information, procedural safeguards are needed. Participants understand the goals and potential of research integrating social media data into cohort studies, but further research is required on the acquisition of their friend’s data. The views gathered from participants provide important guidance for future work seeking to integrate social media in cohort studies.

scholarly journals OSoMe: The IUNI observatory on social media

2016 ◽  
Author(s):  
Clayton A Davis ◽  
Giovanni Luca Ciampaglia ◽  
Luca Maria Aiello ◽  
Keychul Chung ◽  
Michael D Conover ◽  
...  
Keyword(s):  
Social Media ◽  
Online Social Networks ◽  
Network Science ◽  
Computational Social Science ◽  
Collaborative Effort ◽  
Indiana University ◽  
Social Phenomena ◽  
Social Media Data ◽  
Derived Data ◽  
Media Data

The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory, now available at osome.iuni.iu.edu, is the result of a large, six-year collaborative effort coordinated by the Indiana University Network Science Institute.

scholarly journals OSoMe: The IUNI observatory on social media

2016 ◽  
Author(s):  
Clayton A Davis ◽  
Giovanni Luca Ciampaglia ◽  
Luca Maria Aiello ◽  
Keychul Chung ◽  
Michael D Conover ◽  
...  
Keyword(s):  
Social Media ◽  
Online Social Networks ◽  
Network Science ◽  
Computational Social Science ◽  
Collaborative Effort ◽  
Indiana University ◽  
Social Phenomena ◽  
Social Media Data ◽  
Derived Data ◽  
Media Data

The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory, now available at osome.iuni.iu.edu, is the result of a large, six-year collaborative effort coordinated by the Indiana University Network Science Institute.

scholarly journals Initial stages of designing a system for collecting and predictive analysis of social media data

2021 ◽  
pp. 73-84
Author(s):  
Ivan Kalytyuk ◽  
◽  
Galina Frantsuzova ◽  
Andrei Gun’ko ◽  
◽  
...  
Keyword(s):  
Social Media ◽  
Predictive Analytics ◽  
Predictive Analysis ◽  
Mining Machine ◽  
Large Set ◽  
Sources Of Information ◽  
Subsequent Work ◽  
Social Media Data ◽  
Future System ◽  
Media Data

This article discusses the design of a system for collecting and predictive analysis of social media. With the development of the Internet, as well as social media, it has become easier to access and distribute information because network users themselves are both creators and recipients of diverse information. To gain new knowledge that can be useful to users of social media, it is possible to use predictive analytics – a set of statistical analysis methods that extract new information from current and historical data. This method of analyzing social media data is at the stage of its development. Predictive analytics is based on automatic search for connections, anomalies and patterns between various factors. To form a predictive model, a large set of statistical modeling methods, data mining, machine learning, neural networks and other mechanisms are used. Together with various methods of collecting information from Internet resources, such as parsing and social network APIs, predictive analytics can offer the most interesting sources of information for the user. In order to combine the methods of predictive analysis and data collection methods, it is necessary to take a detailed approach to the system design process. The paper proposes a formal description of the data that a future system uses. In addition, the general architecture and algorithm of functioning are highlighted. Special attention is paid to a detailed description of one of the main parts of the system (the collection subsystem). The obtained results will be used in further design, and it is planned to further study the analytics subsystem. Subsequent work on this topic will make it possible to detail the architecture and algorithm of functioning.

scholarly journals Attitudes towards Brands and Advertisements: Qualitative and Thematic Analysis of Social Media Data

2019 ◽  
Author(s):  
Emmanuel Mogaji ◽  
Temitope Farinloye
Keyword(s):  
Social Media ◽  
Research Method ◽  
User Generated Content ◽  
Social Media Data ◽  
Perceptions And Attitudes ◽  
Public Data ◽  
Social Media Platforms ◽  
The Mind ◽  
Media Data ◽  
Insight Into

<div>Social media has been described as a platform for discussing ideas, communicating experiences and exchanging knowledge. It has changed the way individuals interact, providing massive amount of data and rich market insight as customers and brands engage and build relationships. This public declaration is of great concern for any organisation as it transfers the power to shape brand images from the hands of advertisers to the words of consumers’ online connections.</div><div>This chapter sets an agenda proposing the possibilities of qualitatively analysing user-generated content on social media platforms to provide insight into attitudes towards advertisements and their brands. Unlike participants being interviewed in a focus group, filling in questionnaires or neuroscience providing insight into how the mind perceives advertisements which typically requires expensive, bulky equipment and lab-type settings that limit and influence the experience, this is readily available public data which can be thematically analysed to add to existing knowledge.</div><div>Presenting the idea, publicly declared responses to the advertisements of UK banks on Facebook were analysed in order to gain insight into their perceptions and attitudes towards the advertisements and their brands. An outline of how to perform an analysis of user-generated content was provided to buttress the research method. Challenges and limitations of this research method were also considered.</div>

scholarly journals A biomedically oriented automatically annotated Twitter COVID-19 dataset

2021 ◽  
Vol 19 (3) ◽  
pp. e21
Author(s):  
Luis Alberto Robles Hernandez ◽  
Tiffany J. Callahan ◽  
Juan M. Banda
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Biomedical Research ◽  
Societal Implications ◽  
Automatic Annotation ◽  
Social Media Data ◽  
Gold Standard Dataset ◽  
To Come ◽  
Use Of Social Media ◽  
Media Data

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present (Long-). However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don’t generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.

scholarly journals Measurement Considerations for Quantitative Social Science Research Using Social Media Data

2020 ◽  
Author(s):  
Jonathan Ladd ◽  
Rebecca Ryan ◽  
Lisa Singh ◽  
Leticia Bode ◽  
Ceren Budak ◽  
...  
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Social Scientists ◽  
Social Media Data ◽  
Measurement Issues ◽  
Computer Scientists ◽  
Media Data

Harnessing social media data for social science research entails creating measures out of the largely unstructured, noisy data that users generate on different platforms. This harnessing, particularly of data at scale, requires using methods developed in computer science. But it also typically requires integrating these methods with assessments of measurement quality along social science criteria -- reliability, validity and unbiasedness. In this paper, we outline measurement issues that arise when using social media data. We show examples of how to construct measures and discuss different measurement considerations and best practices. We conclude with a discussion of ways to accelerate research in this space, highlighting contributions that can be made by both social scientists and computer scientists.

scholarly journals “It’s harder for the likes of us”: racially minoritised stem cell donation as ethico-racial imperative

BioSocieties ◽  
2021 ◽  
Author(s):  
Ros Williams
Keyword(s):  
Social Media ◽  
Stem Cell ◽  
Racial Identity ◽  
Social Media Data ◽  
Cell Donor ◽  
Course Of Action ◽  
Stem Cell Donation ◽  
Media Data ◽  
Insight Into ◽  
Donor Recruitment

AbstractHow best are we to understand appeals to participate in a biomedical project that are based both on invoking shared racial identity, and on framing engagement as the clear moral course of action? Stem cell donor recruitment, which often focuses on engaging racially minoritised communities, provides useful insight into this question. This article proposes that it is not an essential mutual racial identity between the person asking and the person asked at play. Rather, it is the creative ‘doing’ of relatedness between people at the scale of race as well as family that coalesces into powerful appeals to participate. Through analysis of ethnographic, documentary and social media data, the paper argues that this work relies at least partly on framing donation as a duty of being part of a racialised community, which I describe here as an ethico-racial imperative, in which both race and responsibility become intertwined to compel participation in the biomedical project of donor registration.

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