scholarly journals “You wouldn’t celebrate September 11” - Testing Online Polarisation Between Opposing Ideological Camps on YouTube

2020 ◽  
Author(s):  
Ana-Maria Bliuc ◽  
Laura G. E. Smith ◽  
Tina Moynihan
Keyword(s):  
Group Process ◽  
Online Communication ◽  
September 11 ◽  
Social Identification ◽  
Colonial History ◽  
Pathway Model ◽  
Social Media Data ◽  
Intergroup Interaction ◽  
Dual Pathway ◽  
Media Data

Online communication is increasingly associated with growing polarisation in society. In this research, we test a dual-pathway model of online polarisation via intergroup and intragroup interaction of supporters of opposing ideological camps on YouTube. The interaction occurs over a video parody promoting a campaign to change the date of Australia Day celebrations, a divisive issue entailing contrasting narratives about Australian identity, meanings of the Australia Day, and interpretations of colonial history. To capture ideological polarisation, we conducted computerised linguistic analysis of polarised talk in the form of comments and replies (N=1,027) from supporters and opponents of the campaign. The indicators used to capture polarisation are social identification, position certainty, and psychological distance (as reflected by increased anxiety and hostility). Our results show that most polarisation (in the form of increased hostility) occurs in conditions of expression of outgroup dissent (the intergroup interaction pathway) and the most debated content on the online forum revolves around themes relevant to group identity. In addition to contributing to the understanding of group process in an online context, another key contribution of this research is providing a theory-driven method and blueprint to detect polarisation in social media data.

scholarly journals “You wouldn’t celebrate September 11”: Testing online polarisation between opposing ideological camps on YouTube

2020 ◽  
Vol 23 (6) ◽  
pp. 827-844
Author(s):  
Ana-Maria Bliuc ◽  
Laura G. E. Smith ◽  
Tina Moynihan
Keyword(s):  
Group Process ◽  
Online Communication ◽  
September 11 ◽  
Social Identification ◽  
Colonial History ◽  
Pathway Model ◽  
Social Media Data ◽  
Intergroup Interaction ◽  
Dual Pathway ◽  
Media Data

Online communication is increasingly associated with growing polarisation in society. In this research, we test a dual-pathway model of online polarisation via intergroup and intragroup interaction of supporters of opposing ideological camps on YouTube. The interaction occurs over a video parody promoting a campaign to change the date of Australia Day celebrations, a divisive issue entailing contrasting narratives about Australian identity, meanings of Australia Day, and interpretations of colonial history. To capture ideological polarisation, we conducted computerised linguistic analysis of polarised talk in the form of comments and replies ( N = 1,027) from supporters and opponents of the campaign. The indicators used to capture polarisation are social identification, position certainty, and psychological distance (as reflected by increased anxiety and hostility). Our results show that most polarisation (in the form of increased hostility) occurs in conditions of expression of outgroup dissent (the intergroup interaction pathway) and the most debated content on the online forum revolves around themes relevant to group identity. In addition to contributing to the understanding of group process in an online context, another key contribution of this research is providing a theory-driven method and blueprint to detect polarisation in social media data.

scholarly journals Social media as a big public health data source: review of the international bibliography

2015 ◽  
Author(s):  
Evika Karamagioli
Keyword(s):  
Public Health ◽  
Social Media ◽  
Big Data ◽  
Data Extraction ◽  
Online Communication ◽  
World Health ◽  
Health Crisis ◽  
Social Media Data ◽  
Data Source ◽  
Media Data

Background: As the use of social media creates huge amounts of data, the need for big data analysis has to synthesize the information and determine which actions is generated. Online communication channels such as Facebook, Twitter, Instagram etc provide a wealth of passively collected data that may be mined for public health purposes such as health surveillance, health crisis management, and last but not least health promotion and education. Objective: We explore international bibliography on the potential role and perceptive of use for social media as a big data source for public health purposes. Method: Systematic literature review. Data extraction and synthesis was performed with the use of thematic analysis. Results: Examples of those currently collecting and analyzing big data from generated social content include scientists who are working with the Centers for Disease Control and Prevention to track the spread of flu by analyzing what user searches, and the World Health Organization is working on disaster management relief. But what exactly do we do with this big social media data? We can track real-time trends and understand them quicker through the platforms and processing services. By processing this big social media data, it is possible to determine specific patterns in conversation topics, users behaviors, overall trends and influencers, sociodemographic characteristics, lifestyle behaviors, and social and cultural constructs. Conclusion: The key to fostering big data and social media converge is process and analyze the right data that may be mined for purposes of public health, so as to provide strategic insights for planning, execution and measurement of effective and efficient public health interventions. In this effort, political, economic and legal obstacles need to be seriously considered.

scholarly journals Social media as a big public health data source: review of the international bibliography

2015 ◽  
Author(s):  
Evika Karamagioli
Keyword(s):  
Public Health ◽  
Social Media ◽  
Big Data ◽  
Data Extraction ◽  
Online Communication ◽  
World Health ◽  
Health Crisis ◽  
Social Media Data ◽  
Data Source ◽  
Media Data

Background: As the use of social media creates huge amounts of data, the need for big data analysis has to synthesize the information and determine which actions is generated. Online communication channels such as Facebook, Twitter, Instagram etc provide a wealth of passively collected data that may be mined for public health purposes such as health surveillance, health crisis management, and last but not least health promotion and education. Objective: We explore international bibliography on the potential role and perceptive of use for social media as a big data source for public health purposes. Method: Systematic literature review. Data extraction and synthesis was performed with the use of thematic analysis. Results: Examples of those currently collecting and analyzing big data from generated social content include scientists who are working with the Centers for Disease Control and Prevention to track the spread of flu by analyzing what user searches, and the World Health Organization is working on disaster management relief. But what exactly do we do with this big social media data? We can track real-time trends and understand them quicker through the platforms and processing services. By processing this big social media data, it is possible to determine specific patterns in conversation topics, users behaviors, overall trends and influencers, sociodemographic characteristics, lifestyle behaviors, and social and cultural constructs. Conclusion: The key to fostering big data and social media converge is process and analyze the right data that may be mined for purposes of public health, so as to provide strategic insights for planning, execution and measurement of effective and efficient public health interventions. In this effort, political, economic and legal obstacles need to be seriously considered.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Sign in / Sign up

Export Citation Format

Share Document