Consequences of Group Style for Differential Participation

Social Forces ◽  
2020 ◽  
Author(s):  
Hjalmar Bang Carlsen ◽  
Jonas Toubøl ◽  
Snorre Ralund
Keyword(s):  
Social Media ◽  
Group Interaction ◽  
Supervised Machine Learning ◽  
Political Protest ◽  
Social Media Data ◽  
Online Ethnography ◽  
Solidarity Movement ◽  
Differential Participation ◽  
Level Variable ◽  
Media Data

Abstract This article proposes a theory of how interaction in groups influences differential participation in political activism and interrogates this theory through an empirical analysis of online Facebook group interaction. We study the refugee solidarity movement in a mixed methods design employing online ethnography, survey, and “big” social media data. Instead of conceptualizing the group as a social network or social movement organization (SMO), we argue that the group’s culture emerges as patterns of interaction that have implications for what kind of activities in which group members participate. Based on observations from our online ethnography, we suggest that group interaction influences differential individual participation through processes of (1) encoding different habits and (2) attuning the activist to different aspects of situations. We support our theoretical propositions with six statistical tests of the relationship between the group-level variable of contentious group style and the individual-level variable of participation in political protest. The dependent variable, political protest, and a comprehensive set of controls stem from an original survey of the Danish refugee solidarity movement with 2,283 respondents. We link the survey data with “big” social media data used to estimate the focal explanatory variable, contentious group style, generated from content analysis of online interaction in 119 Facebook groups quantified with supervised machine learning. The results show that group style has a consistently positive relationship with the individual’s degree of participation independent of networks, SMO framing, and individual attributes.

Images of protest in social media: Struggle over visibility and visual narratives

2018 ◽  
Vol 20 (11) ◽  
pp. 4293-4310 ◽  
Author(s):  
Christina Neumayer ◽  
Luca Rossi
Keyword(s):  
Social Media ◽  
Conceptual Understanding ◽  
Media Content ◽  
Political Protest ◽  
Frankfurt Am Main ◽  
Social Media Data ◽  
Visual Narratives ◽  
Empirical Inquiry ◽  
Visual Dimension ◽  
Media Data

While political protest is essentially a visual expression of dissent, both social movement research and media studies have thus far been hesitant to focus on visual social media data from protest events. This research explores the visual dimension (photos and videos) of Twitter communication in the Blockupy protests against the opening of the European Central Bank (ECB) headquarters in Frankfurt am Main on 18 March 2015. It does so through a novel combination of quantitative analysis, content analysis of images, and identification of narratives. The article concludes by arguing that the visual in political protest in social media reproduces existing visualities and hierarchies rather than challenges them. This research enhances our conceptual understanding of how activists’ struggles play out in the visual and contributes to developing methods for empirical inquiry into visual social media content.

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.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

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