2010
DOI: 10.1111/j.1083-6101.2010.01507.x
|View full text |Cite
|
Sign up to set email alerts
|

Automated Delineation of Subgroups in Web Video: A Medical Activism Case Study

Abstract: Web 2.0 tools in general, and Web video in particular, provide new ways for activists to express their viewpoints to a broad audience. In this paper we deployed tools that have been used to find subgroups automatically in social networks and applied them to the problem of distinguishing between two sides of a controversial issue based on patterns of online interaction. We explored the problem of distinguishing between anti-and pro-vaccination activists based on a social network of videos and associated comment… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
9
0

Year Published

2010
2010
2021
2021

Publication Types

Select...
8
1

Relationship

2
7

Authors

Journals

citations
Cited by 12 publications
(9 citation statements)
references
References 46 publications
0
9
0
Order By: Relevance
“…Thus, the present study aimed to conduct a visual content analysis to examine portrayals of obese persons in online news videos about obesity. Video content analyses have been frequently used to examine how images can communicate stereotypes and bias, as well as attitudes toward numerous health topics (e.g., smoking, diabetes, opinions about immunizations, and other public health issues; Babamiri & Nassab, 2010;Chin et al, 2010;Conrad, Dixon, & Zhang, 2009;Greenberg et al, 2003;Herbozo et al, 2004;Himes & Thompson, 2007;Keelan, Pavri-Garcia, Tomlinson, & Wilson, 2007;Kim & Willis, 2007;Kyongseok, Paek, & Lynn, 2010;Molyneaux, Gibson, O'Donnell, & Singer, 2008;Steinberg et al, 2010;White et al, 1999). Thus, this method was selected for the present study to examine the prevalence and types of stigmatizing portrayals of obese persons in online news videos about obesity topics from websites representing the major broadcasting networks on television and cable.…”
Section: R M Puhl Et Almentioning
confidence: 99%
“…Thus, the present study aimed to conduct a visual content analysis to examine portrayals of obese persons in online news videos about obesity. Video content analyses have been frequently used to examine how images can communicate stereotypes and bias, as well as attitudes toward numerous health topics (e.g., smoking, diabetes, opinions about immunizations, and other public health issues; Babamiri & Nassab, 2010;Chin et al, 2010;Conrad, Dixon, & Zhang, 2009;Greenberg et al, 2003;Herbozo et al, 2004;Himes & Thompson, 2007;Keelan, Pavri-Garcia, Tomlinson, & Wilson, 2007;Kim & Willis, 2007;Kyongseok, Paek, & Lynn, 2010;Molyneaux, Gibson, O'Donnell, & Singer, 2008;Steinberg et al, 2010;White et al, 1999). Thus, this method was selected for the present study to examine the prevalence and types of stigmatizing portrayals of obese persons in online news videos about obesity topics from websites representing the major broadcasting networks on television and cable.…”
Section: R M Puhl Et Almentioning
confidence: 99%
“…Online video sharing has the potential to communicate health information to a large segment of the population (Chin et al, 2010). Websites offering video streaming to users, such as YouTube (http:// www.youtube.com) allow user tagging, viewer rating, commenting and ranking, and enable the formation of groups around certain viewpoints (Keelan, Pavri-Garcia, Tomlinson, & Wilson, 2007;Lange, 2007).…”
Section: Online Video Sharingmentioning
confidence: 99%
“…It is important to determine if social media sites are really being accessed by the uncertain parents, or if they are merely echo chambers for individuals with like-minded views. Online social media has the potential to act as an 'echo chamber', where personal opinions are predominately reaffirmed by others in your network [38]. An analysis conducted on networks of users showed that information flows more often between users who share the same sentiment and less often between users who do not share the same sentiments than would be predicted by chance alone [39].…”
Section: The Internet and Social Mediamentioning
confidence: 99%