Content Analysis of Select YouTube Postings: Comparisons of Reactions to the Sandy Hook and Aurora Shootings and Hurricane Sandy

Miller, Eric D.

doi:10.1089/cyber.2015.0045

Cited by 23 publications

(23 citation statements)

References 32 publications

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“…In addition, YouTube video comments are generally genuine personal reactions made by the commenters. Compared with the socially desirable answers obtained from surveys/focus groups, this social media data produces theoretically and empirically sound results [ 48 ]. Moreover, in comparison with the traditional sample size and response rate, this study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gathered from YouTube.…”

Section: Discussionmentioning

confidence: 99%

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

Teng¹,

Khong²,

Sharif³

et al. 2020

JMIR Public Health Surveill

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Background Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities. Objective The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters’ perceptions and sentiments of healthy eating through text mining techniques. Methods This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure. Results With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily. Conclusions This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

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Section: Discussionmentioning

confidence: 99%

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

Teng¹,

Khong²,

Sharif³

et al. 2020

JMIR Public Health Surveill

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“…This study, which largely employed the methods as detailed in Miller (), received an IRB approved Level I review given its use of publicly available information and the removal of all personal information prior to data analyses. No individuals whose YouTube comments were considered or used for this study were ever directly contacted whatsoever.…”

Section: Methodsmentioning

confidence: 99%

“…One possible means of skirting these ethical limitations while still offering a meaningful comparison of differing events featuring evil is to consider YouTube comments associated with events showing varying levels of evil. A previous study by Miller ()—which largely offers the basic methodological foundation of this investigation—contrasted select YouTube comments regarding expressions of grief and related emotional reactions with respect to three tragedies that occurred in 2012: the Sandy Hook Elementary School shootings, the Aurora theater shootings, and the landfall of the destructive Hurricane Sandy. The videos associated with these events all featured breaking news reports and an additional memorial video was shown regarding the Sandy Hook shootings.…”

Section: Introductionmentioning

confidence: 99%

Codifying gradients of evil in select YouTube comment postings

Miller

2019

Human Behav and Emerg Tech

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This investigation featured a content analysis of 2,269 publicly available YouTube comment postings in order to provide insight into how users evaluate and interpret videos containing explicit and graphic imagery with palpable relevance to themes of evil. Famed social psychologist Leonard Berkowitz (1999), suggested that evil should necessarily have differing gradients. Using this framework, select comments from four widely viewed and graphic videos from the following events were utilized as they were deemed to depict evil in the following descending order: The Holocaust, 9/11, the 2015 WDBJ shootings, and the 2014 Ray Rice assault. It was hypothesized and largely found that videos associated with greater evil showed a greater recognition from YouTube users as such as well as greater compassion toward the depicted victims. However, as expected, there were also tendencies for the Holocaust video to be particularly associated with select negative comments (e.g., racism or suggesting it was a fake event). This research furthers our understanding of how individuals make sense of traumatic events with varying degrees of evil in an online context.

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“…The majority of CMSS analyses have turned to automated or manual text content analysis methods applied to messages or posts in order to understand how social support is exchanged in online health communities such as social media networks and online forums (e.g., [9,11,38]). For instance, Wang et al employed machine learning-based methods to automatically identify emotional and informational support in messages posted to an online support group for breast cancer patients and to correlate their prevalence with online group member retention.…”

Section: Computer-mediated Social Support (Cmss)mentioning

confidence: 99%

Where and How to Look for Help Matters: Analysis of Support Exchange in Online Health Communities for People Living with HIV

et al. 2018

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Research is scarce on how direct and indirect support seeking strategies affect support exchange in online health communities. Moreover, prior research has relied mostly on content analysis of forum posts at the post level. In order to generate a more fine-grained analysis of support exchange, we conducted content analysis at the utterance level, taking directness of support seeking, quality of provision, forum type, and seeker gender into account. Our analysis of four popular online support forums for people living with human immunodeficiency virus found that type of support sought and provided, support seeking strategy, and quality of emotional support provision differed in care provider/formal forums versus social/informal forums. Interestingly, indirect support seeking tended to elicit more supportive emotional responses than direct support seeking strategies in all forums; we account for this in terms of type of support sought. Practical implications for online support communities are discussed.

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Content Analysis of Select YouTube Postings: Comparisons of Reactions to the Sandy Hook and Aurora Shootings and Hurricane Sandy

Cited by 23 publications

References 32 publications

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

Codifying gradients of evil in select YouTube comment postings

Where and How to Look for Help Matters: Analysis of Support Exchange in Online Health Communities for People Living with HIV

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