Measuring web ecology by Facebook, Twitter, blogs and online news: 2012 general election in South Korea

Nam, Yoonjae; Lee, YeonOk; Park, Han Woo

doi:10.1007/s11135-014-0016-9

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…Rogers (2000) pointed this out in one of his seminal works, suggesting that the diffusion of social events is a complicated process in which structural characteristics of communities and information channels influence the way information is disseminated and adopted by individuals. Along this line, Nam et al (2014) noted that the concept of the web ecology refers to interrelationships between human behaviors, information systems, and technologies. Oriented by the biological ecology, the concept emphasizes that different individuals employ different information tools in a complementary manner.…”

Section: Network Theory and Information Diffusionmentioning

confidence: 99%

“…Although information diffusion theory and the web ecology model suggest the necessity of considering differing roles of social media in the information diffusion process and organizing collective action, previous studies of political information diffusion through social media have focused mainly on specific social media platforms (Rogers, 2000;Nam et al, 2014). This significant bias can hinder the understanding of distinct functions of social media platforms for collective action.…”

Section: Research Objectivesmentioning

confidence: 99%

“…Researchers have focused on which and how political information is spread through social networks (e.g., Biddix and Park, 2008;Caren and Gaby, 2011;Technological Forecasting & Social Change xxx (2015) xxx-xxx Danowski and Park, 2014), the capability of communication networks to organize collective action (Benkler, 2006;Best and Kellner, 2001;Castells, 2009;Couldry and Curran, 2003), and the characteristics of key actors in the information diffusion process (e.g., Choi and Park, 2014;González-Bailón et al, 2011;Hsu et al, 2013). However, few studies have examined the distinct role of social media platforms as a channel of information diffusion, although characteristics of each medium may shape the way information is disseminated and shared by users (Nam et al, 2014;Rogers, 2000).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing Twitter and YouTube networks in information diffusion: The case of the “Occupy Wall Street” movement

Park

Lim

Park

2015

Technological Forecasting and Social Change

Self Cite

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Section: Network Theory and Information Diffusionmentioning

confidence: 99%

Section: Research Objectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparing Twitter and YouTube networks in information diffusion: The case of the “Occupy Wall Street” movement

Park

Lim

Park

2015

Technological Forecasting and Social Change

Self Cite

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“…In addition, social media users are accustomed to sharing information about their health experiences and opinions [11,12]. This sharing may be enhanced by the communication features of social media websites, which can facilitate sharing and communication among users about weight loss behaviors, goals, and experiences [13,14]. These features make social media platforms particularly attractive for delivering behavioral weight loss interventions.…”

Section: Introductionmentioning

confidence: 99%

Social Network Analysis of the Effects of a Social Media–Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial

Xu¹,

Cavallo²

2021

J Med Internet Res

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Background Obesity is a known risk factor for cardiovascular disease risk factors, including hypertension and type II diabetes. Although numerous weight loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from web-based platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. Objective The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social media–based weight loss intervention. Methods We performed secondary analysis by using data from a pilot study that delivered a dietary and physical activity intervention to a group of participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period and linked participants’ network characteristics (eg, in-degree, out-degree, network constraint) to participants’ changes in theoretical mediators (ie, dietary knowledge, perceived social support, self-efficacy) and weight loss by using regression analysis. We also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. Results In this analysis, 47 participants from 2 waves completed the study and were included. We found that increases in the number of posts, comments, and reactions significantly predicted weight loss (β=–.94, P=.04); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009), and the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight loss (β=–.89, P=.02). Conclusions Our analyses using data from this pilot study linked participants’ network characteristics with changes in several important study outcomes of interest such as self-efficacy, social support, and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which web-based behavioral interventions affect participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and to further explore the relationship between network dynamics and study outcomes in similar and larger trials.

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“…These sources are two social media (Facebook and Twitter) and two relevant online sources, Wikipedia (Internet free encyclopaedia) and YouTube (video sharing website). The contrast of information between different social media and online sources data is considered an important step in order to get a more consistent map of the information obtained (Nam, Lee & Park, 2014).…”

Section: Top Down Approachmentioning

confidence: 99%

Identifying the Relevance of Research Goals through Collecting Citizens’ Voices on Social Media

Olivé

García

Ionescu

et al. 2017

RIMCIS

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Recent debates on the meaning and use of science are focused on addressing citizens’ needs or concerns of society in different fields. Researchers have developed different methodologies for capturing the relevance of topics to be addressed by research in order to map them. This article proposes a new methodology for identifying the relevance of research goals through collecting citizen’s voices on Twitter and Facebook combing two approaches: top down, starting with already defined research goals priorities, and bottom up, departing from the social media. The article presents the results of the application of this methodology through the research goals of Sustainable Development Goals to identify their relevance and if there are some topics not covered by them. Thus, researchers could integrate this methodology in their daily work and be more in line with the needs expressed by citizens in social media.

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Measuring web ecology by Facebook, Twitter, blogs and online news: 2012 general election in South Korea

Cited by 12 publications

References 27 publications

Comparing Twitter and YouTube networks in information diffusion: The case of the “Occupy Wall Street” movement

Comparing Twitter and YouTube networks in information diffusion: The case of the “Occupy Wall Street” movement

Social Network Analysis of the Effects of a Social Media–Based Weight Loss Intervention Targeting Adults of Low Socioeconomic Status: Single-Arm Intervention Trial

Identifying the Relevance of Research Goals through Collecting Citizens’ Voices on Social Media

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