Measuring international relations in social media conversations

Barnett, George A.; Xu, Weiai Wayne; Chu, Jianxun; Jiang, Ke; Huh, Catherine; Park, Ji Young; Park, Han Woo

doi:10.1016/j.giq.2016.12.004

Cited by 37 publications

(15 citation statements)

References 69 publications

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“…Our study contributes to this body of literature by improving our understanding of the mechanisms through which social processes generated through intervention affect participants' psychological and behavioral outcomes. While participant engagement in the Facebook group used in this study exceeded that in previous studies using similar formats targeting diet or physical activity [43,44], similar to previous studies and social media research in other domains, we found great heterogeneity in ways and levels that participants engage in Facebook discussions [45][46][47][48]. Consistent with previous studies, we also found that engagement during the intervention was positively associated with weight loss [49,50].…”

Section: Comparison With Prior Worksupporting

confidence: 87%

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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Section: Comparison With Prior Worksupporting

confidence: 87%

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

View full text Add to dashboard Cite

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“…For instance, the South Korean PD organization Korean Clickers and the US embassy in China have skillfully used Facebook and Tencent, respectively, to create interactive relationships with users (Park & Lim, 2014;Zhong & Lu, 2013). Barnett et al (2017) argued in their article that the way the public discusses foreign countries on social media indicates how they (the public) perceive those countries. Another study by Lee (2017) showed that users react more positively to messages attempting to engage in dialogue than to messages simply disseminating information unilaterally.…”

Section: Pd and Social Mediamentioning

confidence: 99%

Key Influencers in Public Diplomacy 2.0: A Country-Based Social Network Analysis

Ingenhoff

Calamai

Sevin

2021

Social Media + Society

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This article presents a study of Twitter-based communication in order to identify key influencers and to assess the role of their communication in shaping country images. The analysis is based on a 2-month dataset comprised of all tweets including hashtags of the three countries selected for this study: Austria, Switzerland, and the Netherlands. Following a two-step flow model of communication, we initially identified the influential Twitter users in all three countries based on their centrality measures. Subsequently, we carried out a qualitative content analysis of tweets posted by these influential users. Finally, we assessed the similarities and differences across the three country cases. This article offers new insights into public diplomacy 2.0 activities by discussing influence within the context of country images and demonstrating how opinion leaders can play a more dominant role than states or other political actors in creating and disseminating content related to country image. The findings also provide practical insights in the production of a country’s image and its representation on new media platforms.

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“…The authors conceded, however, that numerous methodological issues may have resulted in biases, particularly in their comparisons of the share or number of migrants moving from one country to another (with different penetration rates and different behaviours concerning social networks). The same social media was also used to describe the frequency of transnational networks (Barnett and al. 2017).…”

Section: State Of the Artmentioning

confidence: 99%

How well can we estimate immigration trends using Google data?

Wanner

2020

Qual Quant

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For a country to efficiently monitor international migration, quick access to information on migration flows is helpful. However, traditional data sources fail to provide immediate information on migration flows and do not facilitate the correct anticipation of these flows in the short term. To tackle this issue, this paper evaluates the predictive capacity of big data to estimate the current level or to predict short-term flows. The results show that Google Trends can provide information that reflects the attractiveness of Switzerland for to immigrants from different countries and predict, to some extent, current and future (short-term) migration flows of adults arriving from Spain or Italy. However, the predictions appear not to be satisfactory for other flows (from France and Germany). Additional studies based on alternative approaches are needed to validate or overturn our study results.

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Measuring international relations in social media conversations

Cited by 37 publications

References 69 publications

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

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

Key Influencers in Public Diplomacy 2.0: A Country-Based Social Network Analysis

How well can we estimate immigration trends using Google data?

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