scholarly journals Echo Chambers on Social Media: A Systematic Review of the Literature

2021 ◽  
Vol 9 ◽  
pp. 99-118
Author(s):  
Ludovic Terren ◽  
Rosa Borge-Bravo ◽  
◽  
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Future Research ◽  
Critical Perspective ◽  
Trace Data ◽  
Methodological Choices ◽  
Echo Chambers ◽  
Potential Impact ◽  
Reported Data

There have been growing concerns regarding the potential impact of social media on democracy and public debate. While some theorists have claimed that ICTs and social media would bring about a new independent public sphere and increase exposure to political divergence, others have warned that they would lead to polarization through the formation of echo chambers. The issue of social media echo chambers is both crucial and widely debated. This article attempts to provide a comprehensive account of the scientific literature on this issue, shedding light on the different approaches, their similarities, differences, benefits, and drawbacks, and offering a consolidated and critical perspective that can hopefully support future research in this area. Concretely, it presents the results of a systematic review of 55 studies investigating the existence of echo chambers on social media, providing a first classification of the literature and identifying patterns across the studies’ foci, methods and findings. We found that conceptual and methodological choices influence the results of research on this issue. Most importantly, articles that found clear evidence of echo chambers on social media were all based on digital trace data. In contrast, those that found no evidence were all based on self-reported data. Future studies should take into account the possible biases of the different approaches and the significant potential of combining self-reported data with digital trace data.

A taxonomy of value co-creation on Weibo – a communication perspective

2018 ◽  
Vol 30 (4) ◽  
pp. 2075-2092 ◽  
Author(s):  
Jing Ge ◽  
Ulrike Gretzel
Keyword(s):  
Social Media ◽  
Future Research ◽  
Marketing Organization ◽  
Content Type ◽  
Social Media Platform ◽  
Social Media Platforms ◽  
Media Platform ◽  
Comprehensive Classification ◽  
Practical Implications

Purpose This paper aims to develop a taxonomy of value co-creation types occurring in firm-customer interactions on social media. Design/methodology/approach In total, 570 destination marketing organization (DMO)-initiated posts on Weibo and 3,137 responses were collected to develop a taxonomy by conducting qualitative empirical-to-conceptual analysis. To apply the taxonomy through conceptual-to-empirical analysis, 100 DMO-initiated posts and 823 responses were collected. Findings The communication-focused value co-creation taxonomy shows a variety of co-creators, verbal and non-verbal communicative co-creation actions facilitated by social media, and different co-created value types. Research limitations/implications This study used a single social media platform and selected three DMOs’ Weibo accounts. Future research should focus on other types of firms and different social media platforms. Practical implications This study used a single social media platform and selected three DMOs’ Weibo accounts. Future research should focus on other types of firms and different social media platforms. Originality/value This study enriches the tourism literature and the general marketing literature by examining value co-creation from a communication perspective and provides a comprehensive classification of value co-creation opportunities on social media.

scholarly journals Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review (Preprint)

2017 ◽  
Author(s):  
Sheik Mohammad Roushdat Ally Elaheebocus ◽  
Mark Weal ◽  
Leanne Morrison ◽  
Lucy Yardley
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Behavior Change ◽  
Behavioral Interventions ◽  
Selection Process ◽  
Future Research ◽  
Behavioral Risk ◽  
Lack Of Information ◽  
Behavior Change Interventions ◽  
High Level

BACKGROUND Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. OBJECTIVE The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. METHODS Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. RESULTS A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. CONCLUSIONS Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed at isolating and reporting the effects of social media features on DBCIs, cross-study comparisons, and evaluations.

Effects of CYP2C8 and SLCO1B1 Genetic Polymorphisms on Repaglinide Pharmacokinetics: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 266-274 ◽  
Author(s):  
Shuang Zhou ◽  
Qian Xiang ◽  
Guangyan Mu ◽  
Lingyue Ma ◽  
Shuqing Chen ◽  
...  
Keyword(s):  
Systematic Review ◽  
Blood Glucose ◽  
Genetic Polymorphisms ◽  
Blood Glucose Concentration ◽  
Meta Analysis ◽  
Inclusion Criteria ◽  
Current Systematic Review ◽  
Potential Impact ◽  
Reported Data ◽  
Insufficient Number

Objective: The purpose of this systematic review and meta-analysis was to summarize the potential impact of CYP2C8 and SLCO1B1 genetic polymorphisms on repaglinide pharmacokinetics. Methods: A systematic search was conducted using electronic databases. Eligible studies reported data from pharmacokinetic evaluations of repaglinide in healthy adults according to different categories of CYP2C8 and SLCO1B1 genetic polymorphisms. Results: Six studies including a total of 191 participants met the inclusion criteria. We noted that CYP2C8 *1/*3 carriers exhibited lower AUC(0-∞) (SMD: -0.77; 95%CI: -1.23 to -0.30; P=0.001) and Cmax (SMD: -0.94; 95%CI: - 1.41 to -0.47; P<0.001) than CYP2C8 *1/*1 carriers. There were no significant differences in AUC(0-∞), Cmax, t1/2 and mean change in blood glucose concentration between *1/*4 and *1/*1 carriers. Further, *3/*3 carriers had lower Cmax (SMD: -1.42; 95%CI: -2.66 to -0.17; P=0.026) than *1/*1 carriers. Additionally, *3/*3 carriers had lower Cmax than *1/*3 carriers (SMD: -1.20; 95%CI: -2.40 to -0.00; P=0.050). Finally, we noted that repaglinide pharmacokinetics did not differ by SLCO1B1 genotype. Conclusion: The current systematic review and meta-analysis indicated that the genotype of CYP2C8, but not SLCO1B1, may affect repaglinide pharmacokinetics. However, because of the comparatively insufficient number of published studies included, our conclusions require support from additional studies.

scholarly journals Accounting, accountability, social media and big data: revolution or hype?

2017 ◽  
Vol 30 (4) ◽  
pp. 762-776 ◽  
Author(s):  
Michela Arnaboldi ◽  
Cristiano Busco ◽  
Suresh Cuganesan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Future Research ◽  
Data Governance ◽  
Content Type ◽  
Knowledge Based ◽  
Decision Making Processes ◽  
Key Aspects ◽  
The Impact

Purpose The purpose of this paper is to outline an agenda for researching the relationship between technology-enabled networks – such as social media and big data – and the accounting function. In doing so, it links the contents of an unfolding area research with the papers published in this special issue of Accounting, Auditing and Accountability Journal. Design/methodology/approach The paper surveys the existing literature, which is still in its infancy, and proposes ways in which to frame early and future research. The intention is not to offer a comprehensive review, but to stimulate and conversation. Findings The authors review several existing studies exploring technology-enabled networks and highlight some of the key aspects featuring social media and big data, before offering a classification of existing research efforts, as well as opportunities for future research. Three areas of investigation are identified: new performance indicators based on social media and big data; governance of social media and big data information resources; and, finally, social media and big data’s alteration of information and decision-making processes. Originality/value The authors are currently experiencing a technological revolution that will fundamentally change the way in which organisations, as well as individuals, operate. It is claimed that many knowledge-based jobs are being automated, as well as others transformed with, for example, data scientists ready to replace even the most qualified accountants. But, of course, similar claims have been made before and therefore, as academics, the authors are called upon to explore the impact of these technology-enabled networks further. This paper contributes by starting a debate and speculating on the possible research agendas ahead.

scholarly journals A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

2021 ◽  
Vol 9 (10) ◽  
pp. 1410-1440
Author(s):  
T Heena Fayaz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Networks ◽  
Social Media ◽  
Future Research ◽  
Learning Approaches ◽  
Electoral Campaigns ◽  
Regression Methods ◽  
New Research

Abstract: The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review

How can the spatial equity of health services be defined and measured? A systematic review of spatial equity definitions and methods

2019 ◽  
Vol 24 (4) ◽  
pp. 270-278 ◽  
Author(s):  
Jesse Whitehead ◽  
Amber L. Pearson ◽  
Ross Lawrenson ◽  
Polly Atatoa-Carr
Keyword(s):  
Systematic Review ◽  
Health Services ◽  
Future Research ◽  
Equity Analysis ◽  
Spatial Equity ◽  
Equity Research ◽  
Definition Of ◽  
Distributional Fairness ◽  
The Impact

Background Spatial equity analysis has been carried out in a variety of contexts and on a range of health services. However, there is no clear consensus on spatial equity definitions or measures. This review seeks to summarize spatial equity definitions and methods of analysis. Methods We systematically searched two electronic databases and six journals for papers providing a definition of spatial equity or performing a spatial equity analysis on health services. Studies were classified according to four definition themes: (1) distributional fairness; (2) needs-based distribution; (3) focus on outcomes or causes and (4) none provided. Results Seventy-five studies met our inclusion criteria. Sixty-one papers provided a definition of spatial equity, while a further 14 papers analysed the spatial equity of health services without providing a definition. Most authors used a needs-based definition of spatial equity, while the Gini coefficient was the most commonly used equity measure. However, analysis approaches varied according to the definition provided by each paper. Among the needs-based definitions, spatial autocorrelation was the most common spatial equity measure. Conclusions To our knowledge, this is the first systematic review summarizing spatial equity definitions and analysis methods. A lack of consensus on definitions and measures persists. The classification of measures according to definition themes makes this review a useful tool for planning and interpreting spatial equity investigations. Future research should examine the impact different measures of accessibility and need have on the results of spatial equity research.

Active Support: A Systematic Review and Evidence-Based Practice Evaluation

2011 ◽  
Vol 49 (3) ◽  
pp. 166-171 ◽  
Author(s):  
Jeffery P. Hamelin ◽  
Peter Sturmey
Keyword(s):  
Systematic Review ◽  
Evidence Based Practice ◽  
Effect Sizes ◽  
Future Research ◽  
Evidence Based ◽  
User Engagement ◽  
Data Points ◽  
Promising Treatment ◽  
Independent Variable ◽  
Reported Data

Abstract Researchers have evaluated active support in agencies for persons with developmental disabilities to increase staff assistance and service user engagement. A systematic review identified two studies in which researchers reported three experimental evaluations of active support. Only one experiment showed a clear functional relationship between active support with “ineffective” to “questionable” percentage of nonoverlapping data points effect sizes and acceptable percentage of all nonoverlapping data points effect sizes. Two experiments did not show experimental control; however, there was evidence that the investigators in these studies did not sufficiently manipulate the independent variable. Based on these data, active support only meets Chambless and Hollon's (1998) criterion for a “promising treatment” but not an evidence-based practice. Future research on active support should demonstrate that the experimenter manipulated the independent variable and reported data on individual participants.

scholarly journals Framing Studies Evolution in the Social Media Era. Digital Advancement and Reorientation of the Research Agenda

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Pablo López-Rabadán
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Political Communication ◽  
Research Agenda ◽  
Research Impact ◽  
Future Research ◽  
Experimental Development ◽  
The Public ◽  
The Social ◽  
Digital Development

Framing studies remain a powerful line of research in political communication. However, in recent years, coinciding with the emergence of social media, theoretical and operational advances have been detected, as well as a significant reorientation of its research agenda. The interaction between media and platforms such as Twitter or Facebook has built a clearly hybrid communicative environment and profoundly transformed the organization of public debate. This is the case, especially, with processes such as the setting of the public agenda or the construction of interpretive frames. Based on a systematic review of the international reference literature (2011–2021), this article analyses the influence of social media on the evolution of framing studies. Moreover, specifically, the beginning of a new stage of digital development is contextualized, and a triple research impact is explored. The main contributions of the text are that it (1) identifies advances in the theoretical and empirical organization of these studies; (2) explores its reorientation of content towards a greater balance between the analysis of media and political frames; and (3) reviews the recent experimental development of effects studies. Finally, the main challenges for future research in this field are detailed.

scholarly journals How Do Partisans Consume News on Social Media? A Comparison of Self-Reports With Digital Trace Measures Among Twitter Users

2020 ◽  
Vol 6 (4) ◽  
pp. 205630512098103
Author(s):  
Jieun Shin
Keyword(s):  
Social Media ◽  
Selective Exposure ◽  
The Self ◽  
Trace Data ◽  
News Stories ◽  
Self Reports ◽  
Common Framework ◽  
Twitter Users ◽  
Reported Data ◽  
Survey Responses

There are growing concerns that social media exacerbates the selective exposure of audience members to content that supports their political views. However, despite the hype, the existing literature does not fully address the extent to which social media users selectively consume like-minded news stories, in part due to different methodologies. In an attempt to move toward a common framework, this study examined the partisan selective exposure of a representative sample of Twitter users by combining survey data with digital trace data. Specifically, the study linked survey responses ( n = 558) from Twitter users with their media following and exposure to news via their friends. The study found that selectivity bias was present in all types of data, including self-reported media consumption (survey), media following (Twitter), and indirect exposure to media (Twitter). However, the study found some differences between self-reports and digital measures such that the overlaps in media diets between partisan groups were much larger based on the digital trace data than the self-reported data. In addition, the study observed an asymmetric pattern of selective exposure between conservatives and liberals in the digital trace data, but not in the self-reported data. The implications of these findings are discussed with reference to the contemporary news environment, hostile media effects, and normative assumptions of selective exposure.

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