Twitter and Facebook: Populists’ double-barreled gun?

2020 ◽  
Vol 22 (4) ◽  
pp. 611-633
Author(s):  
Kristof Jacobs ◽  
Linn Sandberg ◽  
Niels Spierings
Keyword(s):  
Social Media ◽  
The Netherlands ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Members Of Parliament ◽  
Binomial Regression ◽  
Content Analyses

Social media are said to be a core driver of populists’ current success. Yet, our knowledge of how populist politicians use social media is limited. We argue that they can use Twitter and Facebook, politically the most important platforms, as a “double-barreled gun,” each serving a different target. Based on the architecture of the platforms and the populist ideology, we expect that Twitter is used to name and shame journalists publicly, Facebook to activate anger among citizens. Both types of use are examined by studying the Members of Parliament (MPs) of Austria, The Netherlands, and Sweden. We collected 9852 tweets for the 475 MPs on Twitter and 10,355 Facebook posts from the 287 MPs with a Facebook Page. Using negative binomial regression and content analyses, we find that populists seem eager to activate anger. They are not more likely to @-mention media accounts, but “shame” them roughly three times more often.

Examining Engagement With Sport Sponsor Activations on Twitter

2021 ◽  
Vol 14 (1) ◽  
pp. 79-108
Author(s):  
Terry Eddy ◽  
B. Colin Cork ◽  
Katie Lebel ◽  
Erin Howie Hickey
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Sport Organizations ◽  
Media Messages ◽  
Binomial Regression ◽  
Different Types ◽  
Use Of Social Media ◽  
Activation Type

Research on sport sponsors’ use of social media has begun to emerge, but, to date, limited research has examined how sponsors are using social media as an activation platform to engage with followers. Thus, the purpose of this research was to examine differences in follower engagement with regard to sponsored Twitter posts from North American professional sport organizations, based upon the focus, scope, and activation type of the sponsored messages. This manuscript consists of two related studies—Study 1 employed a deductive content analysis, followed by negative binomial regression modeling, to examine differences in engagement between message structures defined by focus and scope. Study 2 featured an inductive content analysis to investigate differences in engagement between different types of activations. The findings suggest that, in general, more passive (or less overt) forms of sponsor integration in social media messages drive more engagement among followers.

scholarly journals Using text analytics to measure an effect of topics and sentiments on social-media engagement: Focusing on Facebook fan page of Toyota

2021 ◽  
Vol 13 ◽  
pp. 184797902110162
Author(s):  
Heuiju Chun ◽  
Byung-Hak Leem ◽  
Hyesun Suh
Keyword(s):  
Social Media ◽  
Regression Model ◽  
Topic Modeling ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Influential Factor ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
Social Media Engagement ◽  
The Media

In this study we investigate whether Facebook fan-page posting types and topics have a significant effect on engagement. More specifically, the media type and content theme of posting on Facebook are examined to see whether or not there was a difference between content topics. In order to achieve this goal, we set hypotheses as follows: (1) the media types of posting have a significant effect on engagement; (2) the topics and sentiment polarity of posting have a significant effect on engagement. We tested these hypotheses using research procedures as follows: (1) collection and preprocessing of social-media data, including posting types, comments, and reactions on Facebook fan pages, (2) topic modeling of fan-page postings using R and SAS, (3) testing hypotheses using a negative binomial regression model, and (4) implications and insights for social-media marketing. Topic modeling applying to textual data and sentiment analysis were conducted. After that, in order to find the factors to affect the number of Facebook fan-page engagements, the negative binomial regression model including post type, topic, sentiment, reactions of “love,” “haha,” and their interaction as exploratory variables was considered. Finally, the results show that post type is the most influential factor to affect social-media engagement, and content topics, sentiments of posts and comments also have significant effects on it.

Hiding behind the party brand or currying favor with constituents: Why do representatives engage in different types of constituency-oriented behavior?

2017 ◽  
Vol 25 (3) ◽  
pp. 369-381 ◽  
Author(s):  
Sabri Ciftci ◽  
Tevfik Murat Yildirim
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Ordinary Least Squares ◽  
Party Leader ◽  
Data Sets ◽  
Members Of Parliament ◽  
Binomial Regression ◽  
Different Types ◽  
Role Orientations

Why do representatives prioritize certain types of constituency service in parliamentary systems? This study argues that the choice for constituency-oriented activities is conditioned by both partisan factors and legislative role orientations. Two novel data sets combining behavioral and attitudinal measures of constituency-oriented behavior are used for empirical tests: an elite survey including detailed interviews with 204 members of the Turkish parliament and 4000 parliamentary questions tabled by these members. The results from a series of ordered logit, ordinary least squares (OLS), and negative binomial regression estimations confirm that members of parliament choose different types of constituency-oriented activities based on their visibility to the party leadership and their constituency. This choice is primarily driven by partisanship and members of parliament’s perceptions about the influence of party leader in renomination. The analysis provides important insights about the role of partisan factors as drivers of parliamentary behavior.

scholarly journals 1023 The Role of Social Media in Disseminating Plastic Surgery Research: The Relationship Between Citations, Altmetrics and Article Characteristics

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
M Grant ◽  
K Scott-Bridge ◽  
R Wade
Keyword(s):  
Social Media ◽  
Plastic Surgery ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Aesthetic Surgery ◽  
Binomial Regression ◽  
Surgery Research ◽  
Medical Disciplines ◽  
The Relationship

Abstract Background Social media (SoMe) enable the dissemination of content immediately and directly to interested end-users. Alternative metrics (altmetrics) are non-traditional bibliometrics which describe the exposure and impact of an article on freely available platforms such as Twitter and Facebook. Altmetrics within days of publication are associated with ultimate citation counts in various medical disciplines, except plastic surgery which represents the rationale for this study. Method Altmetric explorer was used to extract altmetrics and citation rates for articles published during 2018 in Plastic and Reconstructive Surgery (PRS), the Journal of Plastic, Reconstructive and Aesthetic Surgery, the Annals of Plastics Surgery and Plastic Surgery. Multivariable negative binomial regression was used to estimate the relationship between citations and predictors (presented as the incidence rate ratio, IRR with 95% confidence interval, CI). Results Overall, 1215 articles were captured. On average, articles published in PRS were cited nearly five times as often as articles published elsewhere (adjusted IRR 4.77 [95% CI 2.36, 9.62]). Overall, SoMe mentions were positively associated with citation rates (adjusted IRR 1.01 [95% CI 1.01, 1.1]); marginal analysis showed that 45 mentions translated to one extra citation. Conclusions Dissemination of plastic surgery research through SoMe channels are associated with significant improvements in short term citations rates.

scholarly journals SOCIAL MEDIA MARKETING ON INSTAGRAM: WHEN IS THE MOST EFFECTIVE POSTING TIMING?

2020 ◽  
pp. 312-321
Author(s):  
Risqo M. Wahid ◽  
Muhammad Wadud
Keyword(s):  
Social Media ◽  
Marketing Strategy ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Social Media Marketing ◽  
Binomial Regression

This study investigates the most effective posting timing on Instagram to enhance content engagements (i.e., likes and comments). Data of 2,958 Indonesian food brands posts were analysed using negative binomial regression to predict the number of likes and comments. This study reveals that the most effective posting days are on Monday, Tuesday, Wednesday, Thursday, and Friday as contents posted on Instagram on these days result in a higher number of likes and comments. In addition, this study also encourages food brands to post their contents during breakfast and lunch times as these times of the day prompt more likes and comments. The findings of this study will help brands and social media managers to include scheduling in their social media marketing strategy on Instagram. KEYWORDS: Social Media Marketing, Instagram, Posting Days, Posting Times, Content Engagement

scholarly journals Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths

2021 ◽  
pp. jech-2020-215039 ◽  
Author(s):  
Anders Malthe Bach-Mortensen ◽  
Michelle Degli Esposti
Keyword(s):  
Negative Binomial ◽  
Care Home ◽  
Negative Binomial Regression ◽  
Population Characteristics ◽  
Care Homes ◽  
Future Research ◽  
Area Deprivation ◽  
Binomial Regression ◽  
Index Of Multiple Deprivation

IntroductionThe COVID-19 pandemic has disproportionately impacted care homes and vulnerable populations, exacerbating existing health inequalities. However, the role of area deprivation in shaping the impacts of COVID-19 in care homes is poorly understood. We examine whether area deprivation is linked to higher rates of COVID-19 outbreaks and deaths among care home residents across upper tier local authorities in England (n=149).MethodsWe constructed a novel dataset from publicly available data. Using negative binomial regression models, we analysed the associations between area deprivation (Income Deprivation Affecting Older People Index (IDAOPI) and Index of Multiple Deprivation (IMD) extent) as the exposure and COVID-19 outbreaks, COVID-19-related deaths and all-cause deaths among care home residents as three separate outcomes—adjusting for population characteristics (size, age composition, ethnicity).ResultsCOVID-19 outbreaks in care homes did not vary by area deprivation. However, COVID-19-related deaths were more common in the most deprived quartiles of IDAOPI (incidence rate ratio (IRR): 1.23, 95% CI 1.04 to 1.47) and IMD extent (IRR: 1.16, 95% CI 1.00 to 1.34), compared with the least deprived quartiles.DiscussionThese findings suggest that area deprivation is a key risk factor in COVID-19 deaths among care home residents. Future research should look to replicate these results when more complete data become available.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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