scholarly journals Visualizing the triple bottom line: A large‐scale automated visual content analysis of European corporations' website and social media images

2020 ◽  
Vol 27 (6) ◽  
pp. 2631-2641 ◽  
Author(s):  
Irina Lock ◽  
Theo Araujo
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Large Scale ◽  
Triple Bottom Line ◽  
Visual Content ◽  
Media Images ◽  
Bottom Line

A shift from science to politics : a content analysis of COVID-19 Videos on YouTube (Preprint)

2021 ◽  
Author(s):  
Chyun-Fung Shi ◽  
Matthew C So ◽  
Sophie Stelmach ◽  
Arielle Earn ◽  
David J D Earn ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Content Analysis ◽  
Large Scale ◽  
Control Measures ◽  
Mitigation Measures ◽  
Health Campaigns ◽  
Public Attention ◽  
Conflicting Information ◽  
Social Media Platforms

BACKGROUND The COVID-19 pandemic is the first pandemic where social media platforms relayed information on a large scale, enabling an “infodemic” of conflicting information which undermined the global response to the pandemic. Understanding how the information circulated and evolved on social media platforms is essential for planning future public health campaigns. OBJECTIVE This study investigated what types of themes about COVID-19 were most viewed on YouTube during the first 8 months of the pandemic, and how COVID-19 themes progressed over this period. METHODS We analyzed top-viewed YouTube COVID-19 related videos in English from from December 1, 2019 to August 16, 2020 with an open inductive content analysis. We coded 536 videos associated with 1.1 billion views across the study period. East Asian countries were the first to report the virus, while most of the top-viewed videos in English were from the US. Videos from straight news outlets dominated the top-viewed videos throughout the outbreak, and public health authorities contributed the fewest. Although straight news was the dominant COVID-19 video source with various types of themes, its viewership per video was similar to that for entertainment news and YouTubers after March. RESULTS We found, first, that collective public attention to the COVID-19 pandemic on YouTube peaked around March 2020, before the outbreak peaked, and flattened afterwards despite a spike in worldwide cases. Second, more videos focused on prevention early on, but videos with political themes increased through time. Third, regarding prevention and control measures, masking received much less attention than lockdown and social distancing in the study period. CONCLUSIONS Our study suggests that a transition of focus from science to politics on social media intensified the COVID-19 infodemic and may have weakened mitigation measures during the first waves of the COVID-19 pandemic. It is recommended that authorities should consider co-operating with reputable social media influencers to promote health campaigns and improve health literacy. In addition, given high levels of globalization of social platforms and polarization of users, tailoring communication towards different digital communities is likely to be essential.

An Analysis of Hospitality and Tourism Research

2015 ◽  
pp. 359-381 ◽  
Author(s):  
Jennifer Calhoun ◽  
Alecia Douglas
Keyword(s):  
Content Analysis ◽  
Dynamic Environment ◽  
Triple Bottom Line ◽  
Qualitative Approach ◽  
Sustainable Competitive Advantage ◽  
Bottom Line ◽  
Tourism Research ◽  
Body Of Knowledge ◽  
Sustainability Practices ◽  
Hospitality And Tourism

Learning organizations (LOs) have been identified as an innovative practice essential for global businesses to not only effectively compete in today's dynamic environment but also to achieve and maintain a sustainable competitive advantage and increase overall firm performance. The objective of this chapter is to examine the current body of knowledge on LOs and their impact on sustainability practices in an effort to identify what is being done by organizations, where knowledge is applied, and, how systems are created to influence sustainability practices. In the context of hospitality and tourism businesses, the literature examining LOs is limited though a wealth of studies have been conducted in the mainstream. Using a qualitative approach, a content analysis was conducted to investigate its impact on sustainability practices in hospitality and tourism organizations. The results indicate that destinations in particular have adopted this approach to compete globally and to address triple-bottom line sustainability.

scholarly journals A survey on compact features for visual content analysis

2016 ◽  
Vol 5 ◽  
Author(s):  
Luca Baroffio ◽  
Alessandro E. C. Redondi ◽  
Marco Tagliasacchi ◽  
Stefano Tubaro
Keyword(s):  
Content Analysis ◽  
Large Scale ◽  
Ad Hoc ◽  
State Of The Art ◽  
Large Body ◽  
Visual Features ◽  
Low Latency ◽  
Visual Content ◽  
Feature Encoding ◽  
Memory Footprint

Visual features constitute compact yet effective representations of visual content, and are being exploited in a large number of heterogeneous applications, including augmented reality, image registration, content-based retrieval, and classification. Several visual content analysis applications are distributed over a network and require the transmission of visual data, either in the pixel or in the feature domain, to a central unit that performs the task at hand. Furthermore, large-scale applications need to store a database composed of up to billions of features and perform matching with low latency. In this context, several different implementations of feature extraction algorithms have been proposed over the last few years, with the aim of reducing computational complexity and memory footprint, while maintaining an adequate level of accuracy. Besides extraction, a large body of research addressed the problem of ad-hoc feature encoding methods, and a number of networking and transmission protocols enabling distributed visual content analysis have been proposed. In this survey, we present an overview of state-of-the-art methods for the extraction, encoding, and transmission of compact features for visual content analysis, thoroughly addressing each step of the pipeline and highlighting the peculiarities of the proposed methods.

scholarly journals Guest Editorial: Large-Scale Multimedia Content Analysis on Social Media

2016 ◽  
Vol 75 (3) ◽  
pp. 1365-1369
Author(s):  
Haojie Li ◽  
Zheng-Jun Zha ◽  
Benoit Huet ◽  
Qi Tian
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Large Scale ◽  
Guest Editorial ◽  
Multimedia Content ◽  
Multimedia Content Analysis

Oh Snap: Chat Style in the 2016 US Presidential Primaries

2017 ◽  
Vol 13 (1) ◽  
pp. 17-33 ◽  
Author(s):  
Eisa Al Nashmi ◽  
David Lynn Painter
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
The Other ◽  
Visual Content ◽  
Quantitative Content Analysis ◽  
Self Presentation ◽  
Presidential Primary ◽  
Quantitative Content ◽  
Exploratory Investigation ◽  
Production Techniques

Based on Goffman’s theories of self-presentation and framing, this exploratory investigation adapted Videostyle and Webstyle protocols to analyse the 2016 US presidential primary candidates’ Snapchat posts. This quantitative content analysis ( N = 871) coded for the visual content, production techniques, nonverbal content and frames used by the five candidates who used Snapchat as a strategic tool to engage voters throughout the course of the 2016 US primary campaign. The results indicate Clinton (D) deviated from the other candidates in the visual and nonverbal content as well as the frames used in her snaps. The implications of these findings on gendered self-presentation theory as well as inferences about the campaigns’ strategic social media motivations and effectiveness are also explored.

scholarly journals Natural Language Model for Automatic Identification of Intimate Partner Violence Reports from Twitter

2021 ◽  
Author(s):  
Mohammed Ali Al-Garadi ◽  
Sangmi Kim ◽  
Yuting Guo ◽  
Elise Warren ◽  
Yuan-Chi Yang ◽  
...  
Keyword(s):  
Social Media ◽  
Intimate Partner Violence ◽  
Content Analysis ◽  
Partner Violence ◽  
Large Scale ◽  
Class Imbalance ◽  
Intimate Partner ◽  
Public Health Issue ◽  
Automatic Identification ◽  
Model Errors

Background Intimate partner violence (IPV) is a preventable public health issue that affects millions of people worldwide. Approximately one in four women are estimated to be or have been victims of severe violence at some point in their lives, irrespective of their age, ethnicity, and economic status. Victims often report IPV experiences on social media, and automatic detection of such reports via machine learning may enable the proactive and targeted distribution of support and/or interventions for those in need. Methods We collected posts from Twitter using a list of keywords related to IPV. We manually reviewed subsets of retrieved posts, and prepared annotation guidelines to categorize tweets into IPV-report or non-IPV-report. We manually annotated a random subset of the collected tweets according to the guidelines, and used them to train and evaluate multiple supervised classification models. For the best classification strategy, we examined the model errors, bias, and trustworthiness through manual and automated content analysis. Results We annotated a total of 6,348 tweets, with inter-annotator agreement (IAA) of 0.86 (Cohen's kappa) among 1,834 double-annotated tweets. The dataset had substantial class imbalance, with only 668 (~11%) tweets representing IPV-reports. The RoBERTa model achieved the best classification performance (accuracy: 95%; IPV-report F1-score 0.76; non-IPV-report F1-score 0.97). Content analysis of the tweets revealed that the RoBERTa model sometimes misclassified as it focused on IPV-irrelevant words or symbols during decision making. Classification outcome and word importance analyses showed that our developed model is not biased toward gender or ethnicity while making classification decisions. Conclusion Our study developed an effective NLP model to identify IPV-reporting tweets automatically and in real time. The developed model can be an essential component for providing proactive social media based intervention and support for victims. It may also be used for population-level surveillance and conducting large-scale cohort studies.

Evaluating universities' strategic online communication: how do Shanghai Ranking's top 50 universities grow stakeholder engagement with Facebook posts?

2020 ◽  
Vol 24 (3) ◽  
pp. 265-283 ◽  
Author(s):  
Birte Fähnrich ◽  
Jens Vogelgesang ◽  
Michael Scharkow
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Large Scale ◽  
Online Communication ◽  
Content Type ◽  
Global Brands ◽  
Automated Content Analysis ◽  
Shanghai Ranking ◽  
Fan Engagement ◽  
Media Channel

PurposeThis study is dedicated to universities' strategic social media communication and focuses on the fan engagement triggered by Facebook postings. The study contributes to a growing body of knowledge that addresses the strategic communication of universities that have thus far hardly dealt with questions of resonance and evaluation of their social media messages.Design/methodology/approachUsing the Facebook Graph API, the authors collected posts from the official Facebook fan pages of the universities listed on Shanghai Ranking's Top 50 of 2015. Specifically, the authors retrieved all posts in a three-year range from October 2012 to September 2015. After downloading the Facebook posts, the authors used tools for automated content analysis to investigate the features of the post messages.FindingsOverall, the median number of likes per 10,000 fans was 4.6, while the number of comments (MD = 0.12) and shares (MD = 0.40) were considerably lower. The average Facebook Like Ratio of universities per 10,000 fans was 17.93%, the average Comment Ratio (CR) was 0.56% and the average Share Ratio (SR) was 2.82%. If we compare the average Like Ratios (17.93%) and Share Ratios (2.82%) of the universities with the respective Like Ratios (5.90%) and Share Ratios (0.45%) of global brands per 10,000 fans, we may find that universities are three times (likes) and six times (shares) as successful as are global brands in triggering engagement among their fan bases.Research limitations/implicationsThe content analysis was solely based on the publicly observable Facebook communication of the Top 50 Shanghai Ranking universities. Furthermore, the content analysis was limited to universities listed on the Shanghai Ranking's Top 50. Also, the Facebook posts have been sampled between 2012 and September 2015. Moreover, the authors solely focused on one social media channel (i.e., Facebook), which might restrict the generalizability of the study findings. The limitations notwithstanding, university communicators are invited to take advantage of the study's insights to become more successful in generating fan engagement.Practical implicationsFirst, posts published on the weekend generate significantly more engagement than those published on workdays. Second, the findings suggest that posts published in the evening generate more engagement than those published during other times of day. Third, research-related posts trigger a certain number of shares, but at the same time these posts tend to lower engagement with regard to liking and commenting.Originality/valueTo the authors’ best knowledge, the automated content analysis of 72,044 Facebook posts of universities listed in the Top 50 of the Shanghai Ranking is the first large scale longitudinal investigation of a social media channel of higher education institutions.

scholarly journals Social Media Images as an Emerging Tool to Monitor Adherence to COVID-19 Public Health Guidelines: A Content Analysis (Preprint)

10.2196/24787 ◽  
2020 ◽  
Author(s):  
Sean Young ◽  
Qingpeng Zheng ◽  
Daniel Dajun Zeng ◽  
Yongcheng Zhan ◽  
William Cumberland
Keyword(s):  
Public Health ◽  
Social Media ◽  
Content Analysis ◽  
Media Images

scholarly journals B Corps’ Social Media Communications during the COVID-19 Pandemic: Through the Lens of the Triple Bottom Line

2021 ◽  
Vol 13 (17) ◽  
pp. 9634
Author(s):  
Manveer Mann ◽  
Sang-Eun Byun ◽  
Whitney Ginder
Keyword(s):  
Social Media ◽  
Qualitative Method ◽  
Triple Bottom Line ◽  
Environmental Issues ◽  
Bottom Line ◽  
Individual Level ◽  
Media Communications ◽  
Social Environmental ◽  
The U.S

The COVID-19 pandemic and rising demand for transparency has heightened the importance of sustainability communications on social media to generate deeper stakeholder engagement. Although B Corporations (B Corps), businesses committed to the triple bottom line (TBL), could serve as a catalyst for sustainable development, little is known about how they communicate on social media during a crisis. Therefore, we examined social media communications of B Corps to (1) identify salient topics and themes, (2) analyze how these themes align with the TBL, and (3) evaluate social media performance against industry benchmarks. We focused on the apparel, footwear, and accessories (AFA) sectors in the U.S. and chose Twitter, a platform known for crisis communication. Using a qualitative method, we found four topics and 21 underlying themes. Topics related to social/environmental issues and COVID-19 were most dominant, followed by product/brand promotions. Further classification of specific themes and cases from a TBL perspective demonstrated that, overall, B Corps in the AFA sectors leveraged various approaches to promote balance between each TBL dimension. Lastly, although collectively B Corps exceeded some of the Twitter industry benchmarks, at an individual level, most brands had room for improvement to build a stronger community and promote synergy among the three pillars of the TBL.

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