scholarly journals Measuring Organizational Legitimacy in Social Media: Assessing Citizens’ Judgments With Sentiment Analysis

2016 ◽  
Vol 57 (1) ◽  
pp. 60-97 ◽  
Author(s):  
Michael Etter ◽  
Elanor Colleoni ◽  
Laura Illia ◽  
Katia Meggiorin ◽  
Antonino D’Eugenio
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
News Media ◽  
Quantitative Methods ◽  
Organizational Legitimacy ◽  
Social Media Data ◽  
Stakeholder Groups ◽  
Use Of Social Media ◽  
The Individual ◽  
Media Data

Conventional quantitative methods for the measurement of organizational legitimacy consider mainly three sources that make judgments about organizations visible: news media, accreditation bodies, and surveys. Over the last decade, however, social media have enabled ordinary citizens to bypass the gatekeeping function of these institutional evaluators and autonomously make individual judgments public. This inclusion of voices beyond functional and formally organized stakeholder groups potentially pluralizes the ongoing discussions about organizations. The individual judgments in blogs, tweets, and Facebook posts give indication about the broader fit between an organization’s perceived behavior and heterogeneous social norms and therefore constitute an indicator of organizational legitimacy that can be accessed and measured. We propose the use of social media data and sentiment analysis to study the affect-based responses to organizational actions by citizens. We critically discuss and compare the method with existing quantitative methods for legitimacy measurement and apply them to a recent case in the banking industry. We discuss the value of the method for studying the process of legitimacy construction as the expression and negotiation of normative judgments about organizations by various evaluators.

scholarly journals Inferring Activities from Social Media Data

2017 ◽  
Vol 2666 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Emmanouil Chaniotakis ◽  
Constantinos Antoniou ◽  
Georgia Aifadopoulou ◽  
Loukas Dimitriou
Keyword(s):  
Social Media ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Social Media Data ◽  
Use Of Social Media ◽  
Definition Of ◽  
The Individual ◽  
Transportation Research ◽  
Direct Use ◽  
Media Data

Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals.

scholarly journals A Multi-platform Approach to Monitoring Negative Dominance for COVID-19 Vaccine-Related Information Online

2021 ◽  
pp. 1-24
Author(s):  
Paola Pascual-Ferrá ◽  
Neil Alperstein ◽  
Daniel J. Barnett
Keyword(s):  
Social Media ◽  
Adverse Events ◽  
News Media ◽  
Search Behavior ◽  
Social Media Data ◽  
Future Studies ◽  
Related Information ◽  
Youtube Videos ◽  
Negative Dominance ◽  
Media Data

Abstract Objective The aim of this study was to test the appearance of negative dominance in COVID-19 vaccine-related information and activity online. We hypothesized that if negative dominance appeared, it would be a reflection of peaks in adverse events related to the vaccine, that negative content would attract more engagement on social media than other vaccine-related posts, and posts referencing adverse events related to COVID-19 vaccination would have a higher average toxicity score. Methods We collected data using Google Trends for search behavior, CrowdTangle for social media data, and Media Cloud for media stories, and compared them against the dates of key adverse events related to COVID-19. We used Communalytic to analyze the toxicity of social media posts by platform and topic. Results While our first hypothesis was partially supported, with peaks in search behavior for image and YouTube videos driven by adverse events, we did not find negative dominance in other types of searches or patterns of attention by news media or on social media. Conclusion We did not find evidence in our data to prove the negative dominance of adverse events related to COVID-19 vaccination on social media. Future studies should corroborate these findings and, if consistent, focus on explaining why this may be the case.

Comparison of different neural networks in sentiment analysis of social media data

2021 ◽  
Author(s):  
Vadim Moshkin ◽  
Andrew Konstantinov ◽  
Nadezhda Yarushkina ◽  
Alexander Dyrnochkin
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Media Data

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

Product Review Analysis Using Social Media Data Based on Sentiment Analysis

2020 ◽  
pp. 527-533
Author(s):  
S. M. Mazharul Hoque Chowdhury ◽  
Sheikh Abujar ◽  
Ohidujjaman ◽  
Khalid Been Md. Badruzzaman ◽  
Syed Akhter Hossain
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Product Review ◽  
Social Media Data ◽  
Review Analysis ◽  
Media Data

Employing the Sentiment Analysis Tool in NVivo 11 Plus on Social Media Data

2017 ◽  
pp. 175-244
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Tool ◽  
Attitudes And Beliefs ◽  
Social Media Data ◽  
Public Issues ◽  
Public Events ◽  
Brand Reputation ◽  
Text Corpora ◽  
Media Data

Sentiment analysis has been used to assess people's feelings, attitudes, and beliefs, ranging from positive to negative, on a variety of phenomena. Several new autocoding features in NVivo 11 Plus enable the capturing of sentiment analysis and extraction of themes from text datasets. This chapter describes eight scenarios in which these tools may be applied to social media data, to (1) profile egos and entities, (2) analyze groups, (3) explore metadata for latent public conceptualizations, (4) examine trending public issues, (5) delve into public concepts, (6) observe public events, (7) analyze brand reputation, and (8) inspect text corpora for emergent insights.

scholarly journals Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 307
Author(s):  
Li Zhang ◽  
Haimeng Fan ◽  
Chengxia Peng ◽  
Guozheng Rao ◽  
Qing Cong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Hpv Vaccination ◽  
Experimental Results ◽  
Fine Tuning ◽  
Hpv Vaccines ◽  
Social Media Data ◽  
Public Sentiment ◽  
Media Data

The widespread use of social media provides a large amount of data for public sentiment analysis. Based on social media data, researchers can study public opinions on human papillomavirus (HPV) vaccines on social media using machine learning-based approaches that will help us understand the reasons behind the low vaccine coverage. However, social media data is usually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limits the application of deep learning methods in effectively training models. To tackle this problem, we propose three transfer learning approaches to analyze the public sentiment on HPV vaccines on Twitter. One was transferring static embeddings and embeddings from language models (ELMo) and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWE-BiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called fine-tuning generative pre-training (GPT) and fine-tuning bidirectional encoder representations from transformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pre-training (GPT) model. The fine-tuned BERT model was constructed with BERT model. The experimental results on the HPV dataset demonstrated the efficacy of the three methods in the sentiment analysis of the HPV vaccination task. The experimental results on the HPV dataset demonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. The fine-tuned BERT model outperforms all other methods. It can help to find strategies to improve vaccine uptake.

scholarly journals Anatomy of a Protest: Spatial Information, Social Media, and Urban Space

2020 ◽  
Vol 6 (1) ◽  
pp. 205630511989732
Author(s):  
Alireza Karduni ◽  
Eric Sauda
Keyword(s):  
Social Media ◽  
Public Space ◽  
Urban Space ◽  
Language Processing ◽  
Local Community ◽  
Spatial Information ◽  
Social Media Data ◽  
Public Events ◽  
Use Of Social Media ◽  
Media Data

Black Lives Matter, like many modern movements in the age of information, makes significant use of social media as well as public space to demand justice. In this article, we study the protests in response to the shooting of Keith Lamont Scott by police in Charlotte, North Carolina, on September 2016. Our goal is to measure the significance of urban space within the virtual and physical network of protesters. Using a mixed-methods approach, we identify and study urban space and social media generated by these protests. We conducted interviews with protesters who were among the first to join the Keith Lamont Scott shooting demonstrations. From the interviews, we identify places that were significant in our interviewees’ narratives. Using a combination of natural language processing and social network analysis, we analyze social media data related to the Charlotte protests retrieved from Twitter. We found that social media, local community, and public space work together to organize and motivate protests and that public events such as protests cause a discernible increase in social media activity. Finally, we find that there are two distinct communities who engage social media in different ways; one group involved with social media, local community and urban space, and a second group connected almost exclusively through social media.

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