scholarly journals Semantic network analysis of vaccine sentiment in online social media

Vaccine ◽  
2017 ◽  
Vol 35 (29) ◽  
pp. 3621-3638 ◽  
Author(s):  
Gloria J. Kang ◽  
Sinclair R. Ewing-Nelson ◽  
Lauren Mackey ◽  
James T. Schlitt ◽  
Achla Marathe ◽  
...  
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
Online Social Media

scholarly journals Network Analysis of COVID-19 Vaccine Misinformation on Social Media

2021 ◽  
Author(s):  
Chad Melton ◽  
Olufunto A. Olusanya ◽  
Arash Shaban-Nejad
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Semantic Network ◽  
Vaccine Safety ◽  
Vaccine Hesitancy ◽  
World Population ◽  
Semantic Network Analysis ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Media Platform

Almost half of the world population has received at least one dose of vaccine against the COVID-19 virus. However, vaccine hesitancy amongst certain populations is driving new waves of infections at alarming rates. The popularity of online social media platforms attracts supporters of the anti-vaccination movement who spread misinformation about vaccine safety and effectiveness. We conducted a semantic network analysis to explore and analyze COVID-19 vaccine misinformation on the Reddit social media platform.

scholarly journals Exploring halal tourism tweets on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ali Feizollah ◽  
Mohamed M. Mostafa ◽  
Ainin Sulaiman ◽  
Zalina Zakaria ◽  
Ahmad Firdaus
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Topic Modeling ◽  
Semantic Network ◽  
Word List ◽  
Global Market ◽  
Semantic Network Analysis ◽  
Tourist Destinations ◽  
Muslim Countries ◽  
Emotion Lexicon

AbstractThis study explores tweets from Oct 2008 to Oct 2018 related to halal tourism. The tweets were extracted from twitter and underwent various cleaning processes. A total of 33,880 tweets were used for analysis. Analysis intended to (1) identify the topics users tweet about regarding halal tourism, and (2) analyze the emotion-based sentiment of the tweets. To identify and analyze the topics, the study used a word list, concordance graphs, semantic network analysis, and topic-modeling approaches. The NRC emotion lexicon was used to examine the sentiment of the tweets. The analysis illustrated that the word “halal” occurred in the highest number of tweets and was primarily associated with the words “food” and “hotel”. It was also observed that non-Muslim countries such as Japan and Thailand appear to be popular as halal tourist destinations. Sentiment analysis found that there were more positive than negative sentiments among the tweets. The findings have shown that halal tourism is a global market and not only restricted to Muslim countries. Thus, industry players should take the opportunity to use social media to their advantage to promote their halal tourism packages as it is an effective method of communication in this decade.

scholarly journals War Metaphors in Chinese Digital Media Coverage of COVID-19

2021 ◽  
Vol 14 (1) ◽  
pp. 9-27
Author(s):  
Zheng Yang
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Digital Media ◽  
Media Coverage ◽  
Semantic Network ◽  
Chinese People ◽  
Semantic Network Analysis ◽  
Social Media Platform ◽  
Media Platform ◽  
Chinese Social Media

War metaphors have been found to be the most frequently used metaphors for conceptualizing diseases, epidemic and medicine. During the COVID-19 epidemic, war metaphors have been found to be widely used in both online and offline coverage. This study mainly focuses on how war metaphors were used in Chinese social media coverage about the COVID-19 epidemic. Using the method of semantic network analysis and the account of The People’s Daily on the Chinese social media platform Weibo as an example, the findings show that war metaphors are widely used in the digital coverage of COVID-19. Compared with defensive metaphors and war process metaphors, offensive war metaphors are appearing much more frequently in digital coverage, and often with the use of national collective subjects. These two characteristics highlight how digital coverage uses militarized metaphors to mobilize and inspire enthusiasm among the Chinese people, and to strengthen the Chinese government’s control in dealing with the COVID-19 epidemic.

scholarly journals Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yeong-Hyeon Choi ◽  
Seungjoo Yoon ◽  
Bin Xuan ◽  
Sang-Yong Tom Lee ◽  
Kyu-Hye Lee
Keyword(s):  
New York ◽  
Social Media ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
Social Media Data ◽  
Big 4 ◽  
Media Data

AbstractThis study used several informatics techniques to analyze consumer-driven social media data from four cities (Paris, Milan, New York, and London) during the 2019 Fall/Winter (F/W) Fashion Week. Analyzing keywords using a semantic network analysis method revealed the main characteristics of the collections, celebrities, influencers, fashion items, fashion brands, and designers connected with the four fashion weeks. Using topic modeling and a sentiment analysis, this study confirmed that brands that embodied similar themes in terms of topics and had positive sentimental reactions were also most frequently mentioned by the consumers. A semantic network analysis of the tweets showed that social media, influencers, fashion brands, designers, and words related to sustainability and ethics were mentioned in all four cities. In our topic modeling, the classification of the keywords into three topics based on the brand collection’s themes provided the most accurate model. To identify the sentimental evaluation of brands participating in the 2019 F/W Fashion Week, we analyzed the consumers’ sentiments through positive, neutral, and negative reactions. This quantitative analysis of consumer-generated social media data through this study provides insight into useful information enabling fashion brands to improve their marketing strategies.

A Semantic Network Analysis of Parenting Stress on Social Media

2020 ◽  
Vol 38 (1) ◽  
pp. 61-77
Author(s):  
Subin Ahn ◽  
Kangyi Lee ◽  
Jaerim Lee ◽  
Eunkyung Kim
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Parenting Stress ◽  
Semantic Network ◽  
Semantic Network Analysis

scholarly journals We Care About Different Things: Non-Elite Conceptualizations of Social Media Privacy

2019 ◽  
Vol 5 (3) ◽  
pp. 205630511986600
Author(s):  
Kelly Quinn ◽  
Dmitry Epstein ◽  
Brenda Moon
Keyword(s):  
Social Media ◽  
Network Analysis ◽  
Topic Modeling ◽  
Representative Sample ◽  
Research Policy ◽  
Semantic Network ◽  
Information Access ◽  
Semantic Network Analysis ◽  
Privacy Concerns ◽  
Unique Dataset

This study explores privacy from the perspective of the user. It leverages a “framing in thought” approach to capture how users make sense of privacy in their social media use. It builds on a unique dataset of privacy definitions collected from a representative sample of 608 US social media users. The data are analyzed using topic modeling and semantic network analysis to unpack the multidimensionality of social media privacy. These dimensions are further examined in relation to established demographic antecedents of privacy concerns and behaviors. Results indicate the dominance of frames related to horizontal dimensions of privacy, or privacy vis-à-vis peers, as compared with the vertical dimensions, or privacy vis-à-vis institutions. In addition, the findings suggest that user conceptualization of privacy reflects a cognate-based approach that emphasizes control and limits to information access. Implications for privacy research, policy, and technology design are discussed.

scholarly journals Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry

2021 ◽  
Vol 13 (7) ◽  
pp. 3813
Author(s):  
Chorong Youn ◽  
Hye Jung Jung
Keyword(s):  
Social Media ◽  
Big Data ◽  
Network Analysis ◽  
Textile Industry ◽  
Semantic Network ◽  
Fashion Industry ◽  
Semantic Network Analysis ◽  
Sustainable Fashion ◽  
Negative Environmental Impact ◽  
Google Search

Consumers are becoming increasingly aware and sensitive to the negative environmental impact caused by the fashion industry and by consumers’ high consumption of fashion. This study analyzes people’s unfiltered comments and behaviors on social media sites related to the sustainability of fashion products. Recently, the number of social media data, called big data, has exploded, transcending the level that can be analyzed with existing tools. This study aims to identify consumers’ perceptions of sustainable fashion using the search words “sustainable fashion” to examine public opinion trends found in SNS big data. Text mining was employed to extract meaningful words from the SNS texts using semantic network analysis to analyze the connectivity and propagation trends. The text data were collected from Facebook using the Google search engine to detect tendencies in the occurrence of keywords related to sustainable fashion in SNS over the past five years (2016~2020). The results revealed that the keywords “eco-friendly”, “ethical”, and “recycle” had the highest frequency and centrality. As a result of grouping the keywords based on their correlations, sustainable fashion texts from the SNS data could be classified into four groups: “supply chain of sustainable fashion”, “circular fashion”, “fashion business concepts for sustainability”, and “academic importance of sustainable fashion”. This study strengthens the extent of research by using SNS big data and provides guidelines for product development and communication strategies for a sustainable fashion industry based on customers’ meaningful opinions.

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