scholarly journals Analysis of Issues Based on Disaster Type Using Text Mining and a Semantic Network

2021 ◽  
Vol 21 (3) ◽  
pp. 49-60
Author(s):  
Tae Jin Kim ◽  
Mi Ryeong Eum ◽  
Sang Hyun Park
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Forest Fire ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
The Public ◽  
State Administration ◽  
The Government ◽  
Media Channels ◽  
Negative Sentiment

Recently, the government has been increasingly communicating with the public in response to their opinions on state administration and policy projects. To examine the practicality of the public’s suggestions, this study investigated issues by disaster type, based on information from major media channels and comment data from the news. An analysis of the frequency of appearance, text mining (TF-IDF, LDA, and sentiment analysis), and the semantic network was performed by extracting the comment data of articles on the themes of “disaster” and “evacuation,” published from January 2010 to May 2020. The analysis results showed that news articles centered on these themes increased rapidly from 2017. The main disasters in Korea were those of “fire,” “typhoon,” “forest fire,” “radioactivity,” and “earthquake,” in order of enormity. Of the total negative words pertaining to “radioactivity” disasters, 43% were negative-sentiment words, and the semantic network analysis revealed that the terms “typhoon,” “forest fire,” and “earthquake” were connected to “radioactivity” disasters. This study is meaningful as it identifies issues by type of disaster and factors of anxiety expressed by the public using news and comment data, without conducting surveys and interviews.

scholarly journals A Semantic Network Analysis of News Comments on Child Abuse: Focusing on the Major Changes of Child Abuse Policies

2021 ◽  
Vol 42 (4) ◽  
pp. 457-471
Author(s):  
Sehyeon Oh ◽  
Hyunah Kang
Keyword(s):  
Child Abuse ◽  
Network Analysis ◽  
Child Protection ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
Policy Changes ◽  
The Public ◽  
Daycare Centers ◽  
Special Cases ◽  
The Government

Objectives: This study analyzes how pulic awareness of perception of child abuse and the recent child abuse policy changes appeared in the news comments about child abuse. The major policy changes include the Act on Special Cases Concerning The Punishment, Etc. of Child Abuse Crimes (Act No. 15255, Dec. 19, 2017), Mandatory CCTV Installation at Daycare Centers (2015), investigation for school children who have been absent school long-term (2016), the 100 state tasks in inclusive welfare (2017), e-Child Happiness Support Service (2018), and Strengthening the Publicness of Child Protection Service (2019).Methods: For the purpose, this study analyzed 1,333,677 comments on news about child abuse from 1 January 2014 to 31 December 2019. In this study, we conducted semantic network analysis to analyze how the contents of child abuse appeared in child abuse comments and the policy contents appeared at the time when major policies were implemented. The analysis using R program.Results: As a result of the analysis, the study found that the public recognized child abuse as a crime. Second, stereotypes on the perpetrators of child abuse were identified. Third, it was confirmed that the public is deeply interested in child abuse incidents occurred at kindergartens and daycare centers. Lastly, the result has revealed that the public, in general, does not yet acknowledge changes on the central policy of child abuse.Conclusion: Based on these findings, policy implcations are discussed to make improvements in awareness of child abuse more accessible to the public. Specifically, The government is responsible for solving stereotypes of child abuse, improving trust in daycare centers, and providing information on child care policies to the public.

scholarly journals The evolution of online discussions about GMOs in China over the past decade: Changes, causes and characteristics

2019 ◽  
Vol 2 (4) ◽  
pp. 311-325 ◽  
Author(s):  
Yang Li ◽  
Chen Luo ◽  
Anfan Chen
Keyword(s):  
New Media ◽  
Genetically Modified Organisms ◽  
Semantic Network ◽  
Online Discussions ◽  
Opinion Leaders ◽  
Semantic Network Analysis ◽  
Conspiracy Theories ◽  
The Public ◽  
The Past ◽  
The Government

This paper uses word frequency statistics and semantic network analysis to analyse text related to genetically modified organisms (GMOs) in microblog in China. We discuss the structure of the main discourses and changes in them over the past decade, explore the reasons for those changes and provide possible references that may be useful when related problems or situations occur in future. We have found that conspiracy theories permeated online discussions and that netizens’ emotions had a nationalist tendency. The GMO issue was highly socialized. Participants in online discussions were from different backgrounds, and the topics went far beyond GMO technology. The public tended to trust the government, rather than experts, while opinion leaders also played a role in guiding public opinion. The keywords in this discussion have gradually changed in recent years from clustering around ‘harmful’ to clustering around ‘scientific’, and new participation models brought about by new media have provided new reference paths for problem solving.

Barack Obama

2021 ◽  
pp. 81-110
Author(s):  
Joshua M. Scacco ◽  
Kevin Coe
Keyword(s):  
Network Analysis ◽  
Reality Television ◽  
Semantic Network ◽  
Semantic Network Analysis ◽  
White House ◽  
The Public ◽  
Web Series ◽  
The Press ◽  
The Relationship ◽  
Cascading Activation

This chapter analyzes Barack Obama’s administration in relation to the components of the ubiquitous presidency, especially how Obama adapted to the changing contexts of accessibility, personalization, and pluralism. It first tracks Twitter attention to Obama across seven years of his presidency, showing how attention spiked in relation to both traditional major addresses and newer approaches (e.g., his own tweets emphasizing elements of the ubiquitous presidency). The chapter then analyzes West Wing Week, a web series pioneered by the first official White House videographer, which takes the form of reality television and reveals the “backstage” of the presidency. Finally, the chapter uses semantic network analysis to track the relationship between the president, the press, and the public on Twitter in the context of the Affordable Care Act (commonly known as Obamacare). These relationships conform to the cascading activation model, in which presidential communication influences the terms used by the press and the public.

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