From Hating Places to Hating People: A Topic Model Analysis of Hate-Related Discourses in South Korean News Reports

2020 ◽  
Vol 28 (4) ◽  
pp. 5-27
Author(s):  
Youngwook Kim ◽  
Seungkyung Ham ◽  
Ji-Myung Choi ◽  
Hyejung Kim
2013 ◽  
Vol 347-350 ◽  
pp. 2625-2630
Author(s):  
Zhen Yu Zhou ◽  
Fang Li
Keyword(s):  

This paper describes the study of topics on microblog based on specific events. First, we use a famous topic model LDA to extract topics from microblog about events. Then, we propose three indexes: Attention Factor (AF), Evolution Factor (EF) to see the performance of microblog topics and Diversity Factor (DF) to calculate the divergence of topics from microblog and news reports. Finally, we choose corpuses for four events to study. The experiments show that, on specific events: 1) There are more critical topics, while factual topics less, and both of them get close AFs. 2) Critical topics last long on microblog and have lower EFs, which means their contents vary little, but factual topics last intermittently and their contents vary greatly. 3) To compare with the same events from news reports, critical topics use totally different words, but factual topics use close words.


2019 ◽  
Vol 173 (1) ◽  
pp. 142-157
Author(s):  
Thomas Chase

This article examines an underexplored area of communication studies to date, the relationship between news translation and national identity construction in China. By analysing the translation into Chinese of Korean language news that takes place on the Chinese volunteer news translation website Ltaaa.com , this article shows how the translation of news reports, and the discussions which these translations engender within the Ltaaa.com community, help to foster an aggressive form of Chinese national identity among community members. By constantly emphasising difference and promoting hostility, through attempts to control historical discussion and by asserting a superordinate status and position for China in its relations with Korea, the translation activities that take place within the Ltaaa.com community encourage the growth of a xenophobic, belligerent and condescending nationalism that is likely to hinder the development of more productive Sino-South Korean ties.


2018 ◽  
Vol 24 (2) ◽  
pp. 221-264 ◽  
Author(s):  
SABINE GRÜNDER-FAHRER ◽  
ANTJE SCHLAF ◽  
GREGOR WIEDEMANN ◽  
GERHARD HEYER

AbstractSocial media are an emerging new paradigm in interdisciplinary research in crisis informatics. They bring many opportunities as well as challenges to all fields of application and research involved in the project of using social media content for an improved disaster management. Using the Central European flooding 2013 as our case study, we optimize and apply methods from the field ofnatural language processingand unsupervised machine learning to investigate the thematic and temporal structure of German social media communication. By means of topic model analysis, we will investigate which kind of content was shared on social media during the event. On this basis, we will, furthermore, investigate the development of topics over time and apply temporal clustering techniques to automatically identify different characteristic phases of communication. From the results, we, first, want to reveal properties of social media content and show what potential social media have for improving disaster management in Germany. Second, we will be concerned with the methodological issue of finding and adapting natural language processing methods that are suitable for analysing social media data in order to obtain information relevant for disaster management. With respect to the first, application-oriented focal point, our study reveals high potential of social media content in the factual, organizational and psychological dimension of the disaster and during all stages of the disaster management life cycle. Interestingly, there appear to be systematic differences in thematic profile between the different platforms Facebook and Twitter and between different stages of the event. In context of our methodological investigation, we claim that if topic model analysis is combined with appropriate optimization techniques, it shows high applicability for thematic and temporal social media analysis in disaster management.


Water Policy ◽  
2017 ◽  
Vol 19 (3) ◽  
pp. 496-512 ◽  
Author(s):  
Hanchen Jiang ◽  
Maoshan Qiang ◽  
Peng Lin ◽  
Qi Wen ◽  
Bingqing Xia ◽  
...  

Development of the Brahmaputra River, which links China, India and Bangladesh, has been hindered by significant challenges, particularly political challenges. News reports can mirror the perceptions of political actors, but are, owing to the complexity of the issue, complicated and unstructured. We present a comparative content analysis of the overall framing in news reports of the Brahmaputra River development from major English news media. A structural topic model is established to discover latent topics in the corpus of 1,569 news articles published in 34 countries or regions. We find that politics, including domestic and international politics, dominates the news narratives. Environmental issues, such as glacier status and climate change impacts, are secondarily discussed. Technology and economy issues are less frequently presented in the media coverage. Advantages of upstream countries and dependences of downstream countries are reflected in news reporting and explicitly emerge in the structural topic model. These findings and implications are important for promoting mutual understanding and cooperation among riparian countries in developing the Brahmaputra River. The proposed approach is expected to be widely used as a methodological strategy in future water policy studies.


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