• 한국콘텐츠학회논문지
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• 제19권12호
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• pp.57-67
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• 2019

뉴스 이용자들이 뉴스를 읽는 방식이 신문에서 스크린으로 바뀌고 있다. 옥스퍼드 대학 로이터 연구소의 2017년 연구에 따르면 뉴스 소비 중 온라인을 이용하는 비율이 꾸준히 증가하고 있으며 미국인의 51%이상이 소셜미디어를 통해 뉴스를 받아보고 있다. 반면에 신문 구독률은 빠르게 줄어들고 있다. 선행연구에 의하면 문자정보의 이해도는 스크린에서보다 인쇄매체에서 더 높게 나타난다. 온라인 뉴스 매체들은 양질의 뉴스를 전달하기 위해 인포그래픽 뉴스 서비스를 제공하고 있다. 따라서 본 연구에서는 뉴스에서 인포그래픽을 사용하는 것이 독자에게 미치는 영향을 이해하고자 했다. 이를 위해 세 가지 실험이 수행되었다. 인포그래픽의 이해도는 매체에 따라 어떻게 다른지 실증 분석한 결과 스크린보다 인쇄매체에서 이해도가 더 높은 것으로 밝혀졌다. 연구 결과에 따르면 뉴스에서 인포그래픽을 사용하면 인지 효과와 수용의도 측면에서 사용자에게 긍정적인 영향을 미친다. 인쇄 뉴스와 비교할 때 온라인뉴스는 이해력 면에서 효과적이지 않다. 온라인 인포그래픽의 디자인 유형에 따라 퀴즈 정답률에는 차이가 없었지만 지각된 이해도에서는 유의미한 차이가 발견되었다.

• 한국언론정보학보
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• 제58권
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• pp.7-30
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• 2012

소셜 미디어가 뉴스정보를 생성, 유포시키면서 부상한 소셜 저널리즘은 시민 저널리즘의 일환으로서 시민들이 SNS를 통해 뉴스정보를 생성하는 새로운 유형의 정보수집과 보도 방식을 의미한다. 이 연구는 <우리는 모두 할레드 사이드이다>라는 특정한 페이스북 페이지가 이집트 민주화 혁명과정에서 특정 시위기간 중에 구체적으로 어떤 뉴스정보를 얼마나 생산하고, 공중과 상호작용을 했으며, 소셜 저널리즘 양식을 통해 뉴스를 전달하였는지를 분석하였다. 연구결과, <우리는 모두 할레드사이드이다>페이지는 일주일 동안 총 331건의 포스트를 통해 시위관련 스트레이트 뉴스를 가장 많이 생산했으며, 동영상과 사진, 만평보도가 텍스트 기사 만큼 비중있게 다뤄졌다. 특히 동영상의 경우는 대부분 연결링크를 소개하고 있어 소셜 저널리즘의 특성을 잘 반영하고 있었다. 네트워크 저널리즘을 반영하는 외신보도인용은 대부분 스트레이트뉴스 에 해당되었는데 알자지라와 영국의 가디언 신문이 주로 인용되었다. 이 연구를 통해 정치적 격동기에 처한 사회에서 제도권 언론이 통제 받을 때 SNS는 현실을 반영하는 뉴스나 정보를 생성, 확산시키는 저널리즘 미디어로서 역할을 수행할 수 있음을 추론할 수 있었다.

• 대한예방한의학회지
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• 제27권3호
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• pp.1-12
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• 2023

Objectives : The aim of this study is to analyze the news articles related to Korean Medicine(KM) and compare trends in news reports from 2018 to 2022. Method : News articles related to KM were collected through the BigKinds, the news bigdata service of the Korea Press Foundation. News reports from 1 January 2018 to 31 December 2022 were searched. 2,950 news articles out of a total of 12,497 met the inclusion criteria. First, quantitative changes in media coverage were analyzed by year, media outlet, and month. For qualitative analysis, two authors independently coded the content of news articles, discussed them until consensus, and consulted with a third researcher to classify them. In addition, keywords extracted by the BigKind's Topic Rank algorithm were compared and analyzed in each year. Results : The number of news articles on KM decreased by 42% in 2022 compared to 2018. Over a fiveyear period, the Naeil Shinmun reported the most on KM among newspapers, while the Hankyoreh did the least. Among broadcasters, YTN reported the most and SBS did the least. When analyzing the reports by category, the most common was 'treatment', followed by 'prevention' and 'scientification'. As a result of extracting keywords with high weight and frequency, 'immunity' and 'immune system' ranked the first and second in 2018, while 'COVID 19' and 'medical law violation' did in 2022. Conclusion : The decrease in media reports on KM during the COVID-19 epidemic period seems to be due to the limited role of KM in responding to infectious diseases, and efforts to expand the scope of KM can induce increased media reports and social interest.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• 한국언론정보학보
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• 제30권
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• pp.35-87
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• 2005

본 연구는 인터넷발달의 과정에서 발달단계인 초기(보급 확산)부터 보편적으로 활용되고 있는 사회문화기인 현재까지 발달과정을 실증적이며 이론모델을 제시한 연구이다. 전통적인 매체에서 어떠한 방식으로 보도해왔는가를 통해 각 시기별로 나타나는 미디어적특성을 이해하는데 그 목적이 있다. 주요 뉴스미디어들이 어떠한 보도경향-틀 짓기(프레임)-방식으로 인터넷발달을 기록해 왔는지를 살펴보았다. 이를 위해 1989년부터 2004년까지의 기사를 대상으로 내용분석을 실시했다. 또한, 인터넷의 기술발전과 이용추이에 따라 4시기로 구분했는데 인터넷 상용화이전 도입기, 인터넷 확산기, 인터넷의 상업적 상용화기, 인터넷의 사회 문화기가 그것이다. 분석결과, 신문보도의 프레임 요소와 방법은 각 시기별로 대체적으로 차별적인 방식으로 구성되었다. 특히 사회 문화기로 접근할수록 초기의 기술위주의 계열화방식에서 점차적으로 통합화 방식으로 전환되어 왔다. 결국, 인터넷의 이용 폭이 확대되고 사회전반으로 그 영향력 범위가 넓어짐에 따라 신문은 사회 안에서 인터넷이 갖고 있는 의미와 문제점 등에 초점을 맞추었다는 것이다. 그러나, 기존의 미디어 대체적 시각에서 바라보는 것과 같은 경쟁적인 프레임이나 보도경향은 뚜렷하게 드러나지 않았다. 오히려 인터넷의 도입 이후 장기적으로 국가차원 이용과 같은 주제보도(thematic coverage) 차원으로부터 일상적인 이용보도인 일화보도(episodic coverage)가 많은 쪽으로 옮아갔다. 장기적으로 인터넷 보도시각은 변화양상을 보여 왔으나, 어느 한 측면(낙관적 혹은 비관적, 호의적 또는 비호의적)에 치우치지 않고 대체로 중립적인 프레임을 실증적으로 확인하였다.

• 스마트미디어저널
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• 제6권3호
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• International Journal of Knowledge Content Development & Technology
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• 제11권4호
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• pp.35-51
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• 2021

This study measured the effects of fake news and propaganda on managing information on COVID-19 among the Nigerian citizenry. This study examined sources of information on COVID-19 available to the people, evaluated reasons behind spreading fake news, examined how fake news has affected the spread of COVID-19 pandemic in Nigeria, established the consequences of fake news on managing COVID-19 pandemic and as well identified ways to contain fake news at a time like this in Nigeria.It is a survey with a sample size of 375 participants selected using simple random technique. Instrument of data gathering was questionnaire widely distributed in the six geo-political zones of Nigeria using Survey monkey. Data was analysed using frequencies, counts and percentages, tables and charts. Findings revealed that people rely more on radio, television, and social media for information on COVID-19. Fake news is spread by people mostly for political reasons and intention to cause panic. In Nigeria, fake news has led to disbelief of the existence of the virus thereby leading to violation of precautionary measures among the citizenry and lack of trust in the government. Concerted effort on the part of the government is required to give public enlightenment on the danger of fake news. Also, directorate of anti-fake news should be established to censor and reprimand sources of fake news. People should always check source of information to confirm its credibility and be weary of sharing unconfirmed information especially on the social media.

• Journal of Information Science Theory and Practice
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• 제8권3호
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• pp.77-90
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• 2020

This study analyzes N=181 social media rumors from Bangladesh to find out the most popular themes, sources, and aims. The result shows that social media rumors have seven popular themes: political, health & education, crime & human rights, religious, religiopolitical, entertainment, and other. Also, online media and mainstream media are the two main sources of social media rumors, along with three tentative aims: positive, negative, and unknown. A few major findings of this research are: Political rumors dominate social media, but its percentage is decreasing, while religion-related rumors are increasing; most of the social media rumors are negative and emerge from online media, and social media itself is the dominant online source of social media rumors; and, most of the health-related rumors are negative and surge during a crisis period, such as the COVID-19 pandemic. This paper identifies some of its limitations with the data collection period, data source, and data analysis. Providing a few research directions, this study also elucidates the contributions of its results in academia and policymaking.

• International Journal of Computer Science & Network Security
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• 제21권11호
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• pp.1-10
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• 2021

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

• Journal of Information Technology Applications and Management
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• 제26권6호
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• pp.1-12
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• 2019

On the Internet age, the news is generated and distributed not only by traditional news media, but also by a variety of online news media, news platforms, content websites/content farms, and social media. Since it is an easy task to create and distribute news, some of these news reports may contain fake or false facts. In the end, the cyberspace is full of fake or false messages. People may wonder if these fake news actually influence our decision making. In this paper, we discussed a real case of fake news. In this case, a Taiwanese company used some fake news, advertorial news, and news placement to manipulate or influence its stock price and trade volume. We collected all news for the case company during a period of four years and five months (from January 2013 to May 2017). We analyzed the relationship between published news and stock price. Based on the analysis results, we conclude that we should not ignore the influence of news placement and fake business news on the stock price.