• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.110-116
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• 2011

The production environment of fast and accurate news video has been continuously changing together with technology advance. Especially, the editing of the news video advanced focusing on the non-linear system by the introduction of digital technology and the recording of news video advanced to 'tapeless' base. In this new news video production environment, the recorded news videos are transmitted, edited and archived through the NPS (Network Production System), which dramatically improves the efficiency of the news video production. However, there are some issues which need to be resolved in the introduction of NPS. The purpose of this research is to identify the necessity of NPS introduction in the changing news video production environment, explore the issues which should be resolved and suggest the proper way to introduce the NPS to broadcasting companies.

• The Journal of Information Systems
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• v.32 no.2
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• pp.87-108
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• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.

• Asian Journal for Public Opinion Research
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• v.9 no.1
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• pp.25-47
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• 2021

This study analyzes 419 fake news items published in India, a fake-news-prone country, to identify the major themes, content types, and sources of social media fake news. The results show that fake news shared on social media has six major themes: health, religion, politics, crime, entertainment, and miscellaneous; eight types of content: text, photo, audio, and video, text & photo, text & video, photo & video, and text & photo & video; and two main sources: online sources and the mainstream media. Health-related fake news is more common only during a health crisis, whereas fake news related to religion and politics seems more prevalent, emerging from online media. Text & photo and text & video have three-fourths of the total share of fake news, and most of them are from online media: online media is the main source of fake news on social media as well. On the other hand, mainstream media mostly produces political fake news. This study, presenting some novel findings that may help researchers to understand and policymakers to control fake news on social media, invites more academic investigations of religious and political fake news in India. Two important limitations of this study are related to the data source and data collection period, which may have an impact on the results.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.89-98
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• 2007

For browsing, searching, and manipulating video documents, an indexing technique to describe video contents is required. Until now, the indexing process is mostly carried out by specialists who manually assign a few keywords to the video contents and thereby this work becomes an expensive and time consuming task. Therefore, automatic classification of video content is necessary. We propose a fully automatic and computationally efficient method for analysis and summarization of spots news video for 5 spots news video such as soccer, golf, baseball, basketball and volleyball. First of all, spots news videos are classified as anchor-person Shots, and the other shots are classified as news reports shots. Shot classification is based on image preprocessing and color features of the anchor-person shots. We then use the dominant color of the field and motion features for analysis of sports shots, Finally, sports shots are classified into five genre type. We achieved an overall average classification accuracy of 75% on sports news videos with 241 scenes. Therefore, the proposed method can be further used to search news video for individual sports news and sports highlights.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1410-1417
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• 1999

In this paper, we propose the method of extracting the caption regions from news video and the method of recognizing the captions that can be used mainly for content-based indexing and retrieving the MPEG-2 compressed news for NOD(News On Demand). The proposed method can reduce the searching time on detecting caption frames with minimum MPEG-2 decoding, and effectively eliminate the noise in caption regions by deliberately devised preprocessing. Because the kind of fonts that are used for captions is not various in the news video, an enhanced template matching method is used for recognizing characters. We could obtain good recognition result in the experiment of sports news video by the proposed methods.

• Journal of KIISE:Databases
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• v.37 no.3
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• pp.127-136
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• 2010

With rapid growth of information and computer communication technologies, the numbers of digital documents including multimedia data have been recently exploded. In particular, news video database and news video mining have became the subject of extensive research, to develop effective and efficient tools for manipulation and analysis of news videos, because of their information richness. However, many research focus on browsing, retrieval and summarization of news videos. Up to date, it is a relatively early state to discover and to analyse the plentiful latent semantic knowledge from news videos. In this paper, we propose the news video mining system based on multi-modal approach and text mining, which uses the visual-textual information of news video clips and their scripts. The proposed system systematically constructs a taxonomy of news video stories in automatic manner with hierarchical clustering algorithm which is one of text mining methods. Then, it multilaterally analyzes the topics of news video stories by means of time-cluster trend graph, weighted cluster growth index, and network analysis. To clarify the validity of our approach, we analyzed the news videos on "The Second Summit of South and North Korea in 2007".

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.530-539
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• 2002

Recently, there have been a lot of researches to develop an archive system for news videos that usually has a fixed structure. However, since the meta-data representation and storing schemes for news video are different from each other in the previously proposed archive systems, it was very hard to exchange these meta-data. This paper proposes a scheme for news video based on MPEG-7 MDS that is an international standard to represent the contents of multimedia, and a summarization mechanism reflecting the characteristics of shots in the news videos. The proposed scheme for news video uses the MPEG-7 MDS schemes such as VideoSegment and TextAnnotation to keep the original structure of news video, and the proposed summarization mechanism uses a slide-show style presentation of key frames with associated audio to reduce the data size of the summary video.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.54-60
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• 2011

The study is content-based video retrieval system based on the news video domain as researching for the video data processing method for searching the multimedia information. For the implementation of effective system, We retrieval meaning information and special information using the knowing knowledge about formation and structure of the video data. These are possible to retrieval searching by articles fast and accurately by indexing contents the users want to search. The news domain used in experiment of our system is the KBS news on the air nowadays and precision and recall is used to evaluate the experiment and performance.

• Journal of Korea Multimedia Society
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• v.10 no.5
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• pp.559-568
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• 2007

Anchor-person scene detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper proposes an efficient algorithm extracting anchor ranges that exist in sports news video for unit structuring of sports news. To detect anchor person scenes, first, anchor person candidate scene is decided by DCT coefficients and motion vector information in the MPEG4 compressed video. Then, from the candidate anchor scenes, image processing method is utilized to classify the news video into anchor-person scenes and non-anchor(sports) scenes. The proposed scheme achieves a mean precision and recall of 98% in the anchor-person scenes detection experiment.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1130-1139
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• 1998

This paper presents an integrated solution for the content-based news video indexing and the retrieval. Currently, it is impossible to automatically index a general video, but we can index a specific structural video such as news videos. Our proposed model extracts automatically the key frames by using the structured knowledge of news and consists of the news item segmentation, caption recognition and search browser modules. We present above three modules in the following: the news event segmentation module recognizes an anchor-person shot based on face recognition, and then its news event are divided by the anchor-person's frame information. The caption recognition module detects the caption-frames with the caption characteristics, extracts their character region by the using split-merge method, and then recognizes characters with OCR software. Finally, the search browser module could make a various of searching mechanism possible.