scholarly journals Integrating multi-modal content analysis and hyperbolic visualization for large-scale news video retrieval and exploration

2008 ◽  
Vol 23 (7) ◽  
pp. 538-553 ◽  
Author(s):  
H. Luo ◽  
J. Fan ◽  
S. Satoh ◽  
J. Yang ◽  
W. Ribarsky
Keyword(s):  
Content Analysis ◽  
Large Scale ◽  
Video Retrieval ◽  
News Video

Large-scale news video retrieval via visualization

2006 ◽  
Author(s):  
Hangzai Luo ◽  
Jianping Fan ◽  
Yuli Gao ◽  
William Ribarsky ◽  
Shin'ichi Satoh
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
News Video

scholarly journals Large-Scale Video Retrieval via Deep Local Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chen Zhang ◽  
Bin Hu ◽  
Yucong Suo ◽  
Zhiqiang Zou ◽  
Yimu Ji
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Video Data ◽  
Query Image ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Storage Cost ◽  
Extraction Algorithm ◽  
Feature Aggregation ◽  
And Storage

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

A shift from science to politics : a content analysis of COVID-19 Videos on YouTube (Preprint)

2021 ◽  
Author(s):  
Chyun-Fung Shi ◽  
Matthew C So ◽  
Sophie Stelmach ◽  
Arielle Earn ◽  
David J D Earn ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Content Analysis ◽  
Large Scale ◽  
Control Measures ◽  
Mitigation Measures ◽  
Health Campaigns ◽  
Public Attention ◽  
Conflicting Information ◽  
Social Media Platforms

BACKGROUND The COVID-19 pandemic is the first pandemic where social media platforms relayed information on a large scale, enabling an “infodemic” of conflicting information which undermined the global response to the pandemic. Understanding how the information circulated and evolved on social media platforms is essential for planning future public health campaigns. OBJECTIVE This study investigated what types of themes about COVID-19 were most viewed on YouTube during the first 8 months of the pandemic, and how COVID-19 themes progressed over this period. METHODS We analyzed top-viewed YouTube COVID-19 related videos in English from from December 1, 2019 to August 16, 2020 with an open inductive content analysis. We coded 536 videos associated with 1.1 billion views across the study period. East Asian countries were the first to report the virus, while most of the top-viewed videos in English were from the US. Videos from straight news outlets dominated the top-viewed videos throughout the outbreak, and public health authorities contributed the fewest. Although straight news was the dominant COVID-19 video source with various types of themes, its viewership per video was similar to that for entertainment news and YouTubers after March. RESULTS We found, first, that collective public attention to the COVID-19 pandemic on YouTube peaked around March 2020, before the outbreak peaked, and flattened afterwards despite a spike in worldwide cases. Second, more videos focused on prevention early on, but videos with political themes increased through time. Third, regarding prevention and control measures, masking received much less attention than lockdown and social distancing in the study period. CONCLUSIONS Our study suggests that a transition of focus from science to politics on social media intensified the COVID-19 infodemic and may have weakened mitigation measures during the first waves of the COVID-19 pandemic. It is recommended that authorities should consider co-operating with reputable social media influencers to promote health campaigns and improve health literacy. In addition, given high levels of globalization of social platforms and polarization of users, tailoring communication towards different digital communities is likely to be essential.

scholarly journals A general pattern in the construction of economic newsworthiness? Analyzing news factors in popular, quality, regional, and financial newspapers

Journalism ◽  
2017 ◽  
Vol 21 (2) ◽  
pp. 279-300 ◽  
Author(s):  
Mark Boukes ◽  
Rens Vliegenthart
Keyword(s):  
Content Analysis ◽  
Large Scale ◽  
General Pattern ◽  
Geographical Proximity ◽  
Detailed Understanding ◽  
Economic News ◽  
Regional Newspapers ◽  
Different Types

Journalists use news factors to construct newsworthy stories. This study investigates whether different types of news outlets emphasize different news factors. Using a large-scale manual content analysis ( n = 6489), we examine the presence of seven news factors in economic news across four different outlets types (i.e. popular, quality, regional, and financial newspapers). Results suggest that popular and regional newspapers particularly rely on the news factors of personification, negativity, and geographical proximity. Quality newspapers, instead, employ a rather general pattern of news factors, whereas the financial newspaper consistently relies on less news factors in its reporting. Findings urge scholars to move toward a more detailed understanding of how newsworthiness is constructed in different types of news outlets.

Public communication about home burglary prevention in Belgium

2007 ◽  
Vol 30 (4) ◽  
pp. 567-586 ◽  
Author(s):  
Dave Gelders ◽  
Hans Peeraer ◽  
Jelle Goossens
Keyword(s):  
Content Analysis ◽  
Focus Group ◽  
Police Officers ◽  
Large Scale ◽  
Empirical Studies ◽  
Public Communication ◽  
Focus Group Interviews ◽  
Content Type ◽  
Group Interviews ◽  
Media Mix

PurposeThe purpose of this paper is to gain insight into the content, format and evaluation of printed public communication from police officers and governments regarding home burglary prevention in Belgium.Design/methodology/approachThe content and format in this paper is analyzed through content analysis of 104 printed communication pieces in the Belgian province of Flemish‐Brabant in 2005. The evaluation is analyzed through five focus group interviews among professionals and common citizens.FindingsThe paper finds that police zones significantly differ in terms of communication efforts. The media mix is not diverse with poor collaboration between police officers and government information officers, while intermediaries (i.e. architects) are rarely used, culminating in poor targeted communication.Research limitations/implicationsThe paper shows that only printed communication is analyzed and more large‐scale empirical research is desired.Practical implicationsThe paper shows that a richer media mix, more targeted communication, more national communication support and additional dialogue between and training of police officers and communication with professionals are advisable.Originality/valueThis paper combines two empirical studies and methods (content analysis and focus group interviews), resulting in a series of recommendations for further inquiry and future action.

Video Retrieval Queries of Large Scale Images: An Efficient Approach

2021 ◽  
Author(s):  
D. Chandra Mouli ◽  
G. Varun Kumar ◽  
S. V. Kiran ◽  
Sanjeev Kumar
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Efficient Approach

Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval

2021 ◽  
Author(s):  
Chen Jiang ◽  
Kaiming Huang ◽  
Sifeng He ◽  
Xudong Yang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Content Based Video Retrieval

The Gut Content Analysis of Polypedilum scalaenum in the Large-scale Weirs of 4 Major River Ecosystems.

2020 ◽  
Vol 53 (1) ◽  
pp. 55-62
Author(s):  
Young-Kwon Na ◽  
Hyunbin Jo ◽  
Jae-Won Park ◽  
Kwang-Hyeon Chang ◽  
Ihn-Sil Kwak
Keyword(s):  
Content Analysis ◽  
Large Scale ◽  
Gut Content Analysis ◽  
Gut Content ◽  
River Ecosystems ◽  
Major River

Logging Analysis and Prediction in Open Source Java Project

2021 ◽  
pp. 733-761
Author(s):  
Sangeeta Lal ◽  
Neetu Sardana ◽  
Ashish Sureka
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Software Development ◽  
Anomaly Detection ◽  
Open Source ◽  
Large Scale ◽  
Source Code ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
Research Questions

Log statements present in source code provide important information to the software developers because they are useful in various software development activities such as debugging, anomaly detection, and remote issue resolution. Most of the previous studies on logging analysis and prediction provide insights and results after analyzing only a few code constructs. In this chapter, the authors perform an in-depth, focused, and large-scale analysis of logging code constructs at two levels: the file level and catch-blocks level. They answer several research questions related to statistical and content analysis. Statistical and content analysis reveals the presence of differentiating properties among logged and nonlogged code constructs. Based on these findings, the authors propose a machine-learning-based model for catch-blocks logging prediction. The machine-learning-based model is found to be effective in catch-blocks logging prediction.

Sign in / Sign up

Export Citation Format

Share Document