scholarly journals THE ALGORITHM PLAYGROUND: A CONTENT ANALYSIS OF USER-PRODUCED CHILDREN’S VIDEOS ON YOUTUBE

2021 ◽  
Author(s):  
Kalia Vogelman-Natan
Keyword(s):  
Content Analysis ◽  
Pretend Play ◽  
Online Video ◽  
Video Content ◽  
Recommendation Algorithm ◽  
Mobile Media ◽  
Future Studies ◽  
Research Questions ◽  
Device Use ◽  
Video Recommendation

With early-childhood mobile media device use on the rise, online video content plays an ever-increasing role in children’s lives. Of the wide variety of content available to children, user-produced videos on YouTube seem to be most popular. However, due to the platform’s size and the overwhelming number of child-targeted videos found on YouTube, scholars have been struggling with how to approach and study this topic. This study aims to address the gap in research by analyzing prevalent user-produced children’s videos on YouTube, with research questions focusing on video genres, their features, and content themes. Drawing on YouTube’s popularity-measurements and video recommendation algorithm, a corpus of 100 user-produced videos targeted to children was assembled. A content analysis of these videos led to the identification and conceptualization of 13 distinct genres of user-produced children’s videos: unboxing, surprise eggs, finger family, play-doh, nursery rhymes, kids songs, learning, pretend play (enactment), pretend play (toys), storytelling, arts & crafts, entertainer in character, and process repetition. Furthermore, the findings indicate that there are often unique interplays between genre type and the content, the production format, and the overall quality and educational rating. In addition to shedding light on the importance of studying child-targeted content on YouTube, this study’s main contribution is a typological map of the user-produced children’s video ecosystem that future studies from various fields can draw on.

Using content analysis as a research methodology for investigating intellectual capital disclosure

2015 ◽  
Vol 16 (1) ◽  
pp. 121-155 ◽  
Author(s):  
John Dumay ◽  
Linlin Cai
Keyword(s):  
Content Analysis ◽  
Intellectual Capital ◽  
Research Methodology ◽  
Content Type ◽  
Future Studies ◽  
Current State ◽  
Rigorous Research ◽  
Comprehensive Text ◽  
Drawing Board ◽  
Research Questions

Purpose – The purpose of this paper is to build on Dumay and Cai’s (2014) prior research to provide a deeper analysis of the problems associated with using content analysis (CA) as a research methodology for investigating intellectual capital disclosure (ICD). Design/methodology/approach – Totally, 110 articles utilising CA as a research methodology for inquiring into ICD are analysed based on Krippendorff’s (2013) conceptual CA research framework and design logic, and tied into issues relating to CA as a research methodology for investigating ICD. Findings – The authors advocate that ICD CA researchers need to go back to the drawing board and ensure that future studies rigorously apply the basic logic of CA design. In its current state, ICD CA research needs to take a few steps back, before it can move forward. If ICD CA researchers can accomplish this, then there is an opportunity to undertake rigorous research to develop reliable and valid outputs that add to new knowledge about IC. Research limitations/implications – The main limitations of the research are the chosen sample of CA-based ICD articles and the adoption of the Krippendorff’s framework. However, the authors have identified the main corpus of CA-based ICD studies and since Krippendorff is the only recognised comprehensive text on CA as a methodology, the authors use the most appropriate data and framework possible for the analysis. Originality/value – Prior CA studies have laid the foundation for what is a popular research methodology. However, the authors argue that the popularity of CA as a research method for investigating ICD has become so great that at times the research methodology “drives the research questions” as opposed to the “research questions driving the methodology” Hence, this research examines reasons for CA limited contemporary contribution and recommends how this may be overcome rather than prescribing how to conduct ICD CA research.

A video recommendation algorithm based on the combination of video content and social network

2016 ◽  
Vol 29 (14) ◽  
pp. e3900 ◽  
Author(s):  
Laizhong Cui ◽  
Linyong Dong ◽  
Xianghua Fu ◽  
Zhenkun Wen ◽  
Nan Lu ◽  
...  
Keyword(s):  
Social Network ◽  
Video Content ◽  
Recommendation Algorithm ◽  
Video Recommendation

scholarly journals A Video Recommendation System for Complex Topic Learning Based on a Sustainable Design Approach

2019 ◽  
Vol 06 (03) ◽  
pp. 329-342
Author(s):  
Vargas Meza Xanat ◽  
Yamanaka Toshimasa
Keyword(s):  
Social Networks ◽  
Recommendation System ◽  
Sustainable Design ◽  
Online Video ◽  
Video Content ◽  
Complex Thinking ◽  
Educational Role ◽  
Algorithmic Bias ◽  
Video Recommendation

There are several issues compromising the educational role of social networks, particularly in the case of video-based online content. Among them, individual (cognitive and emotional), social (privacy and ethics) and structural (algorithmic bias) challenges can be found. To cope with such issues, we propose a recommendation system for online video content, applying the principles of sustainable design. Precision and recall in English were slightly lower for the system in comparison to YouTube, but the variety of recommended items increased; while in Spanish, precision and recall were higher. Expected results include fostering the adoption of complex thinking by taking on account a user’s objective and subjective contexts.

scholarly journals Shadow banking: a bibliometric and content analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ridoy Deb Nath ◽  
Mohammad Ashraful Ferdous Chowdhury
Keyword(s):  
Systematic Review ◽  
Content Analysis ◽  
Bibliometric Analysis ◽  
Banking Sector ◽  
Shadow Banking ◽  
Future Studies ◽  
Research Questions ◽  
Specific Activities ◽  
Scopus Database ◽  
Advance Knowledge

AbstractThis study reports on our systematic review of 2008–2021 literature on shadow banking. We present an overview of the shadow banking sector, wherein we discuss the definitions, evolution, functions, and specific activities that comprise it. We conducted a bibliometric analysis using the VOSviewer bibliometric tool on articles collected from the Scopus database, after which we conducted content analysis on top articles from leading sources, and identified four major streams of shadow banking literature. Additionally, we identified gaps in the literature and proposed seven research questions to be addressed in future studies to advance knowledge of the shadow banking sector. The findings of this review may serve as a robust reference for scholars researching various aspects of shadow banking to develop our understanding of this sector.

Exploring Video Sharing Websites Content with Machine Learning

2014 ◽  
Vol 5 (4) ◽  
pp. 31-50
Author(s):  
Nan Zhao ◽  
Löic Baud ◽  
Patrick Bellot
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Online Video ◽  
Video Content ◽  
Cultural Goods ◽  
Content Category ◽  
Graph Sampling ◽  
Video Sharing ◽  
Category System ◽  
Internet Users

This article studies the characteristics of content on video sharing websites. A better understanding on online video content can help to analyse Internet users' behaviour and improve the video-sharing service. We improved an existing graph-sampling algorithm so that it could be more adapted to sample over the video sharing websites. A newly category system is defined in this paper, which can be applied on many different video sharing websites for content analysis. We also implement machine learning to realize the content re-classification with the newly defined category system. The efficiency reaches at 90%. From the classified content analysis, we find the content category distribution is not constant, and nowadays, cultural goods content take about 70% over all the sampled videos.

scholarly journals Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

2021 ◽  
Vol 7 (1) ◽  
pp. 71
Author(s):  
Tan Yigitcanlar ◽  
Juan M. Corchado ◽  
Rashid Mehmood ◽  
Rita Yi Man Li ◽  
Karen Mossberger ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Local Government ◽  
Conceptual Framework ◽  
Research Agenda ◽  
Future Studies ◽  
Expert Team ◽  
Research Questions ◽  
Urban Problems ◽  
Technological Opportunities ◽  
Made In

The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes.

scholarly journals Can Watching Online Videos Be Addictive? A Qualitative Exploration of Online Video Watching among Chinese Young Adults

2021 ◽  
Vol 18 (14) ◽  
pp. 7247
Author(s):  
Zeyang Yang ◽  
Mark Griffiths ◽  
Zhihao Yan ◽  
Wenting Xu
Keyword(s):  
Social Interaction ◽  
Short Form ◽  
Leisure Activity ◽  
Online Video ◽  
Video Content ◽  
Structured Interviews ◽  
Negative Effects ◽  
Online Videos ◽  
Chinese Young Adults ◽  
Qualitative Exploration

Watching online videos (including short-form videos) has become the most popular leisure activity in China. However, a few studies have reported the potential negative effects of online video watching behaviors (including the potential for ‘addiction’) among a minority of individuals. The present study investigated online video watching behaviors, motivational factors for watching online videos, and potentially addictive indicators of watching online videos. Semi-structured interviews were conducted among 20 young Chinese adults. Qualitative data were analyzed using thematic analysis. Eight themes were identified comprising: (i) content is key; (ii) types of online video watching; (iii) platform function hooks; (iv) personal interests; (v) watching becoming habitual; (vi) social interaction needs; (vii) reassurance needs; and (viii) addiction-like symptoms. Specific video content (e.g., mukbang, pornography), platform-driven continuous watching, and short-form videos were perceived by some participants as being potentially addictive. Specific features or content on Chinese online video platforms (e.g., ‘Danmu’ scrolling comments) need further investigation. Future studies should explore users’ addictive-like behaviors in relation to specific types of online video content and their social interaction on these platforms.

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