Video Comments Research Articles

Pelatihan Pembuatan Kerajinan dari Manik-manik untuk Meningkatkan Kreativitas Anak-anak di Rumah Yoel

This research aims to: Provide assistance to the children of the Rumah Yoel Community to build entrepreneurial skills using the main materials of beads and string which are cheap and easy to obtain. Building children's mindset to become independent, creative and highly competitive individuals in society. This research uses descriptive qualitative methods. The research subjects were the children of the Rumah Yoel Community. The training participants were 16 children in the Rumah Yoel Community. The meeting took place over three meetings with stages, 1) observation and interviews with the Rumah Yoel Community administrators, 2) observation and interviews with the children with the assistance of Rumah Yoel Community teachers, 3) skills training for the Rumah Yoel Community children. Training is evaluated using the results of service video comments, pretest and posttest. Results of the service: 1) researchers were able to train children's skills in making crafts through creativity using the main materials of beads and rope so that children were able to make key chains, bracelets, headbands and rings. Second; 2) researchers invite children to use packaging for their work so that it has economic value. 3) Researchers are able to invite children to take part in activities according to the procedures directed in making works using practical methods. 4) researchers provide skills for the children of the Rumah Yoel community because by training them to be creative with beads it can motivate them to become entrepreneurs from an early age to help their parents.

“Stuck in, Can’t Come Out”: Physical Distancing and YouTube Spaces for LGBTQ Adolescents’ Wellbeing

The pandemic has led to physical distancing measures put in place to minimize the spread of COVID-19, leading schools and community centers to close their physical locations. For many lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals living with their family, their home can be considered harmful and unsupportive due to their family’s rejection towards LGBTQ identity. LGBTQ YouTubers can be a unique avenue to understand how they can use their social media presence to act as an online supportive system that parallel traditional offline supportive systems during the pandemic where offline supportive spaces are limited. The purpose of the study is to analyze online video platforms (ie. YouTube) and whether LGBTQ YouTubers can act as support systems for their LGBTQ community during the pandemic through video content creation. Four LGBTQ YouTubers that have created videos related to the COVID-19 outbreak were chosen and their video comments (NVideo1 = 147; NVideo2 = 195; NVideo3 = 238; NVideo4 = 79) were analyzed through content analysis. Results revealed the following categories: 1) Community support and engagement between the community and the YouTuber, 2) diverse coping strategies as a result of COVID-19, 3) community’s emotional experiences surrounding COVID-19, 4) community members’ use of diverse support types, 5) YouTuber authenticity and relatability to community, and 6) intersectional identity (e.g., disability, ethnicity) experiences. Emerging themes suggest LGBTQ YouTubers can be informal online social support systems that can parallel the physical connections and support lost due to COVID-19.

Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments

Technology-mediated group toxicity polarization is a major socio-technological issue of our time. For better large-scale monitoring of polarization among social media news content, we quantify the toxicity of news video comments using a Toxicity Polarization Score. For polarizing news videos, our premise is that the comments’ toxicity approximates either an “M” or “U” shaped distribution—that is, there is unevenly balanced toxicity among the comments. We evaluate our premises through a case study using a dataset of ~180,000 YouTube comments on ~3,700 real news videos from an international online news organization. Toward polarization-mitigating information systems, we build a predictive machine learning model to score the toxicity polarization of news content even when its comments are disabled or not available, as it is a current trend among news publishers to disable comments. Findings imply that the most engaging news content is also often the most polarizing, which we associate with increasing research on clickbait content and the detrimental effect of attention-based metrics on the health of online social media communities, especially news communities.

Investigating the Coverage of China’s Vaccine Crisis on YouTube: Networked Framing, Grassroots Activism, and Homophily

ABSTRACT This study investigates how China’s domestic vaccine crisis in 2018 was politicized within Chinese video content on YouTube. The analysis, employing the Structural Topic Model, uncovered a notable hyper-politicization of the vaccine crisis in video comments, often diverging from the issue frames presented in video titles. Furthermore, inferential network analysis using the Exponential Random Graph Model revealed the hyper-politicized comments can be attributed to 1) the emergence of networked commenters engaging in cross-video commenting practices, and 2) political homophily among video producers, particularly regarding their critical political stance toward China. The findings suggest the networked dynamics of ssue politicization.

‘유보통합’ 관련 유튜브 영상 댓글 분석

‘유보통합’ 관련 유튜브 영상 댓글 분석

Sentiment-Oriented Transformer-Based Variational Autoencoder Network for Live Video Commenting

Automatic live video commenting is getting increasing attention due to its significance in narration generation, topic explanation, etc. However, the diverse sentiment consideration of the generated comments is missing from current methods. Sentimental factors are critical in interactive commenting, and there has been lack of research so far. Thus, in this article, we propose a Sentiment-oriented Transformer-based Variational Autoencoder (So-TVAE) network, which consists of a sentiment-oriented diversity encoder module and a batch attention module, to achieve diverse video commenting with multiple sentiments and multiple semantics. Specifically, our sentiment-oriented diversity encoder elegantly combines a VAE and random mask mechanism to achieve semantic diversity under sentiment guidance, which is then fused with cross-modal features to generate live video comments. A batch attention module is also proposed in this article to alleviate the problem of missing sentimental samples, caused by the data imbalance that is common in live videos as the popularity of videos varies. Extensive experiments on Livebot and VideoIC datasets demonstrate that the proposed So-TVAE outperforms the state-of-the-art methods in terms of the quality and diversity of generated comments. Related code is available at https://github.com/fufy1024/So-TVAE .

GRAPHIC AND ORTHOGRAPHIC FEATURES OF ENGLISH AND GEORGIAN-LANGUAGE INTERNET DISCOURSE (BASED ON YOUTUBE VIDEO COMMENTS)

The present paper is devoted to the investigation of the linguistic (mainly graphic and orthographic) features of Internet Discourse, as a new type of language. Graphic and orthographic features of Internet Discourse are investigated on the basis of English and Georgian YouTube video comments. The analysis showed that Internet Discourse is characterized by specific graphic and orthographic features: the usage of emoticons, unusual usage of punctuation marks, multiple punctuation, unconventional spelling, capitalization, etc.

Feature Extraction for Improvement Text Classification of Spam YouTube Video Comment using Deep Learning

The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT integrates spam YouTube video comments into the traditional TF-IDF algorithm and generates a weighted word vector. The weighted word vector is fed into BiLSTM CRF to capture context information effectively. The result of this study is a new classification model to spam YouTube comment videos and increase the computational value of its performance. This research conducted two experiments: the first using BiLSTM CRF without DAT and the second using BiLSTM CRF with DAT. The experimental results state that the evaluation score using BiLSTM CRF with DAT shows outstanding performance in text classification, especially in spam YouTube video comment texts, with accuracy = 83.3%, precision = 83.6%, recall = 83.3%, and F-measure = 83.3%. So the combination of the BiLSTM-CRF method and the Data Augmentation Technique is very precise, so it can be used to increase the accuracy of classification texts for spam YouTube video comments

Sentiment Analysis on Acute Kidney Syrup Videos Using CNN and LSTM Algorithms

The issue of acute kidney failure, particularly caused by the consumption of cough syrup, was circulating around October 2022 and has become a serious public health concern. This issue has drawn extensive attention and sparked various reactions on social media. In this digital era, public opinion expressed in comments on social media platforms like YouTube significantly impacts societal perceptions. Therefore, in the context of the aforementioned issue, sentiment analysis on YouTube video comments can provide valuable insights into societal perceptions and people’s reactions. Therefore, this study focuses on the sentiment analysis of public opinions expressed in YouTube comments related to this matter. The methods employed for this analysis include Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with Word2Vec feature extraction. The findings of this study indicate that both these methods produce good performance results with an oversampling dataset with a 90:10 data proportion. In the performance comparison, CNN yielded the highest accuracy, at 0.92, while LSTM was at 0.90.

Perceptions of the COVID-19 pandemic and government response among Turkish-speaking immigrants in Germany during the initial lockdown period.

Understanding the socioeconomic and health impacts of the pandemic and their consequences on immigrants necessitates consideration of how they have perceived the global threat. By applying agenda-setting theory to Turkish-speaking immigrants in Germany as a case study, I investigated what issues created a sense of urgency for them during the initial lockdown and how they approached the government's COVID-19-related agenda. I used purposeful sampling and video comments on COVID-19-related news created by three journalists on YouTube channels and applied qualitative content analysis to interpret the comments. Analysis of 118 comments and 68 replies showed that social media played an important role in immigrants' awareness of COVID-19, especially about authorities' responses, pandemic-related measures, and economic aid packages. Immigrants perceived the information as cause for concern and reactions varied by socioeconomic status. Comments not only supported authorities pandemic strategies but also indicated doubts caused by lack of transparency, certain recommendations, regulations, and aid.

What Indonesian Netizen Says about Covid-19 Vaccination? A Corpus Assisted Discourse Studies of YouTube’s Comment

This research analyzes the views of the Indonesian people regarding Covid-19 vaccination through comments on the YouTube social media platform. This study aimed to analyze arguments, discussion patterns, attitudes, and views of the Indonesian people in the YouTube video comments at the start of the Covid-19 vaccination in Indonesia. Academically, this research provides in-depth information about the views of the Indonesian people on Covid-19 vaccination, which comes from online comments. This research is helpful for information providers and governments to make evidence-based policies and campaigns for effective communication strategies for vaccination programs. This study uses mixed methods by combining corpus linguistic methods with critical discourse analysis. Using the help of Antcont software, we analyzed 22,529 comments on the five videos about Covid-19 vaccination. The results showed various public views about vaccines; we found fifteen categories of comments: actors, health, media, rejection, acceptance, state, religious, mass, Islam, other countries, law, economy, region, knowledge, and politics. The contribution of this research is to fill in the gaps in the literature about people's reactions to vaccination in the context of social media. The implication of this research is to contribute to decision makers to take evidence-based policies to communicate Covid-19 vaccination.

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

The exponential rise of internet platforms, notably YouTube, has resulted in a massive volume of user-generated material, including video comments. Understanding audience input and enhancing user experience require analyzing and forecasting the mood of YouTube comments. This work provides a comprehensive method for YouTube comment prediction and sentiment classification that combines feature selection using Recursive Feature Elimination (RFE), Elastic Net Random Forest with Logistic Regression (RF with LR), and Principal Component Analysis (PCA). The first stage is to choose the most informative features from a given dataset using RFE, a common approach for doing so. RFE aids in the elimination of unnecessary or redundant features, resulting in enhanced model performance and decreased computing complexity. The Elastic Net Random Forest with Logistic Regression (RF with LR) technique is then used to construct a robust sentiment classification model. Elastic Net regularization combines the advantages of both L1 (Lasso) and L2 (Ridge) regularization, allowing for improved feature selection and management of multicollinearity concerns. By integrating many decision trees, the Random Forest ensemble approach improves the model's predictive potential even more. We employ Principal Component Analysis (PCA) to increase the classification model's effectiveness and solve possible difficulties created by high-dimensional data. PCA decreases the dataset's complexity while retaining its fundamental qualities, resulting in a more manageable and efficient feature space for classification. Finally, we compare the performance of three prominent classifiers on the preprocessed dataset: Linear Support Vector Machine (LSVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Decision Tree (DT). We can select the best-performing model for YouTube comment categorization by comparing these classifiers.

Prematüre erken kontraksiyon ile ilgili YouTube videolarının kalite ve güvenilirliğinin değerlendirilmesi

Aim: 
 The internet is a popular and continually utilized platform to gain medical knowledge for patients and health professionals. This research aimed to evaluate the accuracy and quality of videos for premature ventricular contraction (PVC) on YouTube.
 Material and method: The keyword "premature ventricular contraction" was inputted into the YouTube search engine. The 60 most watched videos on YouTube were analyzed. Six of these were excluded from the study. A total of 54 patients were included in the study. General characteristics of the videos were recorded. Two specialist physicians reviewed all videos. GQS, DISCERN, and JAMA scoring systems were used to test the video quality and reliability. Video quality was divided into three groups according to the GQS score. Video characteristics were compared between quality groups.
 Results: 37% of the videos posted were from doctors, and the most frequently seen content was related to general information about PVCs, accounting for 70.4%. There were 24 high-quality videos. The number of video comments (p=0.006), daily view rate (p=0.001), JAMA (p=

The most accepted opinions are mindful: based on research evidence from online video comments

From existing research evidence on the positive relationship between mindfulness and self-well-being, I predict that mindfulness has positive effects on interpersonal relationships. This research is focused on providing solid evidence of the positive relationships between mindful opinions and acceptance. The data is based on Bilibili (a website known as the Chinese version of Youtube), including 22,000 comments that are drawn from 11 types of videos, adding up to nearly 270,000 Chinese characters. All of those comments are analyzed one by one through a judging system created by myself. Using the judging system, the research first aims to verify the correction of the hypothesis and then demonstrate detailed connections between mindful opinions and acceptance. (“Main comments” in this paper refer to the comments directly below the video; ”comments ”in this paper refers to the comments of main comments).

Anti-stigma narratives and emotional comfort against health crisis: a context analysis of UGC short videos from patients with COVID-19 infections

Patients narratives are being recorded increasingly frequently and spontaneously in short user produced content (UGC) films, which may have an impact on the vlogger’s health as well as the public’s comprehension of the relevant health concerns. This paper addressed three research questions regarding the population characteristics of UGC video publishers, the narrative theme of the videos, and the emotional orientation of the commenters. This study aimed to deepen our understanding of COVID-19 patients’ narrative intentions and emotional needs through the theoretical frameworks of theory of planned behavior (TPB) and negative dominance theory (NDT). We collected 335 videos from 28 COVID-19 patients and 572,052 comments as samples on Douyin platform, the largest short-video website in China. Using Latent Semantic Analysis, we analyzed the descriptive information of the video blogs, the narrative textual information of the videos, and the emotional orientation of the comments. Our findings revealled seven categories of narrative themes, with 52.1% of video comments exhibiting a positive emotional orientation. Within a framework integrating TPB and NDT theories, we analyzed the behavioral intentions of vloggers and viewers during COVID-19 epidemic, and interpreted the persistent posting of videos and the active posting of comments as positive actions that counteracted the multiple effects of negative messages. This study contributes to the understanding of individual narratives in macro-risk communication, both theoretically and empirically, and offers policy recommendations in relevant fields.

토픽모델링을 활용한 ‘자해’관련 유튜브 댓글의 내용분석

Objectives In this study, YouTube video comments were collected and analyzed to confirm the impact of YouTube related to “self-harm” on those who do not self-harm and to find effective ways to help those who do self-harm.
 Methods To this end, comments on the top nine videos derived by searching for the keyword “self-harm” on YouTube were collected, and 10 topics were derived by topic modeling comments from users who did not seem to have self-harm experience. Topic modeling is an analysis method that finds hidden topics based on statistics that analyze big data.
 Results The topics of the selected topics are “You need empathy and comfort to help self-inflicted people live in the world.”, “Concerns and discomfort towards self-harming people who worry about scars on their bodies.”, “Presenting a religious solution to self-harm.”, “We should consider self-harm as a social problem and find alternative ways together.”, “Thank you for telling me the reactions that people around you can do.”, “I remember my experiences related to self-harm that I experienced in my adolescence.”, “Need safe and professional counseling on self-harm,” “The way adults deal with self-harm.”, “Reaction to posting self-harm photos.”, “to be aware of one's psychological difficulties through an understanding of others' self-harm.”.
 Conclusions In the comments on YouTube related to “self-harm,” users' efforts and supportive actions to sympathize with and understand those who harm themselves were confirmed. On the other hand, concerns and concerns about self-harm scars appeared at the same time, and although I understand the hard feelings, it was confirmed that there was a negative opinion on self-harm behavior. Finally, we proposed a way to help those who harm themselves through the contents of 10 topics.

Public emotional atmosphere during disasters: understanding emotions in short video comments on the Zhengzhou flood

Short videos are gaining popularity among Chinese netizens who use them to socialize and express themselves online, particularly during disasters. This study examines the characteristics of an emotional atmosphere through short video comments. We developed the Multimodal Social Short Video Crawler V1.0 to analyze 157,747 comments from 343 short videos about the “Zhengzhou flood.” First, we categorized comments into clusters and calculated cluster density using complex network analysis methods. Second, we employed computer-mediated content analysis to derive sentiment values in clusters. To capture the emotional atmosphere in short videos accurately, we developed a trust model to assign weights to both cluster density and sentiment value. Our findings reveal that the emotional atmosphere in short video comments indicates continuity and clustering traits. Furthermore, a correlation was found between cluster size and emotional valence, with the relationship trend varying between macro- and micro-clusters. Regarding theoretical contributions, this study enriches the theory of emotional atmosphere by proposing a mathematical model of emotional atmosphere that combines machine learning and complex network analysis, and introduces cluster density and emotional value calculation methods. Concurrently, this study highlights emotion’s importance in short video comments, which can enhance disaster response efforts in the future.

‘I can’t remember how many I killed’. . .: Child soldiers and memory work in YouTube

The way conflicts are remembered is crucial for the prevention of future violence, and digital technologies play increasing roles in processes of remembering. This article looks at memory work conducted in a YouTube video featuring two former child soldiers in Maluku, Eastern Indonesia, and their story from mutual hatred and war to friendship and peace. Analysing and comparing the video and the related English and Indonesian video commentaries, this article asks how the Moluccan violence is remembered, how that memory travels and how it is translated and received among different audiences. It investigates how connectivity and creativity open up new memory spaces and how, within these digital spaces, transcultural memory tropes and political and cultural contexts of social media users can both solidify hegemonic memory narratives and transform traumatic memories into hope and peace.

Shared learning experiences: Pilot study of an online exchange project between pharmacy students in South Africa and the United States

IntroductionInternationalisation enhances students' understanding of social, cultural, and ethical differences, preparing them to be global-minded, socially accountable healthcare practitioners. Traditionally, internationalisation of education involves international student travel. Online tools provide opportunities for international, peer-driven learning and collaboration without costly travel. This research described the experiences of pharmacy students from South Africa (SA) and the United States (US) that participated in a virtual peer exchange project during the COVID-19 pandemic. MethodsThe virtual peer exchange project allowed students in SA and the US to establish connections within the global pharmacy community and compare healthcare, pharmacy education, and pharmacy practice between the countries. Students engaged in facilitated dialogue through video recordings, video conferencing, and a group discussion board. Student introduction video comments and discussion board posts were thematically analysed. ResultsTwenty-one students participated in the pilot project that met some of the intentions and goals of internationalisation via a virtual platform. Two over-arching themes of Practice of Pharmacy and Pharmacy Education emerged from both the introduction video and discussion board comments. Students described lessons learned about similarities and differences in socioeconomic determinants of health as well as structure, functioning, and financing of the different healthcare systems. ConclusionsThis project was a unique way to conduct exchange programmes via a virtual platform, and bypassed challenges of traditional exchange programmes. Through technology, more students in diverse geographic locations can be exposed to various perspectives and healthcare experiences with international students.

Chinese TikTok (Douyin) challenges and body image concerns: a pilot study

BackgroundSocial media content on Western platforms promoting thinness, or thinspiration, has been found to negatively affect body image perception of users. Less is known about non-Western social media use and its effects on body image concerns. Chinese TikTok, known as Douyin, is a popular short video platform with 600 million daily active users. Recent trends on Douyin encourage users to demonstrate thinness through participation in ‘body challenges’. This paper argues that such content is comparable to thinspiration, however, to date hardly any research has been undertaken on these challenges. Thus, this pilot study aimed to analyse the content of three viral challenges and investigate their impact on Douyin users.MethodsThirty most viewed videos were collected for three challenges (N = 90): the Coin challenge, the A4 Waist challenge, and the Spider leg challenge. Videos were coded for variables relating to thin idealisation, including thin praise, sexualisation and objectification, and analysed through content analytic methods. Video comments (N ≈ 5500) were analysed through thematic analysis, and main themes were identified.ResultsPreliminary findings showed that participants who objectified their bodies to a greater extent expressed more negative body image concerns. In addition, comments on the videos had themes of thin praise, self-comparison, and promotion of dieting behaviours. In particular, videos of the A4 Waist challenge were found to incite more negative self-comparison in viewers.ConclusionPreliminary findings suggest all three challenges promote the thin ideal and encourage body image concerns. Further research about the broader impact of body challenges is needed.