Stories That Big Danmaku Data Can Tell as a New Media

Bai, Qingchun; Hu, Qinmin; Ge, Linhui; He, Liang

doi:10.1109/access.2019.2909054

Cited by 26 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the focus was how users create order or mark their responses in conversations, a distinctive usage pattern emerged and is discussed separately in the section ‘second-person references’. The illustrations of danmu data (Figures 3–8) were inspired by Bai et al (2019: 535510). By juxtaposing two consecutive screenshots and reproducing the translated danmu correspondingly, we managed to consider not only the verbal content, but also time, position or direction that influence the meaning making of each comment.…”

Section: Methodsmentioning

confidence: 99%

Making sense of danmu: Coherence in massive anonymous chats on Bilibili.com

Zhang

Cassany

2020

Discourse Studies

View full text Add to dashboard Cite

Although coherence has been widely studied in computer-mediated communication (CMC), insufficient attention has been paid to emergent multimodal forms. This study analyzes a popular commentary system on Chinese and Japanese video-sharing sites – known as danmu or danmaku – where anonymous comments are superimposed on and scroll across the video frame. Through content and multimodal discourse analysis, we unpack danmu-mediated communication analyzing the newest interface (on Bilibili.com), the comments, the interpersonal interactions and the unusual use of the second-person pronoun. Results show that despite the technological constraints (hidden authorship, unmarked sending date and lack of options to structure comments), users construct order in interactions through repetition, danmu-specific expressions and multimodal references, while using playful language to make fun. This study provides an up-to-date analysis on an increasingly popular CMC medium beyond well-studied social networking sites, and broadens the understanding of coherence in contemporary CMC.

show abstract

Section: Methodsmentioning

confidence: 99%

Making sense of danmu: Coherence in massive anonymous chats on Bilibili.com

Zhang

Cassany

2020

Discourse Studies

View full text Add to dashboard Cite

show abstract

“…Previous research on virality has primarily been drawn from five different fields: psychology (e.g., [9]; [11]; [12]; [13]; [4]), computer science (e.g., [14]; [15]; [16]; [1]; [10]; [17]; [18]; [19]; [20]; [21]; [22]), political science (e.g., [2]; [23]; [24]), marketing (e.g., [25]; [26]; [27]; [28]; [29]; [30]; [31]; [7]; [32]; [33]), and health (e.g., [34]). These studies have been mainly conducted in Western countries, such as the United States, Canada, Germany, Italy, and Australia.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Intelligent Parallel Mixed Method Approach for Characterising Viral YouTube Videos in Saudi Arabia

Alshanqiti¹,

Bajnaid²,

Rehman³

et al. 2020

IJACSA

View full text Add to dashboard Cite

In social networking platforms, comprehending virality, exemplified by YouTube, is of great importance, which helps in understanding what characteristics utilised to create content along with what dynamics involved in contributing to YouTube's strength as a platform for sharing content. The current literature surrounding virality problem appears sparse concerning development theories, investigations regarding empirical facts, and an understanding of what makes videos go viral. The overarching objective is to understand deeply the phenomena of viral YouTube videos in Saudi Arabia, hence we propose an intelligent convergent parallel mixed-methods approach that begins, as an internal step, by a qualitative thematic analyses method and an NLP-based quantitative method independently, followed by training an unsupervised clustering model for integrating the internal analysis outputs for deeper insights. We have empirically analysed some trended YouTube videos along with their contents, for studying such phenomena. One of our main findings revealed that boosting entertainments, traditions, politics, and/or religion issues when making a video, that is associated in somehow with sarcastic or rude remarks, is likely the preeminent impulse for letting a regular video go viral.

show abstract

“…In recent years, danmaku culture has developed rapidly. As a new type of information carrier, danmaku has become an indispensable part of people's daily life, communication, and entertainment [1] [2]. More and more people use danmaku to communicate with others on the Internet.…”

Section: Introductionmentioning

confidence: 99%

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

et al. 2020

View full text Add to dashboard Cite

With the rapid development of social media, danmaku video provides a platform for users to communicate online. To some extent, danmaku video provides emotional timing information and an innovative method to analyze video data. In the age of big data, studying the characteristics of danmaku and its emotional tendencies can not only help us understand the psychological characteristics of users but also feedback the effective information of users to video platforms, which can help the platforms optimize related short video recommendations so that it can provide a more accurate solution for the selection of audiences during video production. However, danmaku is different from traditional comments. Current emotion classification methods are only suitable for two-dimensional classification which are not suitable for danmaku emotion analysis. Aiming at the problems such as the colloquialism, diversity, spelling errors, structural non-linearity informal language on the Internet, diversity of social topics, and context dependency of emotion analysis of the danmaku data, this paper proposes an improved emotion analysis model based on Bi-LSTM model to classify the further four-dimensional emotions of Pleasure, Anger, Sorrow and Joy. Furthermore, we add tags such as comment time and user name to the danmaku information. Experimental results show that the improved model has higher Accuracy, Recall, Precision, and F1-Score under the same conditions compared with the CNN and SVM. The classification effect of improved model is close to the SOTA. Experimental results also show that the improved model can be effectively applied to the analysis of irregular danmaku emotion.

show abstract

Stories That Big Danmaku Data Can Tell as a New Media

Cited by 26 publications

References 27 publications

Making sense of danmu: Coherence in massive anonymous chats on Bilibili.com

Making sense of danmu: Coherence in massive anonymous chats on Bilibili.com

Intelligent Parallel Mixed Method Approach for Characterising Viral YouTube Videos in Saudi Arabia

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Contact Info

Product

Resources

About