# A Social Network Analysis on the Damaku of English-Learning Programs
## 1 Introduction
They found that merely increasing the volume of comments failed to facilitate deep learning. Similarly, [PERSON] et al. (2022) [8] observed that dannaku did not meet learners' expectations when the interaction between learners and teachers remained one-sided, with no feedback from the instructors. In Table 1, we show a summary of prior studies. Previous studies on dannaku have primarily focused on aspects such as the timing of messages, textual content, and emotional expressions, with an emphasis on data mining and text analysis. Most research has concentrated on dannaku in different types of open courses, often using individual videos as the primary research subjects, with limited \begin{table}
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**Authors and Year** & **Domain and Platform** & **Investigation** & **Main Technology** \\ \hline [PERSON] (2020) [5] & General, Bilibili & The influence of dannaku videos on learners’ social interaction and their role in increasing motivation and engagement & The two-pronged model combining a semiotic resource perspective and a social practice perspective \\ \hline [PERSON] \& [PERSON] (2021) [7] & Science, TED-Ed science videos & The impact of dannaku in learning: the correlation between the number of dannaku comments which subjects leave and the test of comprehension \\ \hline [PERSON] et al. (2022) [8] & General, Video lectures & Identifying the interaction mode that dannaku meets & Semi-structured interviews \\ \hline [PERSON] et al. (2022) [1] & English, MOOCs and Bilibili & Comparing the learning experiences provided by MOOCs and Bilibili & Interviews and statistics \\ \hline [PERSON] et al. (2024) [6] & General, Bilibili & To understand students’ learning patterns and present corresponding intervention strategies for different types of students & TextMind \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The summary of prior studies on dannaku. exploration of systematic learning collections. Moreover, studies specifically targeting systematic English teaching through danmaku remain scarce. To address this gap, the present study focuses on a systematic collection of English learning materials, using learners' danmaku data as the primary research subject. In this study, we propose a social network analysis (SNA) approach to visualize the danmaku for better understanding and revealing the interaction. This study diverges from [6] by employing SNA to highlight the relevance of danmaku to various topics in English language instruction. Using Python-based web scraping to collect danmaku data, we aim to examine whether the interactions are related to specific subcategories of English instruction, such as phonetics, vocabulary, and grammar in professional contexts, as well as listening, speaking, reading, and writing in skills-based contexts. This approach seeks to determine whether danmaku is aligned with the two overarching concepts of English teaching--linguistic knowledge and language skills. Moreover, this research intends to explore whether danmaku facilitates a better learning environment through cumulative interactions or, conversely, whether an overload of information leads to distractions. We hypothesize that while the danmaku mechanism can enhance the interactive experience, excessive engagement might hinder effective cognitive processing, impeding the deep learning emphasized in educational frameworks. In support of this, [PERSON] et al. (2022) [8] noted that 40.9% of the interactions in English videos were related to supplemental knowledge and answering queries, highlighting the potential for meaningful engagement. This study seeks to uncover whether similar patterns emerge in our analysis and whether danmaku can indeed provide a conducive environment for deep learning. The other parts of this paper are composed as follows. In the Related Work Section, we present the use of SNA alongside related technologies. The Methods Section details our research methodology.