<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.0 20040830//EN" "journalpublishing.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="2.0" xml:lang="en" article-type="research-article"><front><journal-meta><journal-id journal-id-type="nlm-ta">JMIR Form Res</journal-id><journal-id journal-id-type="publisher-id">formative</journal-id><journal-id journal-id-type="index">27</journal-id><journal-title>JMIR Formative Research</journal-title><abbrev-journal-title>JMIR Form Res</abbrev-journal-title><issn pub-type="epub">2561-326X</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">v10i1e81861</article-id><article-id pub-id-type="doi">10.2196/81861</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Peaceful Death in Japanese YouTube Videos: Content and Network Analysis</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name name-style="western"><surname>Vargas Meza</surname><given-names>Xanat</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Oikawa</surname><given-names>Masanori</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff2">2</xref></contrib></contrib-group><aff id="aff1"><institution>Institute for the Advanced Study of Human Biology, Kyoto University</institution><addr-line>Yoshida-Konoe-cho, Sakyo-ku</addr-line><addr-line>Kyoto</addr-line><country>Japan</country></aff><aff id="aff2"><institution>Department of Medical Ethics, Tohoku University Graduate School of Medicine</institution><addr-line>Sendai</addr-line><country>Japan</country></aff><contrib-group><contrib contrib-type="editor"><name name-style="western"><surname>Schwartz</surname><given-names>Amy</given-names></name></contrib><contrib contrib-type="editor"><name name-style="western"><surname>Balcarras</surname><given-names>Matthew</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type="reviewer"><name name-style="western"><surname>Jim&#x00E9;nez-Alonso</surname><given-names>Bel&#x00E9;n</given-names></name></contrib></contrib-group><author-notes><corresp>Correspondence to Xanat Vargas Meza, PhD, Institute for the Advanced Study of Human Biology, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan, 81 08091707764; <email>kt_designbox@yahoo.com</email></corresp></author-notes><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>13</day><month>3</month><year>2026</year></pub-date><volume>10</volume><elocation-id>e81861</elocation-id><history><date date-type="received"><day>06</day><month>08</month><year>2025</year></date><date date-type="rev-recd"><day>06</day><month>02</month><year>2026</year></date><date date-type="accepted"><day>06</day><month>02</month><year>2026</year></date></history><copyright-statement>&#x00A9; Xanat Vargas Meza, Masanori Oikawa. Originally published in JMIR Formative Research (<ext-link ext-link-type="uri" xlink:href="https://formative.jmir.org">https://formative.jmir.org</ext-link>), 13.3.2026. </copyright-statement><copyright-year>2026</copyright-year><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type="uri" xlink:href="https://formative.jmir.org">https://formative.jmir.org</ext-link>, as well as this copyright and license information must be included.</p></license><self-uri xlink:type="simple" xlink:href="https://formative.jmir.org/2026/1/e81861"/><abstract><sec><title>Background</title><p>Death is a difficult topic to discuss for many. Notwithstanding, there is much to learn regarding the contemporary Japanese people&#x2019;s views on a good (peaceful) death. Particularly, shifts in public perceptions of death following the beginning of the COVID-19 pandemic should be considered by health care staff who deliver end-of-life care.</p></sec><sec><title>Objective</title><p>This study examined the recent representations of peaceful death in Japanese YouTube videos to understand related narratives and family structures.</p></sec><sec sec-type="methods"><title>Methods</title><p>We examined 457 YouTube videos about peaceful death using content and family network analyses. The videos were classified into 3 groups: ordinary people, celebrities, and fictional characters.</p></sec><sec sec-type="results"><title>Results</title><p>We identified emerging medical actors who discussed end-of-life with the public. Death portrayed or discussed in the videos mostly involved adults aged 18 to 59 years (179/501, 36% ages), and the most common narrative was illness (101/839, 12% narratives). Although the most common religious stance was nonreligious (304/532, 57.14% religious stances), videos of regular people tended to mention or show a religious practice or symbol (154/272, 56.61% religious stances). There were significant differences (<italic>F</italic><sub>2, 454</sub>=22.81, <italic>P</italic>&#x003C;.001) in terms of gender between celebrity and regular videos (<italic>P</italic>&#x003C;.001, 95% CI 0.23-0.49), celebrity and fiction videos (<italic>P</italic>=.005, 95% CI 0.05-0.34), and fiction and regular videos (<italic>P</italic>=.008, 95% CI 0.03-0.29). This suggests a bias towards male representations of death among celebrities and fictional characters. Male family members were also overrepresented in videos about celebrities and fictional characters as per the visualization of family networks. The networks also suggested that common people&#x2019;s families were more complex than celebrity and fictional people&#x2019;s families. Furthermore, in common people&#x2019;s families, women and men were equally important when reaching out to other family members quickly and in terms of trust.</p></sec><sec sec-type="conclusions"><title>Conclusions</title><p>The current image of Japan as a nonreligious nation, and the gender bias in Japanese social media should be challenged. Furthermore, health care and research protocols related to end-of-life care for patients beyond older adults should be developed.</p></sec></abstract><kwd-group><kwd>end-of-life</kwd><kwd>family networks</kwd><kwd>Japan</kwd><kwd>social media</kwd><kwd>YouTube</kwd></kwd-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><sec id="s1-1"><title>Background</title><p>Many find death to be a difficult topic to discuss in contemporary times, and our treatment of death impacts how we approach disability and sickness. Death may have several causes, and people adopt various attitudes according to previously agreed-upon values surrounding death. In general, Japanese people avoid talking about death. In Japan, since most people die in hospitals [<xref ref-type="bibr" rid="ref1">1</xref>], a &#x201C;good death&#x201D; in medical contexts involves a grasp on diagnoses, treatment options, and whether patients and relatives decide the place to be treated or laid to rest [<xref ref-type="bibr" rid="ref2">2</xref>]. This concept has been developed through interactions between medical staff, patients, and their kin and is linked to cultural and spiritual values [<xref ref-type="bibr" rid="ref3">3</xref>,<xref ref-type="bibr" rid="ref4">4</xref>]. However, not much is known about &#x201C;good death&#x201D; in other contemporary Japanese contexts.</p><p>In a previous study, we examined the concept of good death as a &#x201C;peaceful death&#x201D; on Japanese Twitter (subsequently rebranded X; Meza Xanat and Masanori, unpublished data). We found it a useful means to uncover conversations about difficult topics, as social media functions with a layer of anonymity that allows more free, open, and spontaneous discussions than conventional surveys and interviews [<xref ref-type="bibr" rid="ref5">5</xref>]. As Japanese Twitter users talking about death tend to be young, we turned our attention to YouTube to complement previous findings. Launched in 2005, this video social media platform is the second most visited website after Google, with broader advertising revenue for male content creators (51.8% vs 48.2%) and broader advertising reach for male viewers (76.4% vs 66.1%, overlapped data) [<xref ref-type="bibr" rid="ref6">6</xref>]. Additionally, it appears to appeal to a wide age range of Japanese users [<xref ref-type="bibr" rid="ref7">7</xref>].</p><p>Therefore, our objective in this study was to examine public communication and perceptions of &#x201C;good death&#x201D; on Japanese YouTube. We reflected on what is considered a good death and end-of-life preparations.</p></sec><sec id="s1-2"><title>Narratives of Death in Japan</title><p>Less educated people, Soka Gakkai Buddhists, and women have a greater tendency to practice religion and believe in the afterlife than others [<xref ref-type="bibr" rid="ref8">8</xref>]. In Yamaguchi in Honshu (the largest island of Japan), 49% of the rural people interviewed had no image associated with the afterlife, recognized a continuity between life and death, and confronted it in a collective manner [<xref ref-type="bibr" rid="ref9">9</xref>]. Meanwhile, a study found that the public does not know about the afterlife, and medical staff mostly do not believe in it; the colors associated with death were gray, white, and black; and this signaled a need for education on death [<xref ref-type="bibr" rid="ref10">10</xref>].</p><p>Long [<xref ref-type="bibr" rid="ref3">3</xref>,<xref ref-type="bibr" rid="ref11">11</xref>] conducted interviews on the Japanese perception of death in hospitals and hospices, including religious themes. Patients&#x2019; experience of dying, autonomy, social context, control over the dying process, and quality of end-of-life care were relevant for a good death [<xref ref-type="bibr" rid="ref4">4</xref>]. A study on patients with cancer reported their desire to fight the disease, retain hope, unawareness of death, and prioritizing good relationships with family; whereas those who did not want to fight cancer prioritized physical and mental comfort during end-of-life care [<xref ref-type="bibr" rid="ref12">12</xref>]. Morooka [<xref ref-type="bibr" rid="ref13">13</xref>] found that the families of those who died at home focused on daily life and dying without causing care or financial burdens. Finally, Gould [<xref ref-type="bibr" rid="ref14">14</xref>] noted that end-of-life products and services by Buddhism-related actors in Japan cater to lonely, stressed families through healing (<italic>iyashi</italic>) and a calm heart.</p><p>Regarding social media, Sueki [<xref ref-type="bibr" rid="ref15">15</xref>] correlated clear and specific statements of wanting to commit suicide on Twitter with suicidal behavior, but not general conversations or mentions of death. Social learning theory argues that media consumption reinforces notions of what is acceptable, including suicide [<xref ref-type="bibr" rid="ref16">16</xref>]. This theory has also been applied to celebrity studies. Katsumata [<xref ref-type="bibr" rid="ref17">17</xref>] proposes that deaths by accident, epidemics, or environmental disasters on social media should convey the gravity of lives lost and the associated grief, with previous consent of the bereaved kin.</p></sec><sec id="s1-3"><title>Death in Video-Based Social Media Platforms</title><p>Technological tools such as photography and films are not neutral. As much as they capture and recreate beauty, they can also be used to export ideas of colonialism, empire, and White supremacy, objectifying communities and societies in the context of death [<xref ref-type="bibr" rid="ref18">18</xref>]. Varied intentions in capturing or manipulating records are not new features of video shared on social media. Furthermore, the scale and potential reach of such materials have increased due to the high rate of internet service adoptions.</p><p>Some of the earliest examinations of death on YouTube were tied to crime [<xref ref-type="bibr" rid="ref19">19</xref>,<xref ref-type="bibr" rid="ref20">20</xref>], which highlights the sensationalization of tragedies. Celebrity deaths were also reported [<xref ref-type="bibr" rid="ref21">21</xref>,<xref ref-type="bibr" rid="ref22">22</xref>]. Furthermore, 377 YouTube videos showcased framing by organ procurement organizations [<xref ref-type="bibr" rid="ref23">23</xref>]. The music in videos featuring mourning on YouTube elicited empathy and sadness [<xref ref-type="bibr" rid="ref24">24</xref>], even when in the background [<xref ref-type="bibr" rid="ref25">25</xref>].</p><p>Ethnography of a multilingual YouTube video for children regarding death and grief noted that the decade-old video went viral across Spanish-speaking countries at the beginning of the COVID-19 pandemic, reaching all genders and ages, and this highlighted the gravity of sudden death in a manner that official information sources did not [<xref ref-type="bibr" rid="ref26">26</xref>]. Content analysis of 100 videos on grief on TikTok showed that users grieved openly and emotionally [<xref ref-type="bibr" rid="ref27">27</xref>]. Interviews with grievers during the COVID-19 pandemic, including 3 YouTube users, showed that they announced the loss, shared artistic and farewell posts, and felt that this helped them maintain a link with the deceased; however, in cases of prolonged grieving, other social media users perceived them to be insincere [<xref ref-type="bibr" rid="ref28">28</xref>].</p><p>Regarding Japan, an analysis of 27 English YouTube videos about the Minamata disease noted that most uploaders were ordinary people using documentary clips, lacking context and explanations [<xref ref-type="bibr" rid="ref29">29</xref>]. Uriu et al [<xref ref-type="bibr" rid="ref30">30</xref>] published a case study of a funeral live cast on YouTube, making it more accessible to older people kin who lived far away; however, they argued that the process could be enriched further through other data formats, and there was a need to consider religious differences. A comparison of the mourning of Asian virtual YouTube uploaders with human and celebrity uploaders revealed that young viewers treated humans more like machines [<xref ref-type="bibr" rid="ref31">31</xref>]. Finally, a study of suicidal ideation in 28 Twitter and YouTube vlogs by women revealed empathic messages, and that the belief that the ability to mitigate suicidal ideation lies in the individual has not changed in the last decade [<xref ref-type="bibr" rid="ref32">32</xref>].</p><p>We found that studies analyze anything from 1 to over a 1000 videos, there is little research on Japanese video-based social media and death, and studies on religious narratives outside of Buddhism are lacking. Therefore, we formulated the following research questions (RQs) in the context of Japanese YouTube videos:</p><list list-type="order"><list-item><p>RQ1: Who are involved in creating and appearing in death-related content?</p></list-item><list-item><p>RQ2: What are the most common narratives regarding death?</p></list-item><list-item><p>RQ3: Which religions are associated with death?</p></list-item></list></sec><sec id="s1-4"><title>Family Networks</title><p>A social network is a set of nodes representing actors (eg, persons) and their ties (relationships). Similarly, a family network is a representation of family members and their relationships. Given that the study of death in social media has not focused on end-of-life care, and that kin are crucial in making decisions in this stage in Japan, we reviewed the literature on family networks.</p><p>Social network methods broaden the definition of family by starting with individuals&#x2019; definition of their family context, and key applications include national representative survey data, family networks across time, and family health networks [<xref ref-type="bibr" rid="ref33">33</xref>]. Recent research on family networks at the end of life has been conducted primarily in Australia. A survey among 7915 individuals over a period of 7 years indicated that care providers were of all adult ages, more than half were extended family and friends, and dying at home was common in these contexts [<xref ref-type="bibr" rid="ref34">34</xref>]. Examining care networks across time in 9 focus groups of people dying at home revealed that these networks grow and relationships strengthen [<xref ref-type="bibr" rid="ref35">35</xref>]. A similar study integrating surveys revealed that relationships with medical care staff were peripheral and those between staff were weak [<xref ref-type="bibr" rid="ref36">36</xref>]. Finally, network data from an American survey conducted over a 5-year period clarified that older adults tended to substitute dead confidants more frequently than younger adults [<xref ref-type="bibr" rid="ref37">37</xref>].</p><p>In Japan, a survey of 129 families in Tokyo identified 11 family structures and the important role of matrilineal and nonkin ties [<xref ref-type="bibr" rid="ref38">38</xref>]. Maeda and Meguro [<xref ref-type="bibr" rid="ref39">39</xref>] examined the strength of urban family ties, noting the influence of class. A comparison between the family networks of older men in urban and rural settings over a 7-year period revealed that mortality increased when rural men were less educated or unmarried; and this also applied to urban men who did not participate in community groups [<xref ref-type="bibr" rid="ref40">40</xref>]. Ishiguro [<xref ref-type="bibr" rid="ref41">41</xref>] noted that the size of kin and friendship networks slightly decreased among women, and for men, networks comprising reliable friends reduced slightly between the 1990s and 2010s. In addition, a survey among 355 older patients in Fukushima showed that those with small social networks discussed end-of-life plans less, regardless of marital status [<xref ref-type="bibr" rid="ref42">42</xref>].</p><p>Although we identified literature on networks in Japan, family networks during the COVID-19 pandemic remained unexplored. Therefore, we added the following RQs for the portrayal of death in Japanese YouTube videos:</p><list list-type="order"><list-item><p>RQ4: Which kin were involved in death-related content?</p></list-item><list-item><p>RQ5: What structures do family networks display during the end-of-life period?</p></list-item></list></sec></sec><sec id="s2" sec-type="methods"><title>Methods</title><sec id="s2-1"><title>Data Collection and Organization</title><p>We tested several keywords based on narratives about &#x201C;good death&#x201D; in Japan [<xref ref-type="bibr" rid="ref3">3</xref>] in Google trends and YouTube. We selected &#x2018;&#x5B89;&#x3089;&#x304B;&#x306A;&#x6B7B;&#x2019; (peaceful death) as a proxy for good death in medical and sociocultural contexts because of the high number of hits. A researcher extracted video data using YouTube Data Tools [<xref ref-type="bibr" rid="ref43">43</xref>], in 2 modules that use the YouTube Application Programming Interface. The Video List module retrieves a list of videos and related information (identifier, publication date, title, description, tags, category, duration, number of views, and number of comments). The video titles were reviewed and the videos were watched to identify those related to death. This resulted in 457 out of 2074 videos, published from January 2019 to May 2023. Subsequently, a researcher used identifiers (the last part of the video hyperlink) obtained through the Video List module to extract comments using the Video Comments module.</p></sec><sec id="s2-2"><title>Content Analysis</title><p>The content analysis was divided into 3 parts: human notation of video content, automatic summarization of video descriptions, and automatic summarization of video comments.</p><sec id="s2-2-1"><title>Human Notation of Video Content</title><p>The 457 videos were watched and annotated, based on previous studies [<xref ref-type="bibr" rid="ref44">44</xref>,<xref ref-type="bibr" rid="ref45">45</xref>] as follows:</p><list list-type="bullet"><list-item><p>To answer RQ1, a researcher identified the gender of the deceased and sorted them according to age: child (0-17 y), adult (18-59 y), and older adult (&#x2265;60 y). The &#x201C;About&#x201D; page of the YouTube channel was consulted to identify uploaders, and speakers were considered individuals who appeared, spoke, or whose words were conveyed in the video. These were then categorized (<xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>).</p></list-item><list-item><p>To answer RQ2, a researcher used the location information on the channel&#x2019;s &#x201C;About&#x201D; page and from within the YouTube video. This information was categorized according to countries and Japanese regions. Furthermore, death and end-of-life narratives were classified based on prior studies (<xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>) [<xref ref-type="bibr" rid="ref7">7</xref>,<xref ref-type="bibr" rid="ref9">9</xref>,<xref ref-type="bibr" rid="ref10">10</xref>,<xref ref-type="bibr" rid="ref45">45</xref>-<xref ref-type="bibr" rid="ref47">47</xref>].</p></list-item><list-item><p>To answer RQ3, we considered religion as supernatural beliefs about death and the afterlife, and activities surrounding death. Therefore, the religions mentioned, religious buildings, religious imagery, and rites displayed in the videos were annotated.</p></list-item><list-item><p>To answer RQ4 and RQ5, kin was annotated. A sample of videos with kin information (73/164, 44.51%) was verified by a second researcher. Their agreement on family members ranged between 0.83 and 1 (<xref ref-type="supplementary-material" rid="app3">Multimedia Appendix 3</xref>), and the corrected notations were incorporated into the data.</p></list-item></list></sec><sec id="s2-2-2"><title>Automatic Summarization of Video Descriptions</title><p>To answer RQ1 and RQ2, we used KH Coder (Ritsumeikan University) [<xref ref-type="bibr" rid="ref48">48</xref>] to summarize frequently appearing terms in Japanese and their relationships with other terms, generating co-occurrence&#x2013;based networks. The networks were drawn as undirected and unipartite, and overlap coefficients were calculated in KH Coder. In the overlap coefficient between 2 groups of words, when group X was a subgroup of Y, the overlap coefficient was equal to 1 [<xref ref-type="bibr" rid="ref49">49</xref>]. We used this coefficient for its capability to generate unified networks wherein word groups are still distinct. As recommended by Higuchi [<xref ref-type="bibr" rid="ref50">50</xref>], the 60 strongest co-occurrences were drawn as network edges.</p></sec><sec id="s2-2-3"><title>Automatic Summarization of Video Comments</title><p>To complement the response to RQ2, Kh Coder was used to summarize the most frequent words in the video comments.</p></sec></sec><sec id="s2-3"><title>Statistical Analysis</title><p>To address RQ1-RQ4, the videos were divided into 3 groups: death of a (real) regular person, death of a celebrity, and fictional death. Each parametric numerical variable that had a median over zero (speakers, religion, and deceased disclosed gender) was analyzed through a one-way ANOVA. We considered a threshold of <italic>P</italic>&#x003C;.01 as significant. All statistical tests were run in IBM SPSS (version 29.0.10).</p></sec><sec id="s2-4"><title>Network Analysis</title><p>Network analysis involves quantitative and qualitative tools. To answer RQ 5, we focused on measurements at the node and network level based on prior studies [<xref ref-type="bibr" rid="ref51">51</xref>-<xref ref-type="bibr" rid="ref54">54</xref>]. While many social network studies focus on trust, Yamagishi [<xref ref-type="bibr" rid="ref55">55</xref>] argues that Japanese people seek assurance when creating stable relationships. Thus, we considered trust as equivalent to assurance in our study. The network measurements (<xref ref-type="supplementary-material" rid="app4">Multimedia Appendix 4</xref>) were calculated using Gephi version 0.10.1 [<xref ref-type="bibr" rid="ref56">56</xref>,<xref ref-type="bibr" rid="ref57">57</xref>]. Gephi was also used to draw unipartite undirected networks laid with the Fruchterman-Reingold algorithm of family member relationships found among the regular people, celebrities, and fictional characters.</p></sec><sec id="s2-5"><title>Ethical Considerations</title><p>This study was exempt from ethical review by the Medical Board of Kyoto University because it involved big data analysis of social media records and did not use human data beyond measuring internet activity. This study follows the Ethical Guidelines for Internet Research [<xref ref-type="bibr" rid="ref58">58</xref>] and the Ethical Guidelines for Medical and Biological Research Involving Human Subjects [<xref ref-type="bibr" rid="ref59">59</xref>]. Based on the above, it was determined that the study did not require informed consent from participants. The findings pertaining to nonpublic persons, including those related to medical conditions, were reported in a way that masks identities.</p></sec></sec><sec id="s3" sec-type="results"><title>Results</title><sec id="s3-1"><title>Actors in Peaceful Death</title><p><xref ref-type="table" rid="table1">Table 1</xref> displays the overlapping 477 uploaders found on the 457 YouTube videos, divided in videos of regular people, celebrities, and fictional characters. No significant differences were found between video groups. Among the 477 uploaders, 208 (43.60%) were citizens, 65 (13.62%) the media, and 44 (9.22%) religious actors.</p><table-wrap id="t1" position="float"><label>Table 1.</label><caption><p>YouTube uploaders in 457 videos.</p></caption><table id="table1" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Actor</td><td align="left" valign="bottom">Regular videos<break/>(n=220), n (%)</td><td align="left" valign="bottom">Celebrity videos<break/>(n=122), n (%)</td><td align="left" valign="bottom">Fictional videos<break/>(n=115), n (%)</td><td align="left" valign="bottom">Total videos<break/>(N=457), n (%)</td></tr></thead><tbody><tr><td align="left" valign="top">Doctor</td><td align="left" valign="top">22 (10)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">22 (5)</td></tr><tr><td align="left" valign="top">Medical student</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td></tr><tr><td align="left" valign="top">Nurse</td><td align="left" valign="top">1 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">1 (0)</td></tr><tr><td align="left" valign="top">Other medical staff</td><td align="left" valign="top">5 (2)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">5 (1)</td></tr><tr><td align="left" valign="top">Donor</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td></tr><tr><td align="left" valign="top">Recipient</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td></tr><tr><td align="left" valign="top">Donor relative</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td></tr><tr><td align="left" valign="top">Recipient relative</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td></tr><tr><td align="left" valign="top">Other deceased</td><td align="left" valign="top">1 (0)</td><td align="left" valign="top">1 (1)</td><td align="left" valign="top">1 (1)</td><td align="left" valign="top">3 (1)</td></tr><tr><td align="left" valign="top">Other deceased relative</td><td align="left" valign="top">4 (2)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">4 (1)</td></tr><tr><td align="left" valign="top">Association</td><td align="left" valign="top">9 (4)</td><td align="left" valign="top">1 (1)</td><td align="left" valign="top">1 (1)</td><td align="left" valign="top">11 (2)</td></tr><tr><td align="left" valign="top">Citizen</td><td align="left" valign="top">91 (41)</td><td align="left" valign="top">50 (41)</td><td align="left" valign="top">67 (58)</td><td align="left" valign="top">208 (46)</td></tr><tr><td align="left" valign="top">Government</td><td align="left" valign="top">2 (1)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">2 (0)</td></tr><tr><td align="left" valign="top">Media</td><td align="left" valign="top">26 (12)</td><td align="left" valign="top">31 (25)</td><td align="left" valign="top">8 (7)</td><td align="left" valign="top">65 (14)</td></tr><tr><td align="left" valign="top">Religious actor</td><td align="left" valign="top">36 (16)</td><td align="left" valign="top">4 (3)</td><td align="left" valign="top">4 (3)</td><td align="left" valign="top">44 (10)</td></tr><tr><td align="left" valign="top">Educational actor</td><td align="left" valign="top">5 (2)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">5 (1)</td></tr><tr><td align="left" valign="top">Unknown</td><td align="left" valign="top">34 (15)</td><td align="left" valign="top">36 (30)</td><td align="left" valign="top">37 (32)</td><td align="left" valign="top">107 (23)</td></tr><tr><td align="left" valign="top">Total overlapping actors</td><td align="left" valign="top">236 (107)</td><td align="left" valign="top">123 (101)</td><td align="left" valign="top">118 (103)</td><td align="left" valign="top">477 (104)</td></tr></tbody></table></table-wrap><p>Regarding the 1303 overlapping speakers (<xref ref-type="table" rid="table2">Table 2</xref>), 355 (27.24%) were nonorgan donor deceased, 329 (25.24%) were citizens, and 187 (14.35%) were the kin of nonorgan donor deceased. There were significant differences (<italic>F</italic><sub>2, 454</sub>=5.72; <italic>P</italic>=.001) in terms of nondonor deceased between the videos on regular people and celebrities (<italic>P</italic>=.02, 95% CI 0.02-0.24), and those of regular people and fictional characters (<italic>P</italic>=.02, 95% CI 0.02-0.25). There was also a difference (<italic>F</italic><sub>2, 454</sub>=8.75; <italic>P</italic>&#x003C;.001) between videos of regular people and fictional characters in terms of citizens (<italic>P&#x003C;</italic>.001, 95% CI 0.09-0.33). This implies that nondonor deceased and citizens are less present in regular people death&#x2019;s videos.</p><table-wrap id="t2" position="float"><label>Table 2.</label><caption><p>Speakers in 457 YouTube videos.</p></caption><table id="table2" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Actor</td><td align="left" valign="bottom">Regular videos (n=220), n (%)mean, standard deviation</td><td align="left" valign="bottom">Celebrity videos (n=122), n (%) mean, standard deviation</td><td align="left" valign="bottom">Fictional videos (n=115), n (%) mean, standard deviation</td><td align="left" valign="bottom">Total videos (N=457), n (%) mean, standard deviation</td></tr></thead><tbody><tr><td align="left" valign="top">Doctor</td><td align="left" valign="top">62 (28) 0,28, 0.45</td><td align="left" valign="top">8 (6) 0.06, 0.24</td><td align="left" valign="top">13 (11) 8.85, 0.31</td><td align="left" valign="top">83 (18) 0.18, 0.38</td></tr><tr><td align="left" valign="top">Medical student</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">0 (0) 0.00, 0.00</td></tr><tr><td align="left" valign="top">Nurse</td><td align="left" valign="top">20 (9) 0.09, 0.28</td><td align="left" valign="top">3 (2) 0.02, 0.15</td><td align="left" valign="top">8 (7) 0.07, 0.25</td><td align="left" valign="top">31 (7) 0.07, 0.25</td></tr><tr><td align="left" valign="top">Other medical staff</td><td align="left" valign="top">15 (7) 0.07, 0.25</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">17 (4) 0.04, 0.18</td></tr><tr><td align="left" valign="top">Donor</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">1 (0) 0.00, 0.04</td></tr><tr><td align="left" valign="top">Recipient</td><td align="left" valign="top">0 (0) 0.00. 0.00</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">1 (0) 0.00, 0.04</td></tr><tr><td align="left" valign="top">Donor relative</td><td align="left" valign="top">0 (0) 0.00. 0.00</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">1 (0) 0.00, 0.04</td></tr><tr><td align="left" valign="top">Recipient relative</td><td align="left" valign="top">0 (0) 0.00. 0.00</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">1 (0) 0.00, 0.04</td></tr><tr><td align="left" valign="top">Other deceased</td><td align="left" valign="top">156 (71) 0.71, 0.45<sup><xref ref-type="table-fn" rid="table2fn1">a</xref></sup></td><td align="left" valign="top">102 (84) 0.84, 0.37</td><td align="left" valign="top">97 (84) 0.84, 0.36<sup><xref ref-type="table-fn" rid="table2fn1">a</xref></sup></td><td align="left" valign="top">355 (78) 0.78, 0.41</td></tr><tr><td align="left" valign="top">Other deceased relative</td><td align="left" valign="top">114 (52) 0.52, 0.50</td><td align="left" valign="top">24 (20) 0.20, 0.39</td><td align="left" valign="top">49 (43) 0.43, 0.49</td><td align="left" valign="top">187 (41) 0.41, 0.49</td></tr><tr><td align="left" valign="top">Association</td><td align="left" valign="top">13 (6) 0.06, 0.23</td><td align="left" valign="top">1 (1), 0.01, 0.09</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">15 (3) 0.03, 0.17</td></tr><tr><td align="left" valign="top">Citizen</td><td align="left" valign="top">141 (64)<sup><xref ref-type="table-fn" rid="table2fn1">a</xref></sup> 0.64, 0.48</td><td align="left" valign="top">90 (74) 0.74, 0.44</td><td align="left" valign="top">98 (85) 0.85, 0.35</td><td align="left" valign="top">329 (72) 0.72, 0.45</td></tr><tr><td align="left" valign="top">Government</td><td align="left" valign="top">47 (21) 0.21, 0.41</td><td align="left" valign="top">16 (13) 0.13, 0.33</td><td align="left" valign="top">41 (36) 0.36, 0.48</td><td align="left" valign="top">104 (23) 0.23, 0.42</td></tr><tr><td align="left" valign="top">Media</td><td align="left" valign="top">27 (12) 0.12, 0.32</td><td align="left" valign="top">37 (30) 0.30, 0.46</td><td align="left" valign="top">3 (3) 0.03, 0.16</td><td align="left" valign="top">67 (15) 0.15, 0.35</td></tr><tr><td align="left" valign="top">Religious actor</td><td align="left" valign="top">50 (23) 0.23, 0.42</td><td align="left" valign="top">9 (7) 0.07, 0.26</td><td align="left" valign="top">11 (10) 0.10, 0.29</td><td align="left" valign="top">70 (15) 0.15, 0.36</td></tr><tr><td align="left" valign="top">Educational actor</td><td align="left" valign="top">26 (12) 0.12, 0.26</td><td align="left" valign="top">6 (5) 0.05, 0.21</td><td align="left" valign="top">7 (6) 0.10, 0.24</td><td align="left" valign="top">39 (9) 0.10, 0.28</td></tr><tr><td align="left" valign="top">Unknown</td><td align="left" valign="top">0 (0) 0.00, 0.29</td><td align="left" valign="top">1 (1) 0.01, 0.24</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">2 (0) 0.00, 0.06</td></tr><tr><td align="left" valign="top">Total overlapping actors</td><td align="left" valign="top">671 (305) 3.05</td><td align="left" valign="top">298 (244) 2.44</td><td align="left" valign="top">334 (290) 3.00</td><td align="left" valign="top">1303 (285) 2.85</td></tr></tbody></table><table-wrap-foot><fn id="table2fn1"><p><sup>a</sup>Statistically significant results.</p></fn></table-wrap-foot></table-wrap><p>The most common age group in 501 overlapped ages in the 457 videos was adults (n=179, 36%), followed by the unknown group (n=158, 31%), older adults (n=121, 24%), and children (n=43, 9%). For regular people, age went mostly unreported (99/256, 39%), whereas for celebrities and fictional characters, most deaths were of adults (52/122, 42% and 59/123, 48% respectively). Regarding gender, <xref ref-type="table" rid="table3">Table 3</xref> shows that in 262 of the 550 (47.63%) overlapped genders identified, the deceased were male. There were significant differences (<italic>F</italic><sub>2, 454</sub>=22.81; <italic>P&#x003C;</italic>.001) in terms of gender between celebrity and regular videos (<italic>P&#x003C;</italic>.001, 95% CI 0.23-0.49), celebrity and fiction videos (<italic>P</italic>=.005, 95% CI 0.05-0.34), and fiction and regular videos (<italic>P</italic>=.008, 95% CI 0.03-0.29). This supports the notion that most male deaths were those of celebrities, followed by fictional characters.</p><table-wrap id="t3" position="float"><label>Table 3.</label><caption><p>Overlapped perceived gender of the deceased.</p></caption><table id="table3" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Perceived gender of the deceased</td><td align="left" valign="bottom">Regular videos<break/>(n=220), n (%) mean, standard deviation</td><td align="left" valign="bottom">Celebrity videos<break/>(n=122), n (%) mean, standard deviation</td><td align="left" valign="bottom">Fictional videos<break/>(n=115), n (%) mean, standard deviation</td><td align="left" valign="bottom">Total videos<break/>(N=457), n (%) mean, standard deviation</td></tr></thead><tbody><tr><td align="left" valign="top">Men</td><td align="left" valign="top">96 (44) 0.44, 0.49<sup><xref ref-type="table-fn" rid="table3fn1">a</xref></sup></td><td align="left" valign="top">97 (80) 0.80, 0.40<sup><xref ref-type="table-fn" rid="table3fn1">a</xref></sup></td><td align="left" valign="top">69 (60) 0.60, 0.49<sup><xref ref-type="table-fn" rid="table3fn1">a</xref></sup></td><td align="left" valign="top">262 (57) 0.57, 0.49</td></tr><tr><td align="left" valign="top">Women</td><td align="left" valign="top">87 (40) 0.40, 0.49</td><td align="left" valign="top">30 (25) 0.25, 0.43</td><td align="left" valign="top">53 (46) 0.46, 0.50</td><td align="left" valign="top">170 (37) 0.37, 0.48</td></tr><tr><td align="left" valign="top">Other or unknown</td><td align="left" valign="top">90 (41) 0.41, 0.49</td><td align="left" valign="top">2 (2) 0.02, 0.12</td><td align="left" valign="top">26 (23) 0.23, 0.42</td><td align="left" valign="top">118 (26) 0.26, 0.43</td></tr><tr><td align="left" valign="top">Total perceived gender of the deceased</td><td align="left" valign="top">273 (124) 1.24</td><td align="left" valign="top">129 (105) 1.05</td><td align="left" valign="top">148 (128) 1.28</td><td align="left" valign="top">550 (120) 1.20</td></tr></tbody></table><table-wrap-foot><fn id="table3fn1"><p><sup>a</sup>Statistically significant results.</p></fn></table-wrap-foot></table-wrap></sec><sec id="s3-2"><title>Narratives of Peaceful Death</title><p>This section presents the contexts of peaceful deaths described in the videos to address RQ2. The most common country identified in overlapping data was Japan (384/592, 64.86%), and a significant difference (<italic>F</italic><sub>2, 454</sub>=6.44; <italic>P</italic>=.002) was found between regular people and celebrity videos (<italic>P</italic>=.001, 95% CI 0.05-0.28). This implies that most of the celebrity deaths highlighted in the Japanese videos occurred abroad.</p><p><xref ref-type="fig" rid="figure1">Figures 1-3</xref> display the communication channels of peaceful death. The node size reflects word frequency, with thicker lines indicating stronger tie strengths between words. Nodes with the same color indicate that they belong to the same group, and nodes with no color are not part of a specific group. <xref ref-type="fig" rid="figure1">Figure 1A</xref> shows descriptions of regular people&#x2019;s deaths, where a purple cluster of words wherein Buddhism is connected to the Amazon store and other YouTube videos. The red cluster refers to descriptions found in videos related to spiritualism and Nnew Rreligion (the term &#x201C;spirit&#x201D;), linked to YouTube video channels. <xref ref-type="fig" rid="figure2">Figure 2A</xref> shows that celebrity deaths were communicated through YouTube, Twitter, Instagram, and the Tokyo Broadcasting System (TBS; in a red cluster). By contrast, <xref ref-type="fig" rid="figure3">Figure 3A</xref> shows only YouTube and Twitter.</p><fig position="float" id="figure1"><label>Figure 1.</label><caption><p>(A) Descriptions (word frequency 50&#x2010;1356; tie strength 0.6&#x2010;1) and (B) comments (word frequency 900&#x2010;6649; tie strength 0.3&#x2010;0.6) of YouTube videos about ordinary people&#x2019;s deaths.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="formative_v10i1e81861_fig01.png"/></fig><fig position="float" id="figure2"><label>Figure 2.</label><caption><p>(<bold>A</bold>) Descriptions (word frequency 34&#x2010;282; tie strength 0.8&#x2010;1) and (<bold>B</bold>) comments (word frequency 600&#x2010;3486; tie strength 0.4&#x2010;0.8) of YouTube videos about celebrities&#x2019; deaths.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="formative_v10i1e81861_fig02.png"/></fig><fig position="float" id="figure3"><label>Figure 3.</label><caption><p>(<bold>A</bold>) Descriptions (word frequency 30&#x2010;577, tie strength 0.6&#x2010;1) and (<bold>B</bold>) comments (word frequency 300&#x2010;2260, tie strength 0.2&#x2010;0.8) of YouTube videos about fictional characters&#x2019; deaths.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="formative_v10i1e81861_fig03.png"/></fig><p>A summary of the descriptions of regular people&#x2019;s videos in <xref ref-type="fig" rid="figure1">Figure 1A</xref> is shown in green, referring to end-of-life care in hospices and academic conferences. Although less central, there seems to be a focus on patients with cancer (orange cluster). Regarding the comments on these videos, <xref ref-type="fig" rid="figure1">Figure 1B</xref> shows a green cluster discussing life, family, and families. The second cluster (in yellow) expresses gratitude to medical experts for their insights, mentioning bereaved parents (&#x201C;father&#x201D; and &#x201C;mother&#x201D;). Peaceful death seems to be connected to medical death and death in hospices. The purple cluster and some loosely connected words include what is considered the opposite of peaceful death, for instance, the death penalty and euthanasia.</p><p>The descriptions of celebrities shown in <xref ref-type="fig" rid="figure2">Figure 2A</xref> centered around 2 men: American actor Luke Perry (purple cluster) and Japanese politician Shinzo Abe (blue cluster). There was a contrast between the formal language used for Abe and the emotional language used for Perry (&#x201C;peaceful&#x201D;). The video comments shown in <xref ref-type="fig" rid="figure2">Figure 2B</xref> were mostly about Abe&#x2019;s death (purple cluster), the official response of the opposition party (blue cluster), and a former political rival (Suga in the purple cluster). There were prayers for &#x51A5;&#x798F; (heavenly bliss, identified 1876 times in the red cluster) for the deaths of rakugo (traditional comedy) performers Shimura and Enraku. This is a common praying behavior in Buddhist funerals. Conversely, the word &#x201C;paradise&#x201D; related to Christian beliefs was found 510 times (not shown in the figure).</p><p>As for fictional deaths, the most central word group of descriptions shown in <xref ref-type="fig" rid="figure3">Figure 3A</xref> is based on a video about a Twitter webtoon called &#x201C;<italic>The Crocodile Who Dies After 100 Days</italic>.&#x201D; This webtoon was tied to New Age beliefs (the word &#x201C;chakra&#x201D;). A cluster in yellow shows the term &#x201C;Metal Gear Solid,&#x201D; an adventure stealth video game franchise connected to a &#x201C;novel.&#x201D; A group in purple shows a character (&#x201C;Walton&#x201D;) from <italic>Frankenstein</italic> by Mary Shelley and other words from videos where people read novels aloud. Regarding the video comments, although fictional deaths had various causes, most focused on suicide (yellow cluster in <xref ref-type="fig" rid="figure3">Figure 3B</xref>). The yellow and purple groups were comments on a webtoon that humanized a fictional former mob man. Satake and Kito (blue cluster) are also characters in a webtoon. Overall, conversations are focused on the opposite of a good death, that is, on violent deaths.</p><p>Regarding the annotated narratives that complement these figures, about a quarter (n=222, 26%) of the overlapping 839 narratives explained death, and 101 (12%) focused on illness (<xref ref-type="supplementary-material" rid="app5">Multimedia Appendix 5</xref>).</p></sec><sec id="s3-3"><title>Religion and Peaceful Death</title><p>In reference to RQ3, <xref ref-type="table" rid="table4">Table 4</xref> suggests that the most identified religion stance in the overlapping data was nonreligious (304/532, 57.14%). There was a significant difference (<italic>F</italic><sub>2, 454</sub>=23.58; <italic>P&#x003C;</italic>.001) in religion between celebrity and regular videos (<italic>P</italic>&#x003C;.001, 95% CI 0.23-0.47). This suggests that religious practices were most prominent in regular people&#x2019;s deaths. <xref ref-type="table" rid="table4">Table 4</xref> notes Buddhism as the most common religion, although a wide variety of religions were found. Other Asian religions included Ainu beliefs, Brahmanism, Confucianism, Feng shui, Hinduism, Onmyodo, Ryukyuan beliefs, Taoism, and beliefs of the Indigenous people in China. Other Christian groups included Evangelists, Protestants, and unspecified Christian beliefs. Finally, other Middle Eastern and Western religions included Greek paganism, Islam, Judaism, and unspecified Abrahamic beliefs.</p><table-wrap id="t4" position="float"><label>Table 4.</label><caption><p>Overlapping religions in 457 videos about peaceful death.</p></caption><table id="table4" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Religion</td><td align="left" valign="bottom">Regular videos<break/>(n=220), n (%) mean, standard deviation</td><td align="left" valign="bottom">Celebrity videos<break/>(n=122), n (%) mean, standard deviation</td><td align="left" valign="bottom">Fictional videos<break/>(n=115), n (%) mean, standard deviation</td><td align="left" valign="bottom">Total videos<break/>(N=457), n (%) mean, standard deviation</td></tr></thead><tbody><tr><td align="left" valign="top">Animism or spiritualism</td><td align="left" valign="top">7 (3) 0.03, 0.17</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">3 (3) 0.03, 0.16</td><td align="left" valign="top">10 (2) 0.02, 0.14</td></tr><tr><td align="left" valign="top">Catholic Christianity</td><td align="left" valign="top">12 (5) 0.05, 0.22</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">13 (3) 0.03, 0.16</td></tr><tr><td align="left" valign="top">Other Christianity</td><td align="left" valign="top">24 (11) 0.11, 0.31</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">4 (3) 0.03, 0.18</td><td align="left" valign="top">29 (6) 0.06, 0.24</td></tr><tr><td align="left" valign="top">Japanese Buddhism</td><td align="left" valign="top">14 (6) 0.06, 0.24</td><td align="left" valign="top">2 (2) 0.02, 0.12</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">16 (4) 0.04, 0.18</td></tr><tr><td align="left" valign="top">Other Buddhism</td><td align="left" valign="top">48 (22) 0.22, 0.41</td><td align="left" valign="top">9 (7) 0.07, 0.26</td><td align="left" valign="top">26 (23) 0.23, 0.42</td><td align="left" valign="top">83 (18) 0.18, 0.38</td></tr><tr><td align="left" valign="top">New religions</td><td align="left" valign="top">14 (6) 0.06, 0.24</td><td align="left" valign="top">4 (3) 0.03, 0.17</td><td align="left" valign="top">0 (0) 0.00, sd</td><td align="left" valign="top">18 (4) 0.03, 0.19</td></tr><tr><td align="left" valign="top">Other Asian religions</td><td align="left" valign="top">11 (5) 0.05, 0.21</td><td align="left" valign="top">1 (1) 0.01, 0.09</td><td align="left" valign="top">5 (4) 0.04, sd</td><td align="left" valign="top">17 (4) 0.04, 0.18</td></tr><tr><td align="left" valign="top">Other Middle East or Western religions</td><td align="left" valign="top">9 (4) 0.04, 0.19</td><td align="left" valign="top">0 (0) 0.00, 0.00</td><td align="left" valign="top">2 (2) 0.02, sd</td><td align="left" valign="top">11 (2) 0.02, 0.15</td></tr><tr><td align="left" valign="top">Shinto</td><td align="left" valign="top">15 (7) 0.07, 0.25</td><td align="left" valign="top">1 (1) 0.01, sd</td><td align="left" valign="top">15 (13) 0.13, 0.33</td><td align="left" valign="top">31 (7) 0.07, 0.25</td></tr><tr><td align="left" valign="top">No religion</td><td align="left" valign="top">118 (54) 0.54, 0.50*<sup><xref ref-type="table-fn" rid="table4fn1">a</xref></sup></td><td align="left" valign="top">108 (89) 0.89, 0.32*</td><td align="left" valign="top">78 (68) 0.68, 0.46</td><td align="left" valign="top">304 (67) 0.67, 0.46</td></tr><tr><td align="left" valign="top">Total of religion stances</td><td align="left" valign="top">272 (123) 1.23</td><td align="left" valign="top">126 (103) 1.03</td><td align="left" valign="top">134 (116) 1.16</td><td align="left" valign="top">532 (116) 1.16</td></tr></tbody></table><table-wrap-foot><fn id="table4fn1"><p><sup>a</sup>Asteriscs indicate the results are statistically significant</p></fn></table-wrap-foot></table-wrap></sec><sec id="s3-4"><title>Family Networks of Those Deceased</title><p>To address RQ6, <xref ref-type="table" rid="table5">Table 5</xref> displays the network measurements of 164 videos that mentioned family members. Please refer to <xref ref-type="supplementary-material" rid="app4">Multimedia Appendix 4</xref> to interpret these results. The high number of edges in the regular and fictional families implies trust (assurance). This is reinforced with the highly weighted degree and high clustering coefficient in the networks of regular people. Access to other family members is faster in regular families based on closeness and path length. Regular family structures are the densest and most complex, as supported by density and the number of triangles. These structures are also the most unified based on their low modularity.</p><table-wrap id="t5" position="float"><label>Table 5.</label><caption><p>Network measurements of family networks calculated with Gephi.</p></caption><table id="table5" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Measure</td><td align="left" valign="bottom">All videos</td><td align="left" valign="bottom">Regular</td><td align="left" valign="bottom">Celebrity</td><td align="left" valign="bottom">Fictional</td></tr></thead><tbody><tr><td align="left" valign="top">Nodes</td><td align="left" valign="top">17</td><td align="left" valign="top">13</td><td align="left" valign="top">10</td><td align="left" valign="top">14</td></tr><tr><td align="left" valign="top">Edges</td><td align="left" valign="top">62</td><td align="left" valign="top">30</td><td align="left" valign="top">19</td><td align="left" valign="top">32</td></tr><tr><td align="left" valign="top">Average weighted degree</td><td align="left" valign="top">47.529</td><td align="left" valign="top">40.6150</td><td align="left" valign="top">8.8000</td><td align="left" valign="top">12.7140</td></tr><tr><td align="left" valign="top">Average harmonic closeness</td><td align="left" valign="top">0.7279</td><td align="left" valign="top">0.8012</td><td align="left" valign="top">0.7111</td><td align="left" valign="top">0.6758</td></tr><tr><td align="left" valign="top">Average betweenness</td><td align="left" valign="top">0.0362</td><td align="left" valign="top">0.0361</td><td align="left" valign="top">0.0722</td><td align="left" valign="top">0.0540</td></tr><tr><td align="left" valign="top">Average clustering coefficient</td><td align="left" valign="top">0.8430</td><td align="left" valign="top">0.8830</td><td align="left" valign="top">0.7810</td><td align="left" valign="top">0.8090</td></tr><tr><td align="left" valign="top">Average path length</td><td align="left" valign="top">1.5441</td><td align="left" valign="top">1.3970</td><td align="left" valign="top">1.5777</td><td align="left" valign="top">1.6483</td></tr><tr><td align="left" valign="top">Density</td><td align="left" valign="top">0.456</td><td align="left" valign="top">0.6030</td><td align="left" valign="top">0.4220</td><td align="left" valign="top">0.3520</td></tr><tr><td align="left" valign="top">Diameter</td><td align="left" valign="top">2</td><td align="left" valign="top">2</td><td align="left" valign="top">2</td><td align="left" valign="top">2</td></tr><tr><td align="left" valign="top">Modularity</td><td align="left" valign="top">0.037</td><td align="left" valign="top">0.022</td><td align="left" valign="top">0.047</td><td align="left" valign="top">0.127</td></tr><tr><td align="left" valign="top">Triangles</td><td align="left" valign="top">118</td><td align="left" valign="top">96</td><td align="left" valign="top">14</td><td align="left" valign="top">32</td></tr><tr><td align="left" valign="top">Connected components</td><td align="left" valign="top">1</td><td align="left" valign="top">1</td><td align="left" valign="top">1</td><td align="left" valign="top">1</td></tr></tbody></table></table-wrap><p>To answer RQ5 and RQ6, <xref ref-type="fig" rid="figure4">Figure 4</xref> illustrates the role of parents as communication hubs in all the videos and in videos of regular people. The larger the label, the higher the closeness centrality, and the thicker the line, the higher the trust (assurance). Although fathers may appear to have the higher closeness centrality, and thus the fastest reach to other family members, the networks of regular people demonstrate that the mother is equally important in this role. Further, the higher trust in videos of regular people is towards sons and daughters.</p><fig position="float" id="figure4"><label>Figure 4.</label><caption><p>(A) Undirected overall family networks (closeness centrality 1&#x2010;0.51; edge thickness 1&#x2010;75) and (B) networks of regular people (closeness centrality 1&#x2010;0.54; edge thickness 1&#x2010;48). Colors indicate different member clusters in each network.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="formative_v10i1e81861_fig04.png"/></fig><p>On the other hand, <xref ref-type="fig" rid="figure5">Figure 5</xref> highlights that in the separate networks of celebrities and fictional characters, male members of the family such as brothers and husbands are prominent in terms of closeness centrality and trust or assurance. Therefore, horizontal male relations are overrepresented in these networks in contrast to regular people, where vertical relationships including women were most relevant. This complements our results in terms of gender bias representation of the deceased.</p><fig position="float" id="figure5"><label>Figure 5.</label><caption><p>(A) Family networks of celebrities (closeness 1&#x2010;0.55; edge thickness 1&#x2010;13) and (B) networks of fictional characters (closeness 1&#x2010;0.53; edge thickness 1&#x2010;14). Colors indicate different member clusters in each network.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="formative_v10i1e81861_fig05.png"/></fig></sec></sec><sec id="s4" sec-type="discussion"><title>Discussion</title><sec id="s4-1"><title>Social Actors in Japanese Peaceful Death</title><p>Based on word graphs, the deaths of men, mostly in the adult and older adult age groups, were broadcasted and mourned, even for foreign actors such as Luke Perry, whose last role was in Riverdale, an American streaming series targeting young adults. Based on content and network analysis, the low visibility of women in end-of-life was noted in fictional characters&#x2019; death, and to some extent, in that of celebrities. This is not representative of the near gender parity in Japanese deaths [<xref ref-type="bibr" rid="ref60">60</xref>], or of women patients&#x2019; desire for social connection at the end of life [<xref ref-type="bibr" rid="ref61">61</xref>]. These features were better reflected in videos of regular people&#x2019;s death, as per <xref ref-type="table" rid="table3">Table 3</xref> and <xref ref-type="fig" rid="figure4">Figure 4</xref>. In particular, <xref ref-type="fig" rid="figure4">Figure 4B</xref> highlights how fathers and mothers have equally fast access to other family members, and how sons and daughters equally show high trust or assurance in the networks.</p><p>This bias in gender representation in the other videos may partly occur due to the broader advertising reach for male YouTube viewers [<xref ref-type="bibr" rid="ref6">6</xref>]. In broader terms, Morioka [<xref ref-type="bibr" rid="ref62">62</xref>] argues that hegemonic masculinity in Japan, with its goal of ensuring socially dominant roles for men, can twist lay people&#x2019;s perceptions regarding risks. This may extend to end-of-life scenarios with popular celebrities and fictional characters, wherein the family networks are simplified and the active roles of women as patients, caretakers, and decision makers tend to be hidden from public view.</p><p>Beyond the gender binary, a few common people videos in the other or unknown category portrayed queer individuals and their kin, particularly in mourning and honoring the dead. Significant barriers remain in access to health care and family involvement in medical decision-making and care for sexual minorities in Japan [<xref ref-type="bibr" rid="ref63">63</xref>]. Considering that the recognition of marriage among queer people is in the process of being legalized, the visibility of queer individuals in the videos is still limited but expected to increase in the future. Consequently, it is worthwhile to conduct further studies on how their end-of-life experiences are portrayed in media and how queer health care networks are constructed. Overall, our results provide evidence that studying celebrities and fictional characters has limits in capturing contemporary end-of-life scenarios in Japan. Comparisons of these groups in other topics are also needed.</p><p>Content analysis of videos revealed that academics in conferences and open lectures, and a hospice doctor&#x2019;s YouTube channel provided fora and expert advice on &#x201C;good death.&#x201D; They included the process of death, end-of-life care, and alleviating the pain of bereaved families. Although this discourse seems disconnected from other health care scenarios, it is a key connection between experts and the public. Another example of emerging discussions of good death in social media was &#x201C;<italic>The Crocodile Who Dies After 100 Days</italic>&#x201D; (Hyaku nichi go ni shinu wani). Although found in a YouTube video, the original was a toon published on Twitter and Instagram between 2019 and 2020. The protagonist is an anthropomorphized crocodile spending time with his friends, wherein the comic panel shows a descending count from 100. The author wished to open discussions about death because &#x201C;being alive is to die someday&#x201D; and used Twitter because it was the best way to reach a large audience quickly [<xref ref-type="bibr" rid="ref64">64</xref>]. These examples show potential in modeling a wider acceptance of public end-of-life conversations among the Japanese public.</p></sec><sec id="s4-2"><title>Narratives of Japanese Peaceful Death</title><p>Based on keyword graphs, the predominance of cancer in Japanese end-of-life scenarios was reflected in regular people&#x2019;s videos; however, in general, death due to illness was high. This may partly stem from COVID-19. Although the Japanese government stopped tracking this illness in 2023, the number of general excess deaths and those due to respiratory and circulatory illnesses as well as cancer has increased since the beginning of the pandemic [<xref ref-type="bibr" rid="ref65">65</xref>]. COVID-19 can be asymptomatic and may also impact multiple organs, triggering respiratory and circulatory issues, and some forms of cancer [<xref ref-type="bibr" rid="ref66">66</xref>,<xref ref-type="bibr" rid="ref67">67</xref>]. Considering that young people are also more affected by long COVID-19 than older individuals [<xref ref-type="bibr" rid="ref68">68</xref>], these trends might be reflected in the videos in our study, where most deaths were not of older adults.</p><p>Some researchers estimate that deaths in Japan might be between 1.5 and 3.9 times higher than those reported, and that there is a need to report deaths with more demographic details including age [<xref ref-type="bibr" rid="ref69">69</xref>]. Our study provides evidence of substantiating this need in the Japanese context, wherein older adults are assumed to account for most of the deaths. We emphasize the need for end-of-life guidelines and care in both sudden death scenarios such as pandemics and long-term disability scenarios for adults of all ages.</p><p>Regarding fictional death narratives, Japanese and Western books combined with oral stories and video games appeared frequently. These stories ranged from <italic>kaidan</italic> (old ghost stories) to stories in modern settings, focused on male protagonists. In a previous study, book influencers were important communicators of death on Japanese Twitter (authors, under review). It seems that YouTubers have capitalized on audiovisuals to convey old and new books for monetary and social capital. This may partly explain the bias in targeting male viewers with male-centric death.</p></sec><sec id="s4-3"><title>Religion in Japanese Peaceful Death</title><p>Buddhism was the most frequent religion in regular people videos, based on content analysis. Buddhist priests, temples, mentions and figures of Buddha, mentions of attaining Buddhahood status in the afterlife, and Buddhist altars for the death (called butsudan) were present in these videos. However, Buddhism was not connected to medical staff or academia. Since most videos did not mention or show a specific Buddhist sect, it is unclear how many people follow new religions masquerading as Buddhism. For example, it is estimated that approximately 3 million people or 2.5% of the Japanese population follows Soka Gakkai [<xref ref-type="bibr" rid="ref70">70</xref>]. Some estimates of this figure combined with other new religions include 26.47% of the Japanese population [<xref ref-type="bibr" rid="ref71">71</xref>].</p><p>Although less present in the videos, new religions were discussed in terms of the higher self, the astral body, and Wicca (modern European witchcraft). New religions were usually mixed with Buddhism and other minority religions of East Asia, and some videos advertised products. They seem to be diffused across several social media channels, from YouTube, which is popular among people of all ages, to Instagram, which is more popular among young adults. New religions were also inserted in viral topics such as the video about &#x201C;<italic>The Crocodile Who Dies After 100 Days</italic>.&#x201D; In <xref ref-type="fig" rid="figure3">Figure 3A</xref>, the word &#x201C;chakra,&#x201D; is mentioned as a form of individualistic spirituality in videos. Loove [<xref ref-type="bibr" rid="ref72">72</xref>] mentions that these kinds of new religious practices, rather than depending on specific leadership, are tied to individualized consumer preferences. This consumerism dimension may explain the advertising efforts by practitioners.</p><p>In the celebrity videos, the death of former Prime Minister Abe was not connected to religion, according to place classification, annotated religions, and the word graphs. The 10 videos in our data related to Abe were released by Japanese national news media channels such as Ni Tele News and TBS News, and local news media such as SBS News and TV Tokyo BIZ. They focused on the words of Japanese politicians in government offices and common people interviewed in the streets. Conversely, influencers with a considerable fanbase spread the news in neighboring China using pictures and videos, relegating official news media to a less important role [<xref ref-type="bibr" rid="ref73">73</xref>]. Moreover, a Twitter-based study suggested that the negative emotions elicited by Abe&#x2019;s death were short-lived [<xref ref-type="bibr" rid="ref74">74</xref>].</p><p>To understand the role of religion in these videos, we must consider that a convergence of interests of some Shinto leaders and state officials to ally when establishing the ideological foundation for Japan. Since the Meiji era, secular politics have been understood as a more civilized form of state formation, so Shinto was redefined by the state as nonreligious immutable customs and traditions of a historically consistent and unified Japan [<xref ref-type="bibr" rid="ref75">75</xref>]. Here, the apparent nonrepresentation of religion in the celebrity videos uploaded by Japanese news media channels seems to reinforce myths of a hegemonic Japan at the end of life through social learning.</p><p>However, we must revisit the example of religion in the Yasukuni Shrine, wherein Abe was among the proponents to enshrine World War II criminals. This act jeopardizes diplomatic relations with neighboring Asian nations that suffered under wartime aggression. Japan is the home of 3.5 million foreign nationals [<xref ref-type="bibr" rid="ref76">76</xref>], and other ethnic denominations with various spiritual beliefs exist. Therefore, the image of Shinto emphasized by politicians such as Abe might generate discomfort among some Asian patients in Japanese hospitals.</p><p>The implication of our results regarding religion for health care providers is that it is not enough to assume that asking simple questions about spirituality at the end-of-life period will capture the beliefs and intentions of an individual and their bereaved family, as previously noted by Uriu et al [<xref ref-type="bibr" rid="ref30">30</xref>]. Understanding the patient&#x2019;s concrete spiritual needs beforehand, in collaboration with kin, is recommended. The implication for Japanese media is that they should showcase more religious diversity at the end-of-life period, particularly in the case of celebrities and fictional characters.</p></sec><sec id="s4-4"><title>Limitations and Future Studies</title><p>This study focused on the term &#x201C;peaceful death.&#x201D; Social media discourse is constantly changing. Therefore, it is advisable to monitor other keywords and their combinations to understand other end-of-life scenarios in Japan and other regions.</p><p>The data on family networks was limited and did not include friendships. We recommend that more data be gathered from both social media and surveys to better understand family dynamics at the end-of-life period.</p></sec><sec id="s4-5"><title>Conclusions</title><p>This study examined recent public discussions about peaceful death in Japanese YouTube videos, contributing to recent discourses about health care and end-of-life. From our analyses, we drew several conclusions:</p><p>1. Medical experts from academic and hospice contexts address regular people&#x2019;s deaths in Japan through social media but do not work with religious stakeholders.</p><p>2. Most deaths that were discussed were of adult men, with the cause being mostly attributed to illness.</p><p>3. Religions are more present in regular peoples&#x2019; deaths than in those of celebrities and fictional characters. Buddhism was the most common religion, although various others were also observed.</p><p>4. While the deaths of celebrities and fictional characters portray male relatives as more relevant, both men and women were relevant in the deaths of regular people.</p><p>5. Networks of regular people indicated the highest assurance, fastest access to other family members, and the highest density and complexity. Fathers and mothers had the fastest access to other family members during peaceful death. Sons and daughters showed the highest assurance during peaceful death.</p></sec></sec></body><back><ack><p>The authors would like to thank the members of Fujita Laboratory in Kyoto University.</p></ack><notes><sec><title>Funding</title><p>This study was supported by the Kyoto University Management Expense Grant and the Uehiro Foundation on Ethics and Education.</p></sec><sec><title>Data Availability</title><p>The anonymized dataset generated and analyzed during this study is available in the Zenodo repository [<xref ref-type="bibr" rid="ref77">77</xref>].</p></sec></notes><fn-group><fn fn-type="con"><p>VMX and OM conceptualized the analysis, determined the methods, verified the data, and reviewed and edited the manuscript. VMX classified and interpreted the data, conducted the formal analysis, drew the data visualizations, and wrote the original draft of the manuscript.</p></fn><fn fn-type="conflict"><p>None declared.</p></fn></fn-group><glossary><title>Abbreviations</title><def-list><def-item><term id="abb1">RQ</term><def><p>research question</p></def></def-item><def-item><term id="abb2">TBS</term><def><p>Tokyo Broadcasting System</p></def></def-item></def-list></glossary><ref-list><title>References</title><ref id="ref1"><label>1</label><nlm-citation citation-type="web"><article-title>Number of deaths. 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pub-id-type="doi">10.5281/zenodo.18168743</pub-id></nlm-citation></ref></ref-list><app-group><supplementary-material id="app1"><label>Multimedia Appendix 1</label><p>Classification of individuality.</p><media xlink:href="formative_v10i1e81861_app1.docx" xlink:title="DOCX File, 14 KB"/></supplementary-material><supplementary-material id="app2"><label>Multimedia Appendix 2</label><p>Narratives of death.</p><media xlink:href="formative_v10i1e81861_app2.docx" xlink:title="DOCX File, 16 KB"/></supplementary-material><supplementary-material id="app3"><label>Multimedia Appendix 3</label><p>Intercoder agreement.</p><media xlink:href="formative_v10i1e81861_app3.docx" xlink:title="DOCX File, 15 KB"/></supplementary-material><supplementary-material id="app4"><label>Multimedia Appendix 4</label><p>Network measurements.</p><media xlink:href="formative_v10i1e81861_app4.docx" xlink:title="DOCX File, 15 KB"/></supplementary-material><supplementary-material id="app5"><label>Multimedia Appendix 5</label><p>Findings from the death narratives.</p><media xlink:href="formative_v10i1e81861_app5.docx" xlink:title="DOCX File, 19 KB"/></supplementary-material></app-group></back></article>