Instagram和X(原Twitter)上的死产话语:内容分析

IF 2.3 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2025-09-24 DOI:10.2196/73980
Abigail Paradise Vit, Daniel Fraidin, Yaniv S Ovadia
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引用次数: 0

摘要

背景:在美国,每160例分娩中就有1例发生死胎,在全球范围内,每70例分娩中就有1例发生死胎。它深刻地影响着父母,往往导致悲伤、抑郁、焦虑和创伤后应激障碍,而社会的耻辱和公众意识的缺乏又加剧了这种情况。然而,还没有对社交媒体上关于死产的讨论进行全面的分析。目的:本研究旨在通过以下方法分析Instagram和X(以前的Twitter)上的死产相关内容:(1)使用主题建模识别主导主题,使用潜在狄利克雷分配、非负矩阵分解(NMF)和BERTopic进行评估;(2)通过共现网络分析检测有影响力的标签;(3)使用基于变压器的模型检查情绪和情绪;(4)使用预定义的代码本,通过人工图像分析对Instagram上死产的视觉表现进行分类(Meta);方法:通过RapidAPI收集与死产相关的帖子(N=27,395),使用Python 3.12.7 (Python软件基金会)分析Instagram帖子(#stillbirth: N= 7415; #stillbirthawareness: N= 8312; 2023-2024)和X帖子(#stillbirth: N= 11,668; 2020-2024),使用NetworkX进行标签共现网络和PageRank算法;由于Instagram应用程序编程接口的限制,比较分析仅限于2023-2024年。主题建模使用潜在狄利克雷分配、NMF和BERTopic进行评估,一致性评分指导我们的模型选择。使用基于变压器的RoBERTa和蒸馏RoBERTa分析情绪和情绪。对X个帖子进行虚假信息筛选。在Instagram上,使用预定义的代码本对2个代表性图像样本(n=366)进行手动分类,并使用Cohen Kappa评估互解释器可靠性。结果:与健康相关的标签(例如,# covid - 19)在X上出现的频率更高。主题建模显示,NMF获得了最高的一致性得分(Instagram上的#stillbirthawareness=0.624和#stillbirth=0.846, X上的#stillbirth=0.816)。在将COVID-19疫苗与死产联系起来的推文中,有27.8%(149/536)出现了医疗错误信息。在图像分析中,“文本图像”是最常见的,其次是记忆视觉(例如,墓地和死产婴儿)。两者间信度较强,分别为κ=0.837 (95% CI 0.773-0.891)和κ=0.821 (95% CI 0.755-0.879), Pearson相关性较高(r=0.999);结论:Instagram强调情感表达,X注重公共健康和信息内容。基于证据的沟通对于打击错误信息是必要的,特别是关于X的错误信息,在COVID-19等危机期间,X的实时信息会放大基于恐惧的叙述。此外,Instagram的视觉和纪念内容提供了一个机会,让父母的悲伤合法化,并通过让失去亲人的父母直接参与意识运动,来验证和人性化损失。针对特定平台的策略和更强的节制可以提高卫生话语的可信度。未来的研究应审查有针对性的方法,以打击错误信息并帮助受影响的人群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Stillbirth Discourse on Instagram and X (Formerly Twitter): Content Analysis.

Stillbirth Discourse on Instagram and X (Formerly Twitter): Content Analysis.

Stillbirth Discourse on Instagram and X (Formerly Twitter): Content Analysis.

Stillbirth Discourse on Instagram and X (Formerly Twitter): Content Analysis.

Background: Stillbirth, the loss of a fetus after the 20th week of pregnancy, affects about 1 in 160 deliveries in the United States and nearly 1 in 70 globally. It profoundly affects parents, often resulting in grief, depression, anxiety, and posttraumatic stress disorder, exacerbated by societal stigma and a lack of public awareness. However, no comprehensive analysis has explored social media discussions of stillbirth.

Objective: This study aimed to analyze stillbirth-related content on Instagram and X (formerly Twitter) by (1) identifying dominant themes using topic modeling, evaluated using latent Dirichlet allocation, non-negative matrix factorization (NMF), and BERTopic; (2) detecting influential hashtags via co-occurrence network analysis; (3) examining sentiments and emotions using transformer-based models; (4) categorizing visual representations of stillbirth on Instagram (Meta) through manual image analysis with a predefined codebook; and (5) screening for misinformation relating to stillbirth on X.

Methods: Stillbirth-related posts were collected via RapidAPI (N=27,395), with Instagram posts (#stillbirth: n=7415; #stillbirthawareness: n=8312; 2023-2024) and X posts (#stillbirth: n=11,668; 2020-2024) analyzed using Python 3.12.7 (Python Software Foundation), with NetworkX for hashtag co-occurrence networks and the PageRank algorithm; comparative analyses were restricted to 2023-2024 due to Instagram application programming interface constraints. Topic modeling was evaluated using latent Dirichlet allocation, NMF, and BERTopic, with coherence scores guiding our model selection. Sentiment and emotion were analyzed using transformer-based RoBERTa and DistilRoBERTa. Misinformation screening was applied to X posts. On Instagram, 2 representative image samples (n=366) were manually categorized using a predefined codebook, with the interrater reliability being assessed using Cohen Kappa.

Results: Health-related hashtags (eg, #COVID19) appeared more frequently on X. Topic modeling showed that NMF achieved the highest coherence scores (#stillbirthawareness=0.624 and #stillbirth=0.846 on Instagram, #stillbirth=0.816 on X). Medical misinformation appeared in 27.8% (149/536) of tweets linking COVID-19 vaccines to stillbirth. In the image analysis, "Image of text" was most common, followed by remembrance visuals (eg, gravesites and stillborn infants). The interrater reliability was strong, κ=0.837 (95% CI 0.773-0.891) and κ=0.821 (95% CI 0.755-0.879), with high Pearson correlation (r=0.999; P<.001) and no significant difference (χ²7=12.4; P=.09). The sentiment analysis found that positive sentiments exceeded negative sentiments. The emotion analysis showed that fear and sadness were dominant, with fear being more prevalent on X.

Conclusions: Instagram emphasizes emotional expression while X focuses on public health and informational content. Evidence-based communication is necessary to counter misinformation, especially on X, whose real-time affordances amplify fear-based narratives during crises, such as COVID-19. In addition, Instagram's visual and commemorative content offers an opportunity to legitimize parental grief and to validate and humanize loss by directly involving bereaved parents in awareness campaigns. Platform-specific strategies and stronger moderation could enhance health discourse credibility. Future research should examine targeted approaches to counter misinformation and assist affected populations.

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