Mapping loneliness through social intelligence analysis: a step towards creating global loneliness map.

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2023-10-01 DOI:10.1136/bmjhci-2022-100728
Hurmat Ali Shah, Mowafa Househ
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Abstract

Objectives: Loneliness is a prevalent global public health concern with complex dynamics requiring further exploration. This study aims to enhance understanding of loneliness dynamics through building towards a global loneliness map using social intelligence analysis.

Settings and design: This paper presents a proof of concept for the global loneliness map, using data collected in October 2022. Twitter posts containing keywords such as 'lonely', 'loneliness', 'alone', 'solitude' and 'isolation' were gathered, resulting in 841 796 tweets from the USA. City-specific data were extracted from these tweets to construct a loneliness map for the country. Sentiment analysis using the valence aware dictionary for sentiment reasoning tool was employed to differentiate metaphorical expressions from meaningful correlations between loneliness and socioeconomic and emotional factors.

Measures and results: The sentiment analysis encompassed the USA dataset and city-wise subsets, identifying negative sentiment tweets. Psychosocial linguistic features of these negative tweets were analysed to reveal significant connections between loneliness, socioeconomic aspects and emotional themes. Word clouds depicted topic variations between positively and negatively toned tweets. A frequency list of correlated topics within broader socioeconomic and emotional categories was generated from negative sentiment tweets. Additionally, a comprehensive table displayed top correlated topics for each city.

Conclusions: Leveraging social media data provide insights into the multifaceted nature of loneliness. Given its subjectivity, loneliness experiences exhibit variability. This study serves as a proof of concept for an extensive global loneliness map, holding implications for global public health strategies and policy development. Understanding loneliness dynamics on a larger scale can facilitate targeted interventions and support.

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通过社会智力分析绘制孤独地图:迈向创建全球孤独地图的一步。
目标:孤独是一个普遍存在的全球公共卫生问题,其复杂的动态需要进一步探索。本研究旨在通过使用社会智力分析构建全球孤独地图,增强对孤独动态的理解。设置和设计:本文使用2022年10月收集的数据,为全球孤独地图提供了概念验证。收集了包含“孤独”、“孤独”和“孤独”等关键词的推特帖子,共有841条 从这些推文中提取了796条来自美国的特定城市的推文数据,构建了一张全国的孤独地图。情绪分析使用效价感知词典作为情绪推理工具,将孤独与社会经济和情绪因素之间的隐喻性表达与有意义的相关性区分开来。措施和结果:情绪分析包括美国数据集和城市子集,识别负面情绪推文。分析了这些负面推文的心理社会语言学特征,揭示了孤独感、社会经济方面和情感主题之间的显著联系。词云描述了语气积极和消极的推文之间的话题变化。负面情绪推文生成了更广泛的社会经济和情感类别中相关主题的频率列表。此外,一个综合表格显示了每个城市最相关的主题。结论:利用社交媒体数据可以深入了解孤独的多方面本质。鉴于其主观性,孤独体验表现出可变性。这项研究为广泛的全球孤独地图提供了概念证明,对全球公共卫生战略和政策制定具有启示。在更大范围内了解孤独的动态可以促进有针对性的干预和支持。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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