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通过分析推特和红迪网数据理解孤独感:比较研究

Understanding Loneliness Through Analysis of Twitter and Reddit Data: Comparative Study.

作者信息

Shah Hurmat Ali, Househ Mowafa

机构信息

Hamad Bin Khalifa University, Doha, Qatar.

出版信息

Interact J Med Res. 2025 Mar 14;14:e49464. doi: 10.2196/49464.

Abstract

BACKGROUND

Loneliness is a global public health issue contributing to a variety of mental and physical health issues. It increases the risk of life-threatening conditions and contributes to the burden on the economy in terms of the number of productive days lost. Loneliness is a highly varied concept, which is associated with multiple factors.

OBJECTIVE

This study aimed to understand loneliness through a comparative analysis of loneliness data on Twitter and Reddit, which are popular social media platforms. These platforms differ in terms of their use, as Twitter allows only short posts, while Reddit allows long posts in a forum setting.

METHODS

We collected global data on loneliness in October 2022. Twitter posts containing the words "lonely," "loneliness," "alone," "solitude," and "isolation" were collected. Reddit posts were extracted in March 2023. Using natural language processing techniques (valence aware dictionary for sentiment reasoning [VADER] tool from the natural language toolkit [NLTK]), the study identified and extracted relevant keywords and phrases related to loneliness from user-generated content on both platforms. The study used both sentiment analysis and the number of occurrences of a topic. Quantitative analysis was performed to determine the number of occurrences of a topic in tweets and posts, and overall meaningful topics were reported under a category.

RESULTS

The extracted data were subjected to comparative analysis to identify common themes and trends related to loneliness across Twitter and Reddit. A total of 100,000 collected tweets and 10,000 unique Reddit posts, including comments, were analyzed. The results of the study revealed the relationships of various social, political, and personal-emotional themes with the expression of loneliness on social media. Both platforms showed similar patterns in terms of themes and categories of discussion in conjunction with loneliness-related content. Both Reddit and Twitter addressed loneliness, but they differed in terms of focus. Reddit discussions were predominantly centered on personal-emotional themes, with a higher occurrence of these topics. Twitter, while still emphasizing personal-emotional themes, included a broader range of categories. Both platforms aligned with psychological linguistic features related to the self-expression of mental health issues. The key difference was in the range of topics, with Twitter having a wider variety of topics and Reddit having more focus on personal-emotional aspects.

CONCLUSIONS

Reddit posts provide detailed insights into data about the expression of loneliness, although at the cost of the diversity of themes and categories, which can be inferred from the data. These insights can guide future research using social media data to understand loneliness. The findings provide the basis for further comparative investigation of the expression of loneliness on different social media platforms and online platforms.

摘要

背景

孤独是一个全球性的公共卫生问题,会引发各种身心健康问题。它增加了危及生命状况的风险,并因生产天数的损失而加重了经济负担。孤独是一个高度多样化的概念,与多种因素相关。

目的

本研究旨在通过对推特和红迪网(Reddit)这两个热门社交媒体平台上的孤独数据进行比较分析来了解孤独。这些平台在使用方式上有所不同,推特只允许发布短文,而红迪网则允许在论坛环境中发布长文。

方法

我们在2022年10月收集了全球范围内关于孤独的数据。收集了包含“孤独”“寂寞”“独自”“独处”和“孤立”等词汇的推特帖子。红迪网的帖子于2023年3月提取。该研究使用自然语言处理技术(来自自然语言工具包[NLTK]的情感推理效价感知词典[VADER]工具),从两个平台上用户生成的内容中识别并提取与孤独相关的相关关键词和短语。该研究同时使用了情感分析和主题出现的次数。进行定量分析以确定推文和帖子中主题出现的次数,并在一个类别下报告总体有意义的主题。

结果

对提取的数据进行比较分析,以识别推特和红迪网上与孤独相关的共同主题和趋势。总共分析了100,000条收集到的推文和10,000条独特的红迪网帖子(包括评论)。研究结果揭示了各种社会、政治和个人情感主题与社交媒体上孤独表达之间的关系。在与孤独相关内容的讨论主题和类别方面,两个平台都呈现出相似的模式。红迪网和推特都涉及孤独问题,但在侧重点上有所不同。红迪网的讨论主要集中在个人情感主题上,这些主题出现的频率更高。推特虽然仍强调个人情感主题,但涵盖的类别范围更广。两个平台都与心理健康问题自我表达相关的心理语言特征相符。关键区别在于主题范围,推特的主题种类更多,而红迪网更侧重于个人情感方面。

结论

红迪网的帖子提供了关于孤独表达数据的详细见解,尽管代价是主题和类别的多样性,这可以从数据中推断出来。这些见解可以指导未来利用社交媒体数据来理解孤独的研究。研究结果为进一步比较不同社交媒体平台和在线平台上孤独表达奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/679c/11953590/ec452e2274f7/ijmr_v14i1e49464_fig1.jpg

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