ArticleFrontiers in psychology2020
Male and Female Users' Differences in Online Technology Community Based on Text Mining.
Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Gender Differences in the Use of ChatGPT as Generative Artificial Intelligence for Clinical Research and Decision-Making in Occupational Medicine.Healthcare (Basel, Switzerland) · 2025Article
- Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism.Scientific reports · 2025Article
- Social anxiety and emoji use: gender differences and the role of loneliness in digital communication among college students.Frontiers in psychology · 2025Article
- "I Been Taking Adderall Mixing it With Lean, Hope I Don't Wake Up Out My Sleep": Harnessing Twitter to Understand Nonmedical Prescription Stimulant Use among Black Women and Men Subscribers.medRxiv : the preprint server for health sciences · 2024Article
- Impact of the COVID-19 Pandemic on Swedish Adolescents' Mental Health, Psychosocial Functioning, Risk Behaviours, and Victimisation: Gender Differences and Implications.International journal of environmental research and public health · 2024Article
- Online Health Information-Seeking Behaviour among People of African Descent in the United Kingdom: A Qualitative Study.Healthcare (Basel, Switzerland) · 2024Article
- Supporting self-management for patients with Interstitial Lung Diseases: Utility and acceptability of digital devices.PLOS digital health · 2024Article
- Article
- Gender-sensitive sentiment analysis for estimating the emotional climate in online teacher education.Learning environments research · 2023Article
- How do topics and emotions develop in elementary school children? A text mining perspective based on free-writing text over 6 years.Frontiers in psychology · 2023Article
- Development and validity of a mentally-passive and mentally-active sedentary time questionnaire in nursing college students.Frontiers in public health · 2023Article
- A Social-Ecological Approach to Modeling Sense of Virtual Community (SOVC) in Livestreaming Communities.Proceedings of the ACM on human-computer interaction · 2022Article
- The Use of Close Friends on Instagram, Help-Seeking Willingness, and Suicidality Among Hong Kong Youth: Exploratory Sequential Mixed Methods Study.Journal of medical Internet research · 2022Article
- Perception of Synchronized Online Teaching Using Blackboard Collaborate among Undergraduate Dental Students in Saudi Arabia.International journal of environmental research and public health · 2022Article
- Research on the Mixed Education Mode for the Safety Engineering Major during the Coronavirus (COVID-19) Epidemic.International journal of environmental research and public health · 2022Article
- Trends in the Study of Cultural-Historical Phenomena on the Internet (based on a study of Russians' attitudes towards money).Psychology in Russia : state of the art · 2022Article
- News Text Mining-Based Business Sentiment Analysis and Its Significance in Economy.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the emergence of online communities, more and more people are participating in online technology communities to meet personalized learning needs. This study aims to investigate whether and how male and female users behave differently in online technology communities. Using text data from the Python Technology Community, through the LDA (Latent Dirichlet Allocation) model, sentiment analysis, and regression analysis, this paper reveals the different topics of male and female users in the online technology community, their sentimental tendencies and activity under different topics, and their correlation and mutual influence. The results show the following: (1) Male users tend to provide information help, while female users prefer to participate in the topic of making friends and advertising. (2) When communicating in the technology community, male and female users mostly express positive emotions, but female users express positive emotions more frequently. (3) Different emotional tendencies of male and female users under different topics have different effects on their activity in the community. The activity of female users is more susceptible to emotional orientation.
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