Affective, linguistic and topic patterns in online autism communities

被引：8

作者：

Nguyen, Thin ^{[1
]}

Duong, Thi ^{[1
]}

Phung, Dinh ^{[1
]}

Venkatesh, Svetha ^{[1
]}

机构：

[1] Deakin University, Australia

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8787卷

关键词：

Data mining - Diagnosis - Social networking (online) - Online systems - Diseases - Learning systems;

D O I：

10.1007/978-3-319-11746-1_35

中图分类号：

学科分类号：

摘要：

Online communities offer a platform to support and discuss health issues. They provide a more accessible way to bring people of the same concerns or interests. This paper aims to study the characteristics of online autism communities (called Clinical) in comparison with other online communities (called Control) using data from 110 Live Journal weblog communities. Using machine learning techniques, we comprehensively analyze these online autism communities. We study three key aspects expressed in the blog posts made by members of the communities: sentiment, topics and language style. Sentiment analysis shows that the sentiment of the clinical group has lower valence, indicative of poorer moods than people in control. Topics and language styles are shown to be good predictors of autism posts. The result shows the potential of social media in medical studies for a broad range of purposes such as screening, monitoring and subsequently providing supports for online communities of individuals with special needs. © Springer International Publishing Switzerland 2014

引用

页码：474 / 488