Comparing Themes Extracted via Topic Modeling and Manual Content Analysis: Korean-Language Discussions of Dementia on Twitter.
Studies in health technology and informatics
2022; 295: 230-233
We randomly examined Korean-language Tweets mentioning dementia/Alzheimer's disease (n= 12,413) posted from November 28 to December 9, 2020, without limiting geographical locations. We independently applied Latent Dirichlet Allocation (LDA) topic modeling and qualitative content analysis to the texts of the Tweets. We compared the themes extracted by LDA topic modeling to those identified via manual coding methods. A total of 16 themes were detected from manual coding, with inter-rater reliability (Cohen's kappa) of 0.842. The proportions of the most prominent themes were: burdens of family caregiving (48.50%), reports of wandering/missing family members with dementia (18.12%), stigma (13.64%), prevention strategies (5.07%), risk factors (4.91%), healthcare policy (3.26%), and elder abuse/safety issues (1.75%). Seven themes whose contents were similar to themes derived from manual coding were extracted from the LDA topic modeling results (perplexity: -6.39, coherence score: 0.45). Our findings suggest that applying LDA topic modeling can be fairly effective at extracting themes from Korean Twitter discussions, in a manner analogous to qualitative coding, to gain insights regarding caregiving for family members with dementia, and our approach can be applied to other languages.
View details for DOI 10.3233/SHTI220704
View details for PubMedID 35773850
Comparative K-Pop Choreography Analysis through Deep-Learning Pose Estimation across a Large Video Corpus
DIGITAL HUMANITIES QUARTERLY
2021; 15 (1)
View details for Web of Science ID 000696408800016
Trends in Poor Health Indicators Among Black and Hispanic Middle-aged and Older Adults in the United States, 1999-2018.
JAMA network open
2020; 3 (11): e2025134
Importance: Adults who belong to racial/ethnic minority groups are more likely than White adults to receive a diagnosis of chronic disease in the United States.Objective: To evaluate which health indicators have improved or become worse among Black and Hispanic middle-aged and older adults since the Minority Health and Health Disparities Research and Education Act of 2000.Design, Setting, and Participants: In this repeated cross-sectional study, a total of 4 856 326 records were extracted from the Behavioral Risk Factor Surveillance System from January 1999 through December 2018 of persons who self-identified as Black (non-Hispanic), Hispanic (non-White), or White and who were 45 years or older.Exposure: The 1999 legislation to reduce racial/ethnic health disparities.Main Outcomes and Measures: Poor health indicators and disparities including major chronic diseases, physical inactivity, uninsured status, and overall poor health.Results: Among the 4 856 326 participants (2 958 041 [60.9%] women; mean [SD] age, 60.4 [11.8] years), Black adults showed an overall decrease indicating improvement in uninsured status (beta=-0.40%; P<.001) and physical inactivity (beta=-0.29%; P<.001), while they showed an overall increase indicating deterioration in hypertension (beta=0.88%; P<.001), diabetes (beta=0.52%; P<.001), asthma (beta=0.25%; P<.001), and stroke (beta=0.15%; P<.001) during the last 20 years. The Black-White gap (ie, the change in beta between groups) showed improvement (2 trend lines converging) in uninsured status (-0.20%; P<.001) and physical inactivity (-0.29%; P<.001), while the Black-White gap worsened (2 trend lines diverging) in diabetes (0.14%; P<.001), hypertension (0.15%; P<.001), coronary heart disease (0.07%; P<.001), stroke (0.07%; P<.001), and asthma (0.11%; P<.001). Hispanic adults showed improvement in physical inactivity (beta=-0.28%; P=.02) and perceived poor health (beta=-0.22%; P=.001), while they showed overall deterioration in hypertension (beta=0.79%; P<.001) and diabetes (beta=0.50%; P<.001). The Hispanic-White gap showed improvement in coronary heart disease (-0.15%; P<.001), stroke (-0.04%; P<.001), kidney disease (-0.06%; P<.001), asthma (-0.06%; P=.02), arthritis (-0.26%; P<.001), depression (-0.23%; P<.001), and physical inactivity (-0.10%; P=.001), while the Hispanic-White gap worsened in diabetes (0.15%; P<.001), hypertension (0.05%; P=.03), and uninsured status (0.09%; P<.001).Conclusions and Relevance: This study suggests that Black-White disparities increased in diabetes, hypertension, and asthma, while Hispanic-White disparities remained in diabetes, hypertension, and uninsured status.
View details for DOI 10.1001/jamanetworkopen.2020.25134
View details for PubMedID 33175177
Bridges, Sex Slaves, Tweets, and Guns A Multi-Domain Model of Conspiracy Theory
FOLKLORE AND SOCIAL MEDIA
View details for DOI 10.7330/9781646420599.c002
View details for Web of Science ID 000635283000003
Comparing published scientific journal articles to their pre-print versions
INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
2019; 20 (4): 335-350
View details for DOI 10.1007/s00799-018-0234-1
View details for Web of Science ID 000497276800004
Reading the Quan Tang shi: Literary History, Topic Modeling, Divergence Measures
DIGITAL HUMANITIES QUARTERLY
2019; 13 (4)
View details for Web of Science ID 000522557300008
SoLoGlo - A Service to Archive, Analyze, and Link Social, Local, and Global News
SOC IMAGING SCIENCE & TECHNOLOGY. 2015: 27-29
View details for Web of Science ID 000409399100007
Semi-Supervised Morphosyntactic Classification of Old Icelandic
2014; 9 (7): e102366
We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data.
View details for DOI 10.1371/journal.pone.0102366
View details for Web of Science ID 000341306600070
View details for PubMedID 25029462
View details for PubMedCentralID PMC4100772
A searchable meta-graph can connect even troublesome house elves and other supernatural beings to scholarly folk categories.
COMMUNICATIONS OF THE ACM
2012; 55 (7): 60-70
View details for DOI 10.1145/2209249.2209267
View details for Web of Science ID 000306310900026