While topic modeling is widely used, some scholars have already announced that the application of topic modeling in social science research is impossible to standardize. Meanwhile, researchers have...

Topic Modeling is currently one of the most widely employed unsupervised text-as-data techniques in the field of communication science. While researchers increasingly recognize the importance of valid...

Simon Donig, Dinara Gagarina and Timur Mitrofanov use computational analysis to gain new insights into how Latvian diaspora newspapers preserved cultural identity.

It wasn’t so long ago that topic modelling was all the rage, particularly in the digital humanities. Techniques like Latent Dirichlet Allocation (LDA), which can be used to unveil the hidden thematic ...

I recently added tags to my blog using BERTopic and a mix of LLMs. You can see the tags in the sidebar to the right (or in the footer on mobile). I’ve done...

Background: In an era dominated by social media conversations, it is pivotal to comprehend how suicide, a critical public health issue, is discussed online. Discussions around suicide often highlight a range of topics, such as mental health challenges, relationship conflicts, and financial distress. However, certain sensitive issues, like those affecting marginalized communities, may be underrepresented in these discussions. This underrepresentation is a critical issue to investigate because it is mainly associated with underserved demographics (eg, racial and sexual minorities), and models trained on such data will underperform on such topics. Objective: The objective of this study was to bridge the gap between established psychology literature on suicidal ideation and social media data by analyzing the topics discussed online. Additionally, by generating synthetic data, we aimed to ensure that datasets used for training classifiers have high coverage of critical risk factors to address and adequately represent underrepresented or misrepresented topics. This approach enhances both the quality and diversity of the data used for detecting suicidal ideation in social media conversations. Methods: We first performed unsupervised topic modeling to analyze suicide-related data from social media and identify the most frequently discussed topics within the dataset. Next, we conducted a scoping review of established psychology literature to identify core risk factors associated with suicide. Using these identified risk factors, we then performed guided topic modeling on the social media dataset to evaluate the presence and coverage of these factors. After identifying topic biases and gaps in the dataset, we explored the use of generative large language models to create topic-diverse synthetic data for augmentation. Finally, the synthetic dataset was evaluated for readability, complexity, topic diversity, and utility in training machine learning classifiers compared to real-world datasets. Results: Our study found that several critical suicide-related topics, particularly those concerning marginalized communities and racism, were significantly underrepresented in the real-world social media data. The introduction of synthetic data, generated using GPT-3.5 Turbo, and the augmented dataset improved topic diversity. The synthetic dataset showed levels of readability and complexity comparable to those of real data. Furthermore, the incorporation of the augmented dataset in fine-tuning classifiers enhanced their ability to detect suicidal ideation, with the F1-score improving from 0.87 to 0.91 on the University of Maryland Reddit Suicidality Dataset test subset and from 0.70 to 0.90 on the synthetic test subset, demonstrating its utility in improving model accuracy for suicidal narrative detection. Conclusions: Our results demonstrate that synthetic datasets can be useful to obtain an enriched understanding of online suicide discussions as well as build more accurate machine learning models for suicidal narrative detection on social media.

Background: #depression is underdiagnosed worldwide, and clinicians rely on interpreting patients’ subjective speech. Qualitative analysis of patient language does not scale, and existing computational #Approaches describe topics with keyword lists that miss clinical nuance. Objective: We evaluated whether clustering spontaneous speech transcripts with large language models (LLMs) yields clusters whose membership is associated with validated clinical scales across multilingual cohorts, and whether LLMs can render those clusters human-readable through fine-grained natural-language descriptions. We further examined which interview questions yield clusters most strongly associated with clinical status, and how sociodemographic factors relate to cluster membership. Methods: We analyzed spontaneous speech transcripts from 4 independent cohorts: a French general population sample (1338 participants) and 3 clinical samples in Italian (n=116), Chinese (n=52), and Spanish (n=90). Responses to open-ended questions were transcribed, embedded with a multilingual language model, dimensionally reduced, and grouped by density-based clustering. An LLM then summarized each cluster into a natural-language description. Cluster membership was tested for association with validated clinical scales (Patient #Health Questionnaire-9, Beck #depression Inventory, Generalized #anxiety Disorder 7-item scale, Athens Insomnia Scale, Multidimensional Fatigue Inventory, and Columbia Suicide Severity Rating Scale), clinician-assigned #depression diagnoses, and sociodemographic factors (age, education, and sex). Results: Unsupervised clustering yielded clusters significantly associated with clinical scores in the French, Italian, and Chinese cohorts, with an exploratory association in the smaller Spanish cohort. In the French general population, Patient #Health Questionnaire-9 #depression scores differed across clusters (η²=0.19, 95% CI 0.17 to 0.24,

LDA goes beyond text analysis, uncovering patterns in image, music, and graph data, driving innovative insights across diverse data types.

Unlocking Our Digital World: The Real-World Natural Language Processing Applications You Use Every Day Ever wonder how your phone finishes your sentences? Or how Gmail knows that sketchy email is…