Minh D. Nguyen

Vishal Dey, Peter Krasniak, Minh Nguyen et al.

Background: A new illness could first come to the public attention over social media before it is medically defined, formally documented or systematically studied. One example is a phenomenon known as breast implant illness (BII) that has been extensively discussed on social media, though vaguely defined in medical literature. Objectives: The objective of this study is to construct a data analysis pipeline to understand emerging illness using social media data, and to apply the pipeline to understand key attributes of BII. Methods: We conducted a pipeline of social media data analysis using Natural Language Processing (NLP) and topic modeling. We extracted mentions related to signs/symptoms, diseases/disorders and medical procedures using the Clinical Text Analysis and Knowledge Extraction System (cTAKES) from social media data. We mapped the mentions to standard medical concepts. We summarized mapped concepts to topics using Latent Dirichlet Allocation (LDA). Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. Results: Our pipeline identified topics related to toxicity, cancer and mental health issues that are highly associated with BII. Our pipeline also shows that cancers, autoimmune disorders and mental health problems are emerging concerns associated with breast implants based on social media discussions. The pipeline also identified mentions such as rupture, infection, pain and fatigue as common self-reported issues among the public, as well as toxicity from silicone implants. Conclusions: Our study could inspire future work studying the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using NLP techniques, and demonstrates the potential of using social media information to better understand similar emerging illnesses.

Minh D. Nguyen, Won-Yong Shin

DBSCAN may not be sufficient when the input data type is heterogeneous in terms of textual description. When we aim to discover clusters of geo-tagged records relevant to a particular point-of-interest (POI) on social media, examining only one type of input data (e.g., the tweets relevant to a POI) may draw an incomplete picture of clusters due to noisy regions. To overcome this problem, we introduce DBSTexC, a newly defined density-based clustering algorithm using spatio--textual information. We first characterize POI-relevant and POI-irrelevant tweets as the texts that include and do not include a POI name or its semantically coherent variations, respectively. By leveraging the proportion of POI-relevant and POI-irrelevant tweets, the proposed algorithm demonstrates much higher clustering performance than the DBSCAN case in terms of $\mathcal{F}_1$ score and its variants. While DBSTexC performs exactly as DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with the textually heterogeneous inputs. Furthermore, to further improve the clustering quality by fully capturing the geographic distribution of tweets, we present fuzzy DBSTexC (F-DBSTexC), an extension of DBSTexC, which incorporates the notion of fuzzy clustering into the DBSTexC. We then demonstrate the robustness of F-DBSTexC via intensive experiments. The computational complexity of our algorithms is also analytically and numerically shown.