Summary of Social Media As a Sensor: Analyzing Twitter Data For Breast Cancer Medication Effects Using Natural Language Processing, by Seibi Kobara et al.
Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing
by Seibi Kobara, Alireza Rafiei, Masoud Nateghi, Selen Bozkurt, Rishikesan Kamaleswaran, Abeed Sarker
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel study employs natural language processing (NLP) methodologies to analyze social media posts from an automatically curated breast cancer cohort. The researchers developed a transformer-based classifier to identify breast cancer patients/survivors on Twitter and collected longitudinal data from their profiles. A multi-layer rule-based model was designed to develop a breast cancer therapy-associated side effect lexicon, detecting patterns of medication usage and associated side effects among breast cancer patients. The study analyzed 1,454,637 posts from 583,962 unique users, identifying well-known side effects of hormone and chemotherapy as well as a subject feeling towards cancer and medications that may suggest pre-clinical phase side effects or emotional distress. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of researchers looked at social media to learn more about how people with breast cancer are doing. They used special computer programs to find posts from people who have been diagnosed with breast cancer and then analyzed what they were saying. They found that many people were talking about their medication side effects, like feeling tired or experiencing emotional distress. The study showed that social media can be a useful way to understand how people with breast cancer are doing and what kinds of challenges they’re facing. |
Keywords
* Artificial intelligence * Natural language processing * Nlp * Transformer
