Applying Sparse Machine Learning Methods to Twitter: Analysis of the 2012 Change in Pap Smear Guidelines. A
Courtney Rees Lyles1, Andrew Godbehere, Gem Le
1Center for Vulnerable Populations & Division of General Internal Medicine at the Zuckerberg San Francisco General Hospital, Department of Medicine, University of California San Francisco, San Francisco, CA, United States. courtney.lyles@ucsf.edu.
JMIR Public Health and Surveillance
|June 12, 2016
Summary
Machine learning effectively summarized Twitter discussions on cervical cancer screening. The analysis revealed public awareness and direct impact from updated Pap smear guidelines.
Area of Science:
- Public Health
- Computational Linguistics
- Social Media Analytics
Background:
- Synthesizing large volumes of social media data is challenging.
- Social media discussions offer insights into public opinion and decision-making.
- Analyzing online conversations can inform public health strategies.
Purpose of the Study:
- To evaluate sparse machine learning for summarizing Twitter dialogues on cervical cancer.
- To analyze cervical cancer discussions before and after 2012 Pap smear guideline changes.
- To assess the utility of machine learning in understanding public health discourse.
Main Methods:
- Applied sparse machine learning algorithms to Twitter data (Jan-June 2012).
- Analyzed messages containing "cervical cancer," "Pap smear," and "Pap test."
- Used topic modeling and qualitative coding to identify discussion themes and changes.
Main Results:
- Machine learning effectively grouped relevant cervical cancer discussion topics.
- Identified ~20% irrelevant content, demonstrating method's precision.
- Qualitative analysis showed discussions reflected guideline changes and screening promotion.
Conclusions:
- Machine learning tools are valuable for analyzing cervical cancer discussions on Twitter.
- Significant public information on screening exists on social media.
- Observed a direct impact of guideline changes on Twitter conversations.
Related Concept Videos
01:52Regression Toward the Mean
7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
01:25Statistical Methods for Analyzing Epidemiological Data
1.2K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.2K
01:18Steps in Outbreak Investigation
691
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
691