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. 2018 Feb 9;4(1):e16.
doi: 10.2196/publichealth.8186.

Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States

Mohsen Farhadloo  1   2 , Kenneth Winneg  2 , Man-Pui Sally Chan  1 , Kathleen Hall Jamieson  2 , Dolores Albarracin  1
Affiliations

Associations of Topics of Discussion on Twitter With Survey Measures of Attitudes, Knowledge, and Behaviors Related to Zika: Probabilistic Study in the United States

Mohsen Farhadloo et al. JMIR Public Health Surveill. .

Abstract

Background: Recent outbreaks of Zika virus around the world led to increased discussions about this issue on social media platforms such as Twitter. These discussions may provide useful information about attitudes, knowledge, and behaviors of the population regarding issues that are important for public policy.

Objective: We sought to identify the associations of the topics of discussions on Twitter and survey measures of Zika-related attitudes, knowledge, and behaviors, not solely based upon the volume of such discussions but by analyzing the content of conversations using probabilistic techniques.

Methods: Using probabilistic topic modeling with US county and week as the unit of analysis, we analyzed the content of Twitter online communications to identify topics related to the reported attitudes, knowledge, and behaviors captured in a national representative survey (N=33,193) of the US adult population over 33 weeks.

Results: Our analyses revealed topics related to "congress funding for Zika," "microcephaly," "Zika-related travel discussions," "insect repellent," "blood transfusion technology," and "Zika in Miami" were associated with our survey measures of attitudes, knowledge, and behaviors observed over the period of the study.

Conclusions: Our results demonstrated that it is possible to uncover topics of discussions from Twitter communications that are associated with the Zika-related attitudes, knowledge, and behaviors of populations over time. Social media data can be used as a complementary source of information alongside traditional data sources to gauge the patterns of attitudes, knowledge, and behaviors in a population.

Keywords: Twitter; Zika; public health; public policy; topic modeling.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Flow of data processing and analyses.
Figure 2
Figure 2
The perplexity formula used to compare the probability models. The log-likelihood of a set of held-out documents can be calculated and used for comparing the models.
Figure 3
Figure 3
Comparison of weighting schemes (binary, term occurrence, and term frequency–inverse document frequency [tfidf]) for a vocabulary size of 8200. Perplexity of the held-out test set is the lowest for the term occurrence weighting scheme.
Figure 4
Figure 4
Probability of topics (circle markers) and survey items (square markers) over time. Using the trained model, the probability of each topic can be calculated in each week. The survey items at each week are the average of the participants' responses. Survey items missing in some weeks were not asked of the respondents in those weeks. Left: Attitude toward ground spraying (survey) compared with congress funding (Twitter) (197/LDA200). Right: Knowledge about microcephaly (survey) compared with Zika protection and travel (Twitter) (149/LDA150). LDA: latent Dirichlet allocation.
Figure 5
Figure 5
Word cloud of topics that showed significant correlation with survey items. LDA: latent Dirichlet allocation.
See this image and copyright information in PMC

References

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