Data note for everday health colab interview.

1_README.md

Exploring Conversations about Health at Scale during COVID-19

Hey there! Recently I was interviewed for a post on IDEO's Colab Blog which uses everday health as an example to discuss how data science shows up on IDEO projects. As some results were discussed to situate that example, this gist provides supplemental data and additional methodological details for data discussed within that post. This is shared publically to add important context to visualizations and discussion of preliminary results. It is not meant to be a complete paper. Other data sources are being considered by the project.

Additional Methodical Details

The Benjamini–Hochberg procedure controls the false discovery rate in the case of multiple comparisons. Overlaps between topics calculated by Jaccard Index. Health content found by filtering for "health" in the attributes listed below for each dataset. The tokens (including hashtags) used in groups found by identifying those appearing more often in health content (both before and during pandemic). Specifically, analysis finds those tags and tokens unique to health conversations by comparing health related content to content in general ("reference group") on each platform before clustering those into topical groups. Some tokens and tags are ambiguous or do not cluster into a larger group and are in the "other" group.

Additional Sample Details

This post uses data from Tumblr (hashtags from 2008–11–10 to 2020–05–19), various news sources (titles from 2020/04/15 to 2020/05/15), and Crunchbase (company descriptions from 2020/01/01 to 2020/05/15). Other datasets considered in internal analysis to try to reach a broader audience and demongraphics.

News Sample

The news outlet sample includes the following (powered by News API):

• ABC
• BBC
• Breitbart
• CNN
• DailyMail
• Fox News
• NPR
• The Verge
• Wall Street Journal
• Wired

Note that there are different numbers of articles for each outlet but the frequencies are normalized such that each outlet has the same weight. These sources chosen to cover a broad range of news topics and ideological audiences.

Tumblr

Tumblr allows for a sample of public conversations over a longer period of time. Furthermore, it's ability to support long form communication enabled the team to investigate rich stories. That said, as with any social media, the sample carries some bias and research must recognize the demographic skew of the platform. The sample size is also slightly small compared to some other sources reviewed.

Crunchbase

Not all compaies are documented within the platform and the potential bias in that sampling is unknown. That said, it still provides a broad perspective on start ups / new companies and an important window into industry activity.

Statistical Test Results

In response to the question "Going back to what you said earlier, what has changed since COVID-19?" the results are significant (P < (i / 124) 0.05). Additionally, results in response to the question "What themes are emerging that we should all pay more attention to?" are significant (P < (i / 6) 0.05). Note that the response to "What did you learn about how the public thinks about health?" is simply the observation that diet and exercise appear at the top of the frequency distribution.

Supplemental Data

Summary data are made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/. The following tables are available:

• 2_sample_size.csv: Size of sample used across different dimensions.
• 3_cross_group_topic_frequency_comparison.csv: Frequency (z-scores) of different tag groups across the different datastets examined.
• 4_tumblr_jaccards.csv: Jaccard index for overlaps between different hashtag groups in the Tumblr health posts dataset.
• 5_tumblr_health_tags_with_increase.csv: Tumblr hashtags with significant (P < (i/m) 0.05) increase from before pandemic to during (pre 2020 vs 2020).
• 6_token_mapping.csv: Mapping from token to token group.
• 7_other_tokens.csv: The list of tokens in the "other" group.

Future Work

Future work could investigate additional platforms and databases which may have different bias / demographic skew. Additionally, work in languages other than English is important to broadening these perspectives. Furthermore, this investigation focuses on passive observation but direct engagement through tools like surveys could further complement these methods. Next, even though the data yield significant results, the data could be expanded to acheve larger sample sizes. Finally, further investigation into the "other" group could reveal additional insights and other forms of topic modeling (other than clustering terms of higher frequency compared a "reference sample") could be explored though the brevity of the observations means that many topic modeling techniques like LDA performed poorly. Of course, a study over a longer time frame could help with addressing seasonality which was not considered in the results presented in the blog post.

2_sample_size.csv

3_cross_group_topic_frequency_comparison.csv

4_tumblr_jaccards.csv

5_tumblr_health_tags_with_increase.csv

6_token_mapping.csv

7_other_tokens.txt

bbc
could
fox
help
know
latest
2020
spears
britney
rise
ship
americans
heart
ban
today
cruise
warns
linked
rate
likely
early
threat
questions
release
concern
need
city
fighting
poll
healthtips
gesundheit
deutschesärzteblattaktuelles
cdnhealth
tips
breakingnews
tophealthnewssciencedaily
media
nyt
propaganda
personal
body
dailymail
healthbenefits
pakistan
healthtipsandtricks
women
udemy
infraredsauna
quotes
recovery
drpounders
healthcaribbean

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