ArticlePloS one2017
The Lexicocalorimeter: Gauging public health through caloric input and output on social media.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Monitoring the well-being of vulnerable transit riders using machine learning based sentiment analysis and social media: Lessons from COVID-19.Environment and planning. B, urban analytics and city science · 2023Article
- Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours.Public health nutrition · 2021Review
- The Sentiment Analysis of Tweets as a New Tool to Measure Public Perception of Male Erectile and Ejaculatory Dysfunctions.Sexual medicine · 2019Article
- Assessing Canadians Health Activity and Nutritional Habits Through Social Media.Frontiers in public health · 2019Article
- Weather impacts expressed sentiment.PloS one · 2018Article
- Big Data's Role in Precision Public Health.Frontiers in public health · 2018Review
- Forecasting the onset and course of mental illness with Twitter data.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We propose and develop a Lexicocalorimeter: an online, interactive instrument for measuring the "caloric content" of social media and other large-scale texts. We do so by constructing extensive yet improvable tables of food and activity related phrases, and respectively assigning them with sourced estimates of caloric intake and expenditure. We show that for Twitter, our naive measures of "caloric input", "caloric output", and the ratio of these measures are all strong correlates with health and well-being measures for the contiguous United States. Our caloric balance measure in many cases outperforms both its constituent quantities; is tunable to specific health and well-being measures such as diabetes rates; has the capability of providing a real-time signal reflecting a population's health; and has the potential to be used alongside traditional survey data in the development of public policy and collective self-awareness. Because our Lexicocalorimeter is a linear superposition of principled phrase scores, we also show we can move beyond correlations to explore what people talk about in collective detail, and assist in the understanding and explanation of how population-scale conditions vary, a capacity unavailable to black-box type methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.