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Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning
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In: Front Artif Intell (2022)
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Quantifying language changes surrounding mental health on Twitter ...
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The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community ...
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Augmenting semantic lexicons using word embeddings and transfer learning ...
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Generalized word shift graphs: a method for visualizing and explaining pairwise comparisons between texts
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In: Springer Berlin Heidelberg (2021)
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Local information sources received the most attention from Puerto Ricans during the aftermath of Hurricane Maria
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In: PLoS One (2021)
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Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter
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In: Sci Adv (2021)
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The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020
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In: EPJ Data Sci (2021)
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How the world’s collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter
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In: PLoS One (2021)
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Local information sources received the most attention from Puerto Ricans during the aftermath of Hurricane María ...
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Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter ...
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Hahahahaha, Duuuuude, Yeeessss!: A two-parameter characterization of stretchable words and the dynamics of mistypings and misspellings
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In: PLoS One (2020)
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Fame and Ultrafame: Measuring and comparing daily levels of `being talked about' for United States' presidents, their rivals, God, countries, and K-pop ...
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Forecasting the onset and course of mental illness with Twitter data
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The Lexicocalorimeter: Gauging public health through caloric input and output on social media
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Alajajian, Sharon E.; Williams, Jake Ryland; Reagan, Andrew J.; Alajajian, Stephen C.; Frank, Morgan R.; Mitchell, Lewis; Lahne, Jacob; Danforth, Christopher M.; Dodds, Peter Sheridan. - : Public Library of Science, 2017
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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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Research Article
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URL: http://www.ncbi.nlm.nih.gov/pubmed/28187216 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302853/ https://doi.org/10.1371/journal.pone.0168893
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Forecasting the onset and course of mental illness with Twitter data
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Forecasting the onset and course of mental illness with Twitter data ...
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What we write about when we write about causality: Features of causal statements across large-scale social discourse ...
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The Lexicocalorimeter: Gauging public health through caloric input and output on social media
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In: PLoS (2015)
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