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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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Abstract:
Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being the most dominant. To quantify social spreading in each language over time, we compute the ‘contagion ratio’: The balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1—the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.
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Regular Article
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URL: https://doi.org/10.1140/epjds/s13688-021-00271-0 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010293/
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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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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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