@ARTICLE{Lerman2017jcss,
author="Lerman, Kristina and Marin, Luciano G.
and Arora, Megha and de Lima, Lucas H. Costa  and Ferrara, Emilio and Garcia, David",
title="Language, demographics, emotions, and the structure of online social networks",
journal="Journal of Computational Social Science",
year="2017",
month="Oct",
day="31",
abstract="Social networks affect individuals' economic opportunities and well-being. However, few of the factors thought to shape networks---culture, language, education, and income---were empirically validated at scale. To fill this gap, we collected a large number of social media posts from a major US metropolitan area. By associating these posts with US Census tracts through their locations, we linked socioeconomic indicators to group-level signals extracted from social media, including emotions, language, and online social ties. Our analysis shows that tracts with higher education levels have weaker social ties, but this effect is attenuated for tracts with high ratio of Hispanic residents. Negative emotions are associated with more frequent online interactions, or stronger social ties, while positive emotions are associated with weaker ties. These results hold for both Spanish and English tweets, evidencing that language does not affect this relationship between emotion and social ties. Our findings highlight the role of cognitive and demographic factors in online interactions and demonstrate the value of traditional social science sources, like US Census data, within social media studies.",
issn="2432-2725",
doi="10.1007/s42001-017-0001-x",
url="https://doi.org/10.1007/s42001-017-0001-x"
}

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C.","Ferrara, E.","Garcia, D."],"year":2017,"bibtype":"article","biburl":"https://bibbase.org/network/files/iNQKC4NCiGYaef6D9","bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Lerman"],"firstnames":["Kristina"],"suffixes":[]},{"propositions":[],"lastnames":["Marin"],"firstnames":["Luciano","G."],"suffixes":[]},{"propositions":[],"lastnames":["Arora"],"firstnames":["Megha"],"suffixes":[]},{"propositions":["de"],"lastnames":["Lima"],"firstnames":["Lucas","H.","Costa"],"suffixes":[]},{"propositions":[],"lastnames":["Ferrara"],"firstnames":["Emilio"],"suffixes":[]},{"propositions":[],"lastnames":["Garcia"],"firstnames":["David"],"suffixes":[]}],"title":"Language, demographics, emotions, and the structure of online social networks","journal":"Journal of Computational Social Science","year":"2017","month":"Oct","day":"31","abstract":"Social networks affect individuals' economic opportunities and well-being. However, few of the factors thought to shape networks—culture, language, education, and income—were empirically validated at scale. To fill this gap, we collected a large number of social media posts from a major US metropolitan area. By associating these posts with US Census tracts through their locations, we linked socioeconomic indicators to group-level signals extracted from social media, including emotions, language, and online social ties. Our analysis shows that tracts with higher education levels have weaker social ties, but this effect is attenuated for tracts with high ratio of Hispanic residents. Negative emotions are associated with more frequent online interactions, or stronger social ties, while positive emotions are associated with weaker ties. These results hold for both Spanish and English tweets, evidencing that language does not affect this relationship between emotion and social ties. Our findings highlight the role of cognitive and demographic factors in online interactions and demonstrate the value of traditional social science sources, like US Census data, within social media studies.","issn":"2432-2725","doi":"10.1007/s42001-017-0001-x","url":"https://doi.org/10.1007/s42001-017-0001-x","bibtex":"@ARTICLE{Lerman2017jcss,\nauthor=\"Lerman, Kristina and Marin, Luciano G.\nand Arora, Megha and de Lima, Lucas H. Costa and Ferrara, Emilio and Garcia, David\",\ntitle=\"Language, demographics, emotions, and the structure of online social networks\",\njournal=\"Journal of Computational Social Science\",\nyear=\"2017\",\nmonth=\"Oct\",\nday=\"31\",\nabstract=\"Social networks affect individuals' economic opportunities and well-being. However, few of the factors thought to shape networks---culture, language, education, and income---were empirically validated at scale. To fill this gap, we collected a large number of social media posts from a major US metropolitan area. By associating these posts with US Census tracts through their locations, we linked socioeconomic indicators to group-level signals extracted from social media, including emotions, language, and online social ties. Our analysis shows that tracts with higher education levels have weaker social ties, but this effect is attenuated for tracts with high ratio of Hispanic residents. Negative emotions are associated with more frequent online interactions, or stronger social ties, while positive emotions are associated with weaker ties. These results hold for both Spanish and English tweets, evidencing that language does not affect this relationship between emotion and social ties. Our findings highlight the role of cognitive and demographic factors in online interactions and demonstrate the value of traditional social science sources, like US Census data, within social media studies.\",\nissn=\"2432-2725\",\ndoi=\"10.1007/s42001-017-0001-x\",\nurl=\"https://doi.org/10.1007/s42001-017-0001-x\"\n}\n\n","author_short":["Lerman, K.","Marin, L. G.","Arora, M.","de Lima, L. H. 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