{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:13:09Z","timestamp":1767651189145,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["19-18-00271"],"award-info":[{"award-number":["19-18-00271"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EPJ Data Sci."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Digital traces have become an essential source of data in social sciences because they provide new insights into human behavior and allow studies to be conducted on a larger scale. One particular area of interest is the estimation of various users\u2019 characteristics from their texts on social media. Although it has been established that basic categorical attributes could be effectively predicted from social media posts, the extent to which it applies to more complex continuous characteristics is less understood. In this research, we used data from a nationally representative panel of students to predict their educational outcomes measured by standardized tests from short texts on a popular Russian social networking site VK. We combined unsupervised learning of word embeddings on a large corpus of VK posts with a simple, supervised model trained on individual posts. The resulting model was able to distinguish between posts written by high- and low-performing students with an accuracy of 94%. We then applied the model to reproduce the ranking of 914 high schools from 3 cities and of the 100 largest universities in Russia. We also showed that the same model could predict academic performance from tweets as well as from VK posts. Finally, we explored predictors of high and low academic performance to obtain insights into the factors associated with different educational outcomes.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-020-00245-8","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T15:05:06Z","timestamp":1598972706000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Estimating educational outcomes from students\u2019 short texts on social media"],"prefix":"10.1140","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8347-6703","authenticated-orcid":false,"given":"Ivan","family":"Smirnov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,1]]},"reference":[{"key":"245_CR1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1146\/annurev-soc-071913-043145","volume":"40","author":"SA Golder","year":"2014","unstructured":"Golder SA, Macy MW (2014) Digital footprints: opportunities and challenges for online social research. Annu Rev Sociol 40:129\u2013152","journal-title":"Annu Rev Sociol"},{"key":"245_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1146\/annurev-soc-060116-053457","volume":"43","author":"D Lazer","year":"2017","unstructured":"Lazer D, Radford J (2017) Data ex machina: introduction to big data. Annu Rev Sociol 43:19\u201339","journal-title":"Annu Rev Sociol"},{"issue":"6264","key":"245_CR3","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1126\/science.aac4420","volume":"350","author":"J Blumenstock","year":"2015","unstructured":"Blumenstock J, Cadamuro G, On R (2015) Predicting poverty and wealth from mobile phone metadata. Science 350(6264):1073\u20131076","journal-title":"Science"},{"issue":"50","key":"245_CR4","doi-asserted-by":"publisher","first-page":"13108","DOI":"10.1073\/pnas.1700035114","volume":"114","author":"T Gebru","year":"2017","unstructured":"Gebru T, Krause J, Wang Y, Chen D, Deng J, Aiden EL, Fei-Fei L (2017) Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proc Natl Acad Sci 114(50):13108\u201313113","journal-title":"Proc Natl Acad Sci"},{"key":"245_CR5","doi-asserted-by":"crossref","unstructured":"Hills TT, Proto E, Sgroi D, Seresinhe CI (2019) Historical analysis of national subjective wellbeing using millions of digitized books. Nat Hum Behav: 1\u20135","DOI":"10.1038\/s41562-019-0781-5"},{"key":"245_CR6","volume-title":"Tenth international AAAI conference on web and social media","author":"J An","year":"2016","unstructured":"An J, Weber I (2016) # greysanatomy vs.# yankees: demographics and hashtag use on Twitter. In: Tenth international AAAI conference on web and social media"},{"issue":"9","key":"245_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0138717","volume":"10","author":"D Preo\u0163iuc-Pietro","year":"2015","unstructured":"Preo\u0163iuc-Pietro D, Volkova S, Lampos V, Bachrach Y, Aletras N (2015) Studying user income through language, behaviour and affect in social media. PLoS ONE 10(9):0138717","journal-title":"PLoS ONE"},{"key":"245_CR8","first-page":"689","volume-title":"European conference on information retrieval","author":"V Lampos","year":"2016","unstructured":"Lampos V, Aletras N, Geyti JK, Zou B, Cox IJ (2016) Inferring the socioeconomic status of social media users based on behaviour and language. In: European conference on information retrieval. Springer, Berlin, pp\u00a0689\u2013695"},{"issue":"9","key":"245_CR9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0073791","volume":"8","author":"HA Schwartz","year":"2013","unstructured":"Schwartz HA, Eichstaedt JC, Kern ML, Dziurzynski L, Ramones SM, Agrawal M, Shah A, Kosinski M, Stillwell D, Seligman ME et al.(2013) Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8(9):73791","journal-title":"PLoS ONE"},{"key":"245_CR10","doi-asserted-by":"crossref","unstructured":"Stier S, Breuer J, Siegers P, Thorson K (2019) Integrating survey data and digital trace data: key issues in developing an emerging field. Soc Sci Comput Rev","DOI":"10.1177\/0894439319843669"},{"issue":"4","key":"245_CR11","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1089\/cyber.2017.0384","volume":"21","author":"M Settanni","year":"2018","unstructured":"Settanni M, Azucar D, Marengo D (2018) Predicting individual characteristics from digital traces on social media: a meta-analysis. Cyberpsychol Behav Soc Netw 21(4):217\u2013228","journal-title":"Cyberpsychol Behav Soc Netw"},{"issue":"1","key":"245_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-0233-7","volume":"3","author":"S Chancellor","year":"2020","unstructured":"Chancellor S, De Choudhury M (2020) Methods in predictive techniques for mental health status on social media: a critical review. NPJ Digit Med 3(1):1\u201311","journal-title":"NPJ Digit Med"},{"issue":"15","key":"245_CR13","doi-asserted-by":"publisher","first-page":"5802","DOI":"10.1073\/pnas.1218772110","volume":"110","author":"M Kosinski","year":"2013","unstructured":"Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci 110(15):5802\u20135805","journal-title":"Proc Natl Acad Sci"},{"key":"245_CR14","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.chb.2017.09.032","volume":"78","author":"O Bogolyubova","year":"2018","unstructured":"Bogolyubova O, Panicheva P, Tikhonov R, Ivanov V, Ledovaya Y (2018) Dark personalities on Facebook: harmful online behaviors and language. Comput Hum Behav 78:151\u2013159","journal-title":"Comput Hum Behav"},{"key":"245_CR15","volume-title":"Seventh international AAAI conference on weblogs and social media","author":"M De Choudhury","year":"2013","unstructured":"De Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. In: Seventh international AAAI conference on weblogs and social media"},{"issue":"1","key":"245_CR16","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-016-0097-x","volume":"6","author":"AG Reece","year":"2017","unstructured":"Reece AG, Danforth CM (2017) Instagram photos reveal predictive markers of depression. EPJ Data Sci 6(1):1","journal-title":"EPJ Data Sci"},{"key":"245_CR17","unstructured":"Organisation for Economic Cooperation and Development (2013) PISA 2012 Assessment and Analytical Framework Mathematics, Reading, Science, Problem Solving and Financial Literacy. OECD Publishing"},{"issue":"2","key":"245_CR18","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.econedurev.2004.04.008","volume":"24","author":"JN Arendt","year":"2005","unstructured":"Arendt JN (2005) Does education cause better health? A panel data analysis using school reforms for identification. Econ Educ Rev 24(2):149\u2013160","journal-title":"Econ Educ Rev"},{"issue":"1","key":"245_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.0963-7214.2004.01301001.x","volume":"13","author":"LS Gottfredson","year":"2004","unstructured":"Gottfredson LS, Deary IJ (2004) Intelligence predicts health and longevity, but why? Curr Dir Psychol Sci 13(1):1\u20134","journal-title":"Curr Dir Psychol Sci"},{"issue":"5","key":"245_CR20","doi-asserted-by":"publisher","DOI":"10.1037\/0021-9010.81.5.548","volume":"81","author":"PL Roth","year":"1996","unstructured":"Roth PL, BeVier CA, Switzer FS III, Schippmann JS (1996) Meta-analyzing the relationship between grades and job performance. J Appl Psychol 81(5):548","journal-title":"J Appl Psychol"},{"issue":"3","key":"245_CR21","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1007\/s10902-012-9369-8","volume":"14","author":"CA Olsson","year":"2013","unstructured":"Olsson CA, McGee R, Nada-Raja S, Williams SM (2013) A 32-year longitudinal study of child and adolescent pathways to well-being in adulthood. J Happ Stud 14(3):1069\u20131083","journal-title":"J Happ Stud"},{"issue":"1","key":"245_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/s41239-020-0177-7","volume":"17","author":"E Alyahyan","year":"2020","unstructured":"Alyahyan E, D\u00fc\u015fteg\u00f6r D (2020) Predicting academic success in higher education: literature review and best practices. Int J Educ Technol Higher Educ 17(1):3","journal-title":"Int J Educ Technol Higher Educ"},{"key":"245_CR23","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1145\/3293881.3295783","volume-title":"Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education","author":"A Hellas","year":"2018","unstructured":"Hellas A, Ihantola P, Petersen A, Ajanovski VV, Gutica M, Hynninen T, Knutas A, Leinonen J, Messom C, Liao SN (2018) Predicting academic performance: a systematic literature review. In: Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education, pp\u00a0175\u2013199"},{"key":"245_CR24","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.chb.2017.12.041","volume":"82","author":"F Giunchiglia","year":"2018","unstructured":"Giunchiglia F, Zeni M, Gobbi E, Bignotti E, Bison I (2018) Mobile social media usage and academic performance. Comput Hum Behav 82:177\u2013185","journal-title":"Comput Hum Behav"},{"key":"245_CR25","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1109\/ICDM.2016.0130","volume-title":"2016 IEEE 16th international conference on data mining (ICDM)","author":"D Lian","year":"2016","unstructured":"Lian D, Ye Y, Zhu W, Liu Q, Xie X, Xiong H (2016) Mutual reinforcement of academic performance prediction and library book recommendation. In: 2016 IEEE 16th international conference on data mining (ICDM). IEEE Press, New York, pp\u00a01023\u20131028"},{"key":"245_CR26","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1145\/2750858.2804251","volume-title":"Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing","author":"R Wang","year":"2015","unstructured":"Wang R, Harari G, Hao P, Zhou X, Campbell AT (2015) Smartgpa: how smartphones can assess and predict academic performance of college students. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, pp\u00a0295\u2013306"},{"key":"245_CR27","doi-asserted-by":"crossref","unstructured":"Kassarnig V, Bjerre-Nielsen A, Mones E, Lehmann S, Lassen DD (2017) Class attendance, peer similarity, and academic performance in a large field study. PLoS ONE 12(11)","DOI":"10.1371\/journal.pone.0187078"},{"key":"245_CR28","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.knosys.2018.07.042","volume":"161","author":"S Helal","year":"2018","unstructured":"Helal S, Li J, Liu L, Ebrahimie E, Dawson S, Murray DJ, Long Q (2018) Predicting academic performance by considering student heterogeneity. Knowl-Based Syst 161:134\u2013146","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"245_CR29","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1332\/175795919X15468755933416","volume":"10","author":"V Malik","year":"2019","unstructured":"Malik V (2019) The Russian panel study\u2019trajectories in education and careers\u2019. Longit Life Course Stud 10(1):125\u2013144","journal-title":"Longit Life Course Stud"},{"key":"245_CR30","unstructured":"Organisation for Economic Cooperation and Development (2014) PISA 2012 Results What Students Know and Can Do. Student Performance in Mathematics, Reading and Science. OECD Publishing"},{"key":"245_CR31","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/BF00973726","volume":"8","author":"WL Sanders","year":"1994","unstructured":"Sanders WL, Horn SP (1994) The Tennessee value-added assessment system (TVAAS): mixed-model methodology in educational assessment. J Pers Eval Educ 8:299\u2013311","journal-title":"J Pers Eval Educ"},{"key":"245_CR32","unstructured":"Schleicher A, Zimmer K, Evans J, Clements N (2009) Pisa 2009 assessment framework: key competencies in reading, mathematics and science. OECD Publishing (NJ1)"},{"issue":"2","key":"245_CR33","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1177\/1073191113514104","volume":"21","author":"ML Kern","year":"2014","unstructured":"Kern ML, Eichstaedt JC, Schwartz HA, Dziurzynski L, Ungar LH, Stillwell DJ, Kosinski M, Ramones SM, Seligman ME (2014) The online social self: an open vocabulary approach to personality. Assessment 21(2):158\u2013169","journal-title":"Assessment"},{"issue":"11","key":"245_CR34","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0201703","volume":"13","author":"V Kulkarni","year":"2018","unstructured":"Kulkarni V, Kern ML, Stillwell D, Kosinski M, Matz S, Ungar L, Skiena S, Schwartz HA (2018) Latent human traits in the language of social media: an open-vocabulary approach. PLoS ONE 13(11):0201703","journal-title":"PLoS ONE"},{"key":"245_CR35","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135\u2013146","journal-title":"Trans Assoc Comput Linguist"},{"key":"245_CR36","doi-asserted-by":"publisher","unstructured":"Smirnov I Predicting academic performance from short texts on social media. https:\/\/doi.org\/10.17605\/OSF.IO\/9PBKR","DOI":"10.17605\/OSF.IO\/9PBKR"},{"key":"245_CR37","unstructured":"Raghu M, Schmidt E (2020) A survey of deep learning for scientific discovery. arXiv preprint. arXiv:2003.11755"},{"key":"245_CR38","unstructured":"Schools of Saint Petersburg: Schools of Saint Petersburg. https:\/\/shkola-spb.ru\/"},{"key":"245_CR39","unstructured":"Zeus: Zeus. http:\/\/zeus.volgamonitor.com\/"},{"key":"245_CR40","unstructured":"Higher School of Economics: Quality of University Admission. https:\/\/ege.hse.ru\/"},{"issue":"4","key":"245_CR41","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1093\/sf\/soz113","volume":"98","author":"M Jackson","year":"2020","unstructured":"Jackson M, Khavenson T, Chirkina T (2020) Raising the stakes: inequality and testing in the Russian education system. Soc Forces 98(4):1613\u20131635","journal-title":"Soc Forces"},{"key":"245_CR42","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten LVD, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"245_CR43","unstructured":"Open Data University Research Consortium. https:\/\/opendata.university\/en\/"}],"container-title":["EPJ Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-020-00245-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1140\/epjds\/s13688-020-00245-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1140\/epjds\/s13688-020-00245-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:06:10Z","timestamp":1630454770000},"score":1,"resource":{"primary":{"URL":"https:\/\/epjdatascience.springeropen.com\/articles\/10.1140\/epjds\/s13688-020-00245-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,1]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["245"],"URL":"https:\/\/doi.org\/10.1140\/epjds\/s13688-020-00245-8","relation":{},"ISSN":["2193-1127"],"issn-type":[{"type":"electronic","value":"2193-1127"}],"subject":[],"published":{"date-parts":[[2020,9,1]]},"assertion":[{"value":"25 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"27"}}