PMC:7143846 / 5616-9107 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"89","span":{"begin":31,"end":43},"obj":"Species"},{"id":"91","span":{"begin":1384,"end":1391},"obj":"Disease"},{"id":"95","span":{"begin":2439,"end":2465},"obj":"Disease"},{"id":"96","span":{"begin":2499,"end":2506},"obj":"Disease"},{"id":"97","span":{"begin":2508,"end":2518},"obj":"Disease"},{"id":"101","span":{"begin":2977,"end":2984},"obj":"Disease"},{"id":"102","span":{"begin":2986,"end":2996},"obj":"Disease"},{"id":"103","span":{"begin":3280,"end":3288},"obj":"Disease"}],"attributes":[{"id":"A89","pred":"tao:has_database_id","subj":"89","obj":"Tax:9606"},{"id":"A91","pred":"tao:has_database_id","subj":"91","obj":"MESH:D001007"},{"id":"A95","pred":"tao:has_database_id","subj":"95","obj":"MESH:D007859"},{"id":"A96","pred":"tao:has_database_id","subj":"96","obj":"MESH:D001007"},{"id":"A97","pred":"tao:has_database_id","subj":"97","obj":"MESH:D000275"},{"id":"A101","pred":"tao:has_database_id","subj":"101","obj":"MESH:D001007"},{"id":"A102","pred":"tao:has_database_id","subj":"102","obj":"MESH:D000275"},{"id":"A103","pred":"tao:has_database_id","subj":"103","obj":"MESH:C000657245"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"2. Materials and Methods\n\n2.1. Participants and Data Collection\nThe samples in this study were from the original Weibo data pool [17]. The data pool contained more than 1.16 million active Weibo users. Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].\nThe retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages. Privacy was strictly protected during the procedure, referring to the ethical principles [19]. We have obtained the Ethical Committee’s approval and the ethic code is H15009.\nThe following inclusion criteria were employed to select active Weibo users from the data pool. First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020. Second, their authentication type is non-institutional (e.g., individual user, etc.). Third, their regional authentication is in China, not “overseas” or “other”.\nWe acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.\n\n2.2. Measurement of Psychological Traits and Procedures\nIn this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.\nWe employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23]. We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23]. These lexical features were data sources for word frequency analysis.\nAfter feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. All the prediction models have reached a moderate correlation with questionnaire scores. The feasibility of predictive models has been repeatedly demonstrated [26,27,28].\nWe calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages. We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA."}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T30","span":{"begin":1384,"end":1391},"obj":"Disease"},{"id":"T32","span":{"begin":2499,"end":2518},"obj":"Disease"},{"id":"T33","span":{"begin":2499,"end":2506},"obj":"Disease"},{"id":"T35","span":{"begin":2508,"end":2518},"obj":"Disease"},{"id":"T36","span":{"begin":2977,"end":2996},"obj":"Disease"},{"id":"T37","span":{"begin":2977,"end":2984},"obj":"Disease"},{"id":"T39","span":{"begin":2986,"end":2996},"obj":"Disease"},{"id":"T40","span":{"begin":3280,"end":3288},"obj":"Disease"}],"attributes":[{"id":"A30","pred":"mondo_id","subj":"T30","obj":"http://purl.obolibrary.org/obo/MONDO_0005618"},{"id":"A31","pred":"mondo_id","subj":"T30","obj":"http://purl.obolibrary.org/obo/MONDO_0011918"},{"id":"A32","pred":"mondo_id","subj":"T32","obj":"http://purl.obolibrary.org/obo/MONDO_0041086"},{"id":"A33","pred":"mondo_id","subj":"T33","obj":"http://purl.obolibrary.org/obo/MONDO_0005618"},{"id":"A34","pred":"mondo_id","subj":"T33","obj":"http://purl.obolibrary.org/obo/MONDO_0011918"},{"id":"A35","pred":"mondo_id","subj":"T35","obj":"http://purl.obolibrary.org/obo/MONDO_0002050"},{"id":"A36","pred":"mondo_id","subj":"T36","obj":"http://purl.obolibrary.org/obo/MONDO_0041086"},{"id":"A37","pred":"mondo_id","subj":"T37","obj":"http://purl.obolibrary.org/obo/MONDO_0005618"},{"id":"A38","pred":"mondo_id","subj":"T37","obj":"http://purl.obolibrary.org/obo/MONDO_0011918"},{"id":"A39","pred":"mondo_id","subj":"T39","obj":"http://purl.obolibrary.org/obo/MONDO_0002050"},{"id":"A40","pred":"mondo_id","subj":"T40","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"2. Materials and Methods\n\n2.1. Participants and Data Collection\nThe samples in this study were from the original Weibo data pool [17]. The data pool contained more than 1.16 million active Weibo users. Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].\nThe retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages. Privacy was strictly protected during the procedure, referring to the ethical principles [19]. We have obtained the Ethical Committee’s approval and the ethic code is H15009.\nThe following inclusion criteria were employed to select active Weibo users from the data pool. First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020. Second, their authentication type is non-institutional (e.g., individual user, etc.). Third, their regional authentication is in China, not “overseas” or “other”.\nWe acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.\n\n2.2. Measurement of Psychological Traits and Procedures\nIn this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.\nWe employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23]. We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23]. These lexical features were data sources for word frequency analysis.\nAfter feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. All the prediction models have reached a moderate correlation with questionnaire scores. The feasibility of predictive models has been repeatedly demonstrated [26,27,28].\nWe calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages. We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T28","span":{"begin":182,"end":188},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T29","span":{"begin":211,"end":212},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T30","span":{"begin":283,"end":293},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T31","span":{"begin":331,"end":333},"obj":"http://purl.obolibrary.org/obo/CLO_0050510"},{"id":"T32","span":{"begin":675,"end":681},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T33","span":{"begin":780,"end":781},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T34","span":{"begin":1023,"end":1029},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T35","span":{"begin":1674,"end":1676},"obj":"http://purl.obolibrary.org/obo/CLO_0050507"},{"id":"T36","span":{"begin":1935,"end":1942},"obj":"http://purl.obolibrary.org/obo/BFO_0000030"},{"id":"T37","span":{"begin":2322,"end":2328},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T38","span":{"begin":2768,"end":2769},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T39","span":{"begin":2855,"end":2858},"obj":"http://purl.obolibrary.org/obo/CLO_0051582"},{"id":"T40","span":{"begin":3337,"end":3341},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T41","span":{"begin":3400,"end":3402},"obj":"http://purl.obolibrary.org/obo/CLO_0050507"}],"text":"2. Materials and Methods\n\n2.1. Participants and Data Collection\nThe samples in this study were from the original Weibo data pool [17]. The data pool contained more than 1.16 million active Weibo users. Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].\nThe retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages. Privacy was strictly protected during the procedure, referring to the ethical principles [19]. We have obtained the Ethical Committee’s approval and the ethic code is H15009.\nThe following inclusion criteria were employed to select active Weibo users from the data pool. First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020. Second, their authentication type is non-institutional (e.g., individual user, etc.). Third, their regional authentication is in China, not “overseas” or “other”.\nWe acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.\n\n2.2. Measurement of Psychological Traits and Procedures\nIn this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.\nWe employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23]. We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23]. These lexical features were data sources for word frequency analysis.\nAfter feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. All the prediction models have reached a moderate correlation with questionnaire scores. The feasibility of predictive models has been repeatedly demonstrated [26,27,28].\nWe calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages. We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T2","span":{"begin":2707,"end":2716},"obj":"Chemical"}],"attributes":[{"id":"A2","pred":"chebi_id","subj":"T2","obj":"http://purl.obolibrary.org/obo/CHEBI_47867"}],"text":"2. Materials and Methods\n\n2.1. Participants and Data Collection\nThe samples in this study were from the original Weibo data pool [17]. The data pool contained more than 1.16 million active Weibo users. Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].\nThe retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages. Privacy was strictly protected during the procedure, referring to the ethical principles [19]. We have obtained the Ethical Committee’s approval and the ethic code is H15009.\nThe following inclusion criteria were employed to select active Weibo users from the data pool. First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020. Second, their authentication type is non-institutional (e.g., individual user, etc.). Third, their regional authentication is in China, not “overseas” or “other”.\nWe acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.\n\n2.2. Measurement of Psychological Traits and Procedures\nIn this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.\nWe employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23]. We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23]. These lexical features were data sources for word frequency analysis.\nAfter feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. All the prediction models have reached a moderate correlation with questionnaire scores. The feasibility of predictive models has been repeatedly demonstrated [26,27,28].\nWe calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages. We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA."}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T7","span":{"begin":1384,"end":1391},"obj":"Phenotype"},{"id":"T8","span":{"begin":2499,"end":2506},"obj":"Phenotype"},{"id":"T9","span":{"begin":2508,"end":2518},"obj":"Phenotype"},{"id":"T10","span":{"begin":2977,"end":2984},"obj":"Phenotype"},{"id":"T11","span":{"begin":2986,"end":2996},"obj":"Phenotype"}],"attributes":[{"id":"A7","pred":"hp_id","subj":"T7","obj":"http://purl.obolibrary.org/obo/HP_0000739"},{"id":"A8","pred":"hp_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/HP_0000739"},{"id":"A9","pred":"hp_id","subj":"T9","obj":"http://purl.obolibrary.org/obo/HP_0000716"},{"id":"A10","pred":"hp_id","subj":"T10","obj":"http://purl.obolibrary.org/obo/HP_0000739"},{"id":"A11","pred":"hp_id","subj":"T11","obj":"http://purl.obolibrary.org/obo/HP_0000716"}],"text":"2. Materials and Methods\n\n2.1. Participants and Data Collection\nThe samples in this study were from the original Weibo data pool [17]. The data pool contained more than 1.16 million active Weibo users. Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].\nThe retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages. Privacy was strictly protected during the procedure, referring to the ethical principles [19]. We have obtained the Ethical Committee’s approval and the ethic code is H15009.\nThe following inclusion criteria were employed to select active Weibo users from the data pool. First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020. Second, their authentication type is non-institutional (e.g., individual user, etc.). Third, their regional authentication is in China, not “overseas” or “other”.\nWe acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.\n\n2.2. Measurement of Psychological Traits and Procedures\nIn this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.\nWe employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23]. We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23]. These lexical features were data sources for word frequency analysis.\nAfter feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. All the prediction models have reached a moderate correlation with questionnaire scores. The feasibility of predictive models has been repeatedly demonstrated [26,27,28].\nWe calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages. We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA."}

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T9","span":{"begin":408,"end":417},"obj":"http://purl.obolibrary.org/obo/GO_0007610"},{"id":"T10","span":{"begin":1519,"end":1535},"obj":"http://purl.obolibrary.org/obo/GO_0048731"},{"id":"T11","span":{"begin":1702,"end":1714},"obj":"http://purl.obolibrary.org/obo/GO_0035282"},{"id":"T12","span":{"begin":1787,"end":1799},"obj":"http://purl.obolibrary.org/obo/GO_0035282"},{"id":"T13","span":{"begin":2444,"end":2452},"obj":"http://purl.obolibrary.org/obo/GO_0007612"}],"text":"2. Materials and Methods\n\n2.1. Participants and Data Collection\nThe samples in this study were from the original Weibo data pool [17]. The data pool contained more than 1.16 million active Weibo users. Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].\nThe retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages. Privacy was strictly protected during the procedure, referring to the ethical principles [19]. We have obtained the Ethical Committee’s approval and the ethic code is H15009.\nThe following inclusion criteria were employed to select active Weibo users from the data pool. First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020. Second, their authentication type is non-institutional (e.g., individual user, etc.). Third, their regional authentication is in China, not “overseas” or “other”.\nWe acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.\n\n2.2. Measurement of Psychological Traits and Procedures\nIn this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.\nWe employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23]. We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23]. These lexical features were data sources for word frequency analysis.\nAfter feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. All the prediction models have reached a moderate correlation with questionnaire scores. The feasibility of predictive models has been repeatedly demonstrated [26,27,28].\nWe calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages. We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA."}

{"project":"LitCovid-sentences","denotations":[{"id":"T36","span":{"begin":0,"end":2},"obj":"Sentence"},{"id":"T37","span":{"begin":3,"end":24},"obj":"Sentence"},{"id":"T38","span":{"begin":26,"end":30},"obj":"Sentence"},{"id":"T39","span":{"begin":31,"end":63},"obj":"Sentence"},{"id":"T40","span":{"begin":64,"end":134},"obj":"Sentence"},{"id":"T41","span":{"begin":135,"end":201},"obj":"Sentence"},{"id":"T42","span":{"begin":202,"end":335},"obj":"Sentence"},{"id":"T43","span":{"begin":336,"end":442},"obj":"Sentence"},{"id":"T44","span":{"begin":443,"end":537},"obj":"Sentence"},{"id":"T45","span":{"begin":538,"end":617},"obj":"Sentence"},{"id":"T46","span":{"begin":618,"end":713},"obj":"Sentence"},{"id":"T47","span":{"begin":714,"end":840},"obj":"Sentence"},{"id":"T48","span":{"begin":841,"end":926},"obj":"Sentence"},{"id":"T49","span":{"begin":927,"end":1003},"obj":"Sentence"},{"id":"T50","span":{"begin":1004,"end":1185},"obj":"Sentence"},{"id":"T51","span":{"begin":1187,"end":1191},"obj":"Sentence"},{"id":"T52","span":{"begin":1192,"end":1242},"obj":"Sentence"},{"id":"T53","span":{"begin":1243,"end":1496},"obj":"Sentence"},{"id":"T54","span":{"begin":1497,"end":1761},"obj":"Sentence"},{"id":"T55","span":{"begin":1762,"end":2092},"obj":"Sentence"},{"id":"T56","span":{"begin":2093,"end":2162},"obj":"Sentence"},{"id":"T57","span":{"begin":2163,"end":2341},"obj":"Sentence"},{"id":"T58","span":{"begin":2342,"end":2633},"obj":"Sentence"},{"id":"T59","span":{"begin":2634,"end":2728},"obj":"Sentence"},{"id":"T60","span":{"begin":2729,"end":2817},"obj":"Sentence"},{"id":"T61","span":{"begin":2818,"end":2899},"obj":"Sentence"},{"id":"T62","span":{"begin":2900,"end":3165},"obj":"Sentence"},{"id":"T63","span":{"begin":3166,"end":3491},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"2. Materials and Methods\n\n2.1. Participants and Data Collection\nThe samples in this study were from the original Weibo data pool [17]. The data pool contained more than 1.16 million active Weibo users. Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].\nThe retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages. Privacy was strictly protected during the procedure, referring to the ethical principles [19]. We have obtained the Ethical Committee’s approval and the ethic code is H15009.\nThe following inclusion criteria were employed to select active Weibo users from the data pool. First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020. Second, their authentication type is non-institutional (e.g., individual user, etc.). Third, their regional authentication is in China, not “overseas” or “other”.\nWe acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.\n\n2.2. Measurement of Psychological Traits and Procedures\nIn this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.\nWe employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23]. We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23]. These lexical features were data sources for word frequency analysis.\nAfter feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. All the prediction models have reached a moderate correlation with questionnaire scores. The feasibility of predictive models has been repeatedly demonstrated [26,27,28].\nWe calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages. We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA."}

{"project":"2_test","denotations":[{"id":"32204411-26348336-49451142","span":{"begin":533,"end":535},"obj":"26348336"},{"id":"32204411-27322382-49451143","span":{"begin":1757,"end":1759},"obj":"27322382"},{"id":"32204411-27322382-49451144","span":{"begin":2088,"end":2090},"obj":"27322382"},{"id":"32204411-28059682-49451145","span":{"begin":2895,"end":2897},"obj":"28059682"},{"id":"T34506","span":{"begin":533,"end":535},"obj":"26348336"},{"id":"T70343","span":{"begin":1757,"end":1759},"obj":"27322382"},{"id":"T61461","span":{"begin":2088,"end":2090},"obj":"27322382"},{"id":"T27610","span":{"begin":2895,"end":2897},"obj":"28059682"}],"text":"2. Materials and Methods\n\n2.1. Participants and Data Collection\nThe samples in this study were from the original Weibo data pool [17]. The data pool contained more than 1.16 million active Weibo users. Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].\nThe retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages. Privacy was strictly protected during the procedure, referring to the ethical principles [19]. We have obtained the Ethical Committee’s approval and the ethic code is H15009.\nThe following inclusion criteria were employed to select active Weibo users from the data pool. First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020. Second, their authentication type is non-institutional (e.g., individual user, etc.). Third, their regional authentication is in China, not “overseas” or “other”.\nWe acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.\n\n2.2. Measurement of Psychological Traits and Procedures\nIn this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.\nWe employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23]. We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23]. These lexical features were data sources for word frequency analysis.\nAfter feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. All the prediction models have reached a moderate correlation with questionnaire scores. The feasibility of predictive models has been repeatedly demonstrated [26,27,28].\nWe calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages. We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA."}