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PMC:7143846 / 5616-9107 JSONTXT

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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
89 31-43 Species denotes Participants Tax:9606
91 1384-1391 Disease denotes anxiety MESH:D001007
95 2439-2465 Disease denotes deep learning technologies MESH:D007859
96 2499-2506 Disease denotes anxiety MESH:D001007
97 2508-2518 Disease denotes depression MESH:D000275
101 2977-2984 Disease denotes anxiety MESH:D001007
102 2986-2996 Disease denotes depression MESH:D000275
103 3280-3288 Disease denotes COVID-19 MESH:C000657245

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T30 1384-1391 Disease denotes anxiety http://purl.obolibrary.org/obo/MONDO_0005618|http://purl.obolibrary.org/obo/MONDO_0011918
T32 2499-2518 Disease denotes anxiety, depression http://purl.obolibrary.org/obo/MONDO_0041086
T33 2499-2506 Disease denotes anxiety http://purl.obolibrary.org/obo/MONDO_0005618|http://purl.obolibrary.org/obo/MONDO_0011918
T35 2508-2518 Disease denotes depression http://purl.obolibrary.org/obo/MONDO_0002050
T36 2977-2996 Disease denotes anxiety, depression http://purl.obolibrary.org/obo/MONDO_0041086
T37 2977-2984 Disease denotes anxiety http://purl.obolibrary.org/obo/MONDO_0005618|http://purl.obolibrary.org/obo/MONDO_0011918
T39 2986-2996 Disease denotes depression http://purl.obolibrary.org/obo/MONDO_0002050
T40 3280-3288 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T28 182-188 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T29 211-212 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T30 283-293 http://purl.obolibrary.org/obo/CLO_0001658 denotes activities
T31 331-333 http://purl.obolibrary.org/obo/CLO_0050510 denotes 18
T32 675-681 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T33 780-781 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T34 1023-1029 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T35 1674-1676 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T36 1935-1942 http://purl.obolibrary.org/obo/BFO_0000030 denotes objects
T37 2322-2328 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T38 2768-2769 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T39 2855-2858 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T40 3337-3341 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T41 3400-3402 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T2 2707-2716 Chemical denotes indicator http://purl.obolibrary.org/obo/CHEBI_47867

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T7 1384-1391 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T8 2499-2506 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T9 2508-2518 Phenotype denotes depression http://purl.obolibrary.org/obo/HP_0000716
T10 2977-2984 Phenotype denotes anxiety http://purl.obolibrary.org/obo/HP_0000739
T11 2986-2996 Phenotype denotes depression http://purl.obolibrary.org/obo/HP_0000716

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T9 408-417 http://purl.obolibrary.org/obo/GO_0007610 denotes behaviors
T10 1519-1535 http://purl.obolibrary.org/obo/GO_0048731 denotes system developed
T11 1702-1714 http://purl.obolibrary.org/obo/GO_0035282 denotes segmentation
T12 1787-1799 http://purl.obolibrary.org/obo/GO_0035282 denotes segmentation
T13 2444-2452 http://purl.obolibrary.org/obo/GO_0007612 denotes learning

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T36 0-2 Sentence denotes 2.
T37 3-24 Sentence denotes Materials and Methods
T38 26-30 Sentence denotes 2.1.
T39 31-63 Sentence denotes Participants and Data Collection
T40 64-134 Sentence denotes The samples in this study were from the original Weibo data pool [17].
T41 135-201 Sentence denotes The data pool contained more than 1.16 million active Weibo users.
T42 202-335 Sentence denotes Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].
T43 336-442 Sentence denotes The retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages.
T44 443-537 Sentence denotes Privacy was strictly protected during the procedure, referring to the ethical principles [19].
T45 538-617 Sentence denotes We have obtained the Ethical Committee’s approval and the ethic code is H15009.
T46 618-713 Sentence denotes The following inclusion criteria were employed to select active Weibo users from the data pool.
T47 714-840 Sentence denotes First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020.
T48 841-926 Sentence denotes Second, their authentication type is non-institutional (e.g., individual user, etc.).
T49 927-1003 Sentence denotes Third, their regional authentication is in China, not “overseas” or “other”.
T50 1004-1185 Sentence denotes We 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.
T51 1187-1191 Sentence denotes 2.2.
T52 1192-1242 Sentence denotes Measurement of Psychological Traits and Procedures
T53 1243-1496 Sentence denotes In 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.
T54 1497-1761 Sentence denotes We 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].
T55 1762-2092 Sentence denotes 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].
T56 2093-2162 Sentence denotes These lexical features were data sources for word frequency analysis.
T57 2163-2341 Sentence denotes After feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users.
T58 2342-2633 Sentence denotes 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.
T59 2634-2728 Sentence denotes Figure 1 portrays the procedure from feature extraction to psychological indicator prediction.
T60 2729-2817 Sentence denotes All the prediction models have reached a moderate correlation with questionnaire scores.
T61 2818-2899 Sentence denotes The feasibility of predictive models has been repeatedly demonstrated [26,27,28].
T62 2900-3165 Sentence denotes We 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.
T63 3166-3491 Sentence denotes 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.

2_test

Id Subject Object Predicate Lexical cue
32204411-26348336-49451142 533-535 26348336 denotes 19
32204411-27322382-49451143 1757-1759 27322382 denotes 23
32204411-27322382-49451144 2088-2090 27322382 denotes 23
32204411-28059682-49451145 2895-2897 28059682 denotes 28
T34506 533-535 26348336 denotes 19
T70343 1757-1759 27322382 denotes 23
T61461 2088-2090 27322382 denotes 23
T27610 2895-2897 28059682 denotes 28