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Data · dataset · 2026

Dataset from the Depression Community on Weibo, 2018-2024

Listed in ScienceDB

The study developed a depression risk annotation scheme for social networks and constructed a depression risk prediction model based on deep learning algorithms to dynamically identify depression risks in social networks.

Description

The data for the study was sourced from 451,071 publicly available texts obtained through the keyword "depression supertopic" between January 1, 2018, and May 1, 2024. As the study focused solely on user statements within the depression community, texts not originating from the "depression supertopic" were excluded, resulting in an effective dataset containing 344,817 texts from 55,618 community members, with an average of 6.199 texts per member.

The study respects the legal rights of participants, and the data consists entirely of publicly available user text data acquired through the Weibo open platform, with all information that could be used to trace members strictly removed or concealed.

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Machine learning 71% · Text 75%
Provenance · 1 source records, 13 field assertions
SourceKeyLast seenRaw
ScienceDB10.57760/sciencedb.0875011 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].local:field:social-sciencemapping · scidb cnconnector:scidb_cn@1.0.0
concepts[modality].local:modality:textenrichment · scidb cnkeyword-concept-rules@1.0.0title+description (75%)
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