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{ "family_name": "Cecchi", "given_name": "Guillermo", "name": "Cecchi, Guillermo", "type": "personal" } }, { "affiliations": [ { "name": "MIT & Harvard Medical School" } ], "person_or_org": { "family_name": "Ghosh", "given_name": "Satrajit S.", "name": "Ghosh, Satrajit S.", "type": "personal" } } ], "description": "
\n\n
This dataset contains posts from 28 subreddits (15 mental health support groups) from 2018-2020. We used this dataset to understand the impact of COVID-19 on mental health support groups from January to April, 2020 and included older timeframes to obtain baseline posts before COVID-19.
\n\nPlease cite if you use this dataset:
\n\nLow, D. M., Rumker, L., Torous, J., Cecchi, G., Ghosh, S. S., & Talkar, T. (2020). Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study. Journal of medical Internet research, 22(10), e22635.
\n\n@article{low2020natural,\n title={Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study},\n author={Low, Daniel M and Rumker, Laurie and Torous, John and Cecchi, Guillermo and Ghosh, Satrajit S and Talkar, Tanya},\n journal={Journal of medical Internet research},\n volume={22},\n number={10},\n pages={e22635},\n year={2020},\n publisher={JMIR Publications Inc., Toronto, Canada}\n}\n\n
\nLicense
This dataset is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/
\n\nIt was downloaded using pushshift API. Re-use of this data is subject to Reddit API terms.
\n\n\n\n
Reddit Mental Health Dataset
\n\nContains posts and text features for the following timeframes from 28 mental health and non-mental health subreddits:
\n\nfilenames
and corresponding timeframes:
post:
Jan 1 to April 20, 2020 (called "mid-pandemic" in manuscript; r/COVID19_support appears). Unique users: 320,364. pre:
Dec 2018 to Dec 2019. A full year which provides more data for a baseline of Reddit posts. Unique users: 327,289.2019:
Jan 1 to April 20, 2019 (r/EDAnonymous appears). A control for seasonal fluctuations to match post
data. Unique users: 282,560.2018:
Jan 1 to April 20, 2018. A control for seasonal fluctuations to match post
data. Unique users: 177,089Unique users across all time windows (pre and 2019 overlap): 826,961.
\n\nSee manuscript Supplementary Materials (https://doi.org/10.31234/osf.io/xvwcy) for more information.
\n\nNote: if subsampling (e.g., to balance subreddits), we recommend bootstrapping analyses for unbiased results.
\n\n", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2020-07-13", "publisher": "Zenodo", "related_identifiers": [ { "identifier": "10.17605/OSF.IO/7PEYQ", "relation_type": { "id": "isidenticalto", "title": { "de": "Ist identisch mit", "en": "Is identical to" } }, "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "scheme": "doi" }, { "identifier": "10.31234/osf.io/xvwcy", "relation_type": { "id": "isdocumentedby", "title": { "de": "Wird dokumentiert von", "en": "Is documented by" } }, "resource_type": { "id": "publication-preprint", "title": { "de": "Preprint", "en": "Preprint" } }, "scheme": "doi" } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "" }, "id": "pddl-1.0", "props": { "scheme": "spdx", "url": "http://opendatacommons.org/licenses/pddl/1.0/" }, "title": { "en": "ODC Public Domain Dedication & License 1.0" } } ], "subjects": [ { "subject": "Natural Language Processing" }, { "subject": "Mental Health" }, { "subject": "Psychiatry" }, { "subject": "COVID-19" }, { "subject": "Reddit" }, { "subject": "Social Media" } ], "title": "Reddit Mental Health Dataset", "version": "01" }, "parent": { "access": { "owned_by": { "user": 111576 } }, "communities": { "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2020-03-16T11:40:44.487619+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "10f33f78-3f29-41b6-bb10-f757a8f03cb8", "links": {}, "metadata": { "curation_policy": "
The Coronavirus Disease Research Community - COVID-19 is curated by a selected team of experts nominated by OpenAIRE* (see list below). Each time a Zenodo user wants to add a record into the community, an email is sent to the curators that will decide whether to include the record or not.
\r\n\r\nOnly records that may be relevant to the Corona Virus Disease (COVID-19) or the SARS-CoV-2 should be included in this community. The Community curators are not able to edit records, therefore they may ask the corresponding authors to modify the record metadata when necessary, to provide the readers/users with more detailed information according to the FAIR principle of Open Science.
\r\n\r\nIf after its acceptance, a record is subsequently found not to be compliant, we reserve the right to remove it from the community.
\r\n\r\nThe curation team is reachable through the following email address for further clarification or information: covid19@openaire.eu.
\r\n\r\nCurator List:
\r\n\r\n* OpenAIRE: open access and open science training and support since 2009. OpenAIRE is the largest aggregator of European Commission funded research outputs and beyond, also delivering on-demand services for research communities.
\r\n", "page": "This community collects research outputs that may be relevant to the Coronavirus Disease (COVID-19) or the SARS-CoV-2. Scientists are encouraged to upload their outcome in this collection to facilitate sharing and discovery of information. Although Open Access articles and datasets are recommended, also closed and restricted access material are accepted. All types of research outputs can be included in this Community (Publication, Poster, Presentation, Dataset, Image, Video/Audio, Software, Lesson, Other).
\r\n\r\nThe recent Corona Virus Disease (COVID-19) outbreak is requiring unseen efforts of collaboration of the scientific community that need to act fast and to share results in an unpredictable manner. In order to facilitate the Scientist efforts, this community was created to collect all research results that could be relevant for the scientific community working on the Corona Virus Disease (COVID-19) and SARS-CoV-2.
\r\n\r\nAlthough Open Access articles and datasets are recommended, also closed and restricted access material are accepted. All types of research outputs can be included in this Community (Publication, Poster, Presentation, Dataset, Image, Video/Audio, Software, Lesson, Other).
\r\n\r\nWhen depositing a resource that is linked to other resources (not limited to the records deposited in Zenodo but also in other repositories), please make sure that your record is linked to all the other related elements already available, in order to adhere to the FAIR principles of Open Science to maximise the reusability of research results.
\r\n\r\n", "title": "Coronavirus Disease Research Community - COVID-19" }, "revision_id": 0, "slug": "covid-19", "updated": "2021-02-23T14:39:53.029415+00:00" }, { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2019-04-12T13:51:58.685710+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "3eec7ae6-7230-439d-b9eb-58b23297fa67", "links": {}, "metadata": { "curation_policy": "
Manual curation.
\r\n", "page": "The idea of natural language processing, short NLP, is to process texts and to understand the texts' conveyed meaning.
\r\n\r\nNatural Language Understanding can be seen as a subtask of Natural Language Processing. However, those terms are often used synonymously.
\r\n\r\nhttps://en.wikipedia.org/wiki/Natural_language_processing
", "title": "Natural Language Processing" }, "revision_id": 0, "slug": "natural-language-processing", "updated": "2020-06-08T21:07:14.336530+00:00" }, { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2018-11-20T09:59:47.825777+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "629a6594-ebed-4f9d-aa2a-16766d76d068", "links": {}, "metadata": { "description": "Freely accessible collection of diverse resources including corpora, guidelines, publications, gazetteers and lexical resources related to health and biomedical/clinical natural language processing, text mining and language models.", "organizations": [ { "id": "05sd8tv96" } ], "page": "A considerable amount of medically relevant information is hidden in large unstructured heterogeneous data collections, such as the medical literature, medicinal patents, electronic health records or specialized web-content (health blogs, patient forums or information generated by scientific and medical societies). To process more efficiently medical big data there is a growing interest in exploiting natural language processing and text mining approaches, in particularly deep learning and artificial intelligence-based strategies.
\n\nA considerable amount of medically relevant information is hidden in large unstructured heterogeneous data collections, such as the medical literature, medicinal patents, electronic health records or specialized web-content (health blogs, patient forums or information generated by scientific and medical societies). To process more efficiently medical big data there is a growing interest in exploiting natural language processing and text mining approaches, in particularly deep learning and artificial intelligence-based strategies.
\n\nThe aim of the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL), the Spanish national Plan for the Advancement of Language Technology, is to promote the development of resources of critical importance for processing textual data in Spanish as well as Catalan, Basque and Galician. The Health and biomedical domain constitute one of the flagship topics of the Spanish Plan TL.
\n\nTo promote the development of health-related language technology applications, the Plan TL is both developing and identifying resources of key relevance including individual components/libraries, terminological resources, annotated corpora and annotation guidelines, as well as document collections and language models.
", "title": "Medical NLP (maintained by NLP4BIA unit at BSC)\u2013 language technology resources for clinical and biomedical documents in multiple languages", "type": { "id": "topic" }, "website": "https://www.bsc.es/discover-bsc/organisation/research-departments/nlp-biomedical-information-analysis" }, "revision_id": 1, "slug": "medicalnlp", "updated": "2023-11-07T07:23:33.103958+00:00" } ], "ids": [ "10f33f78-3f29-41b6-bb10-f757a8f03cb8", "3eec7ae6-7230-439d-b9eb-58b23297fa67", "629a6594-ebed-4f9d-aa2a-16766d76d068" ] }, "id": "3941386", "pids": {} }, "pids": { "doi": { "identifier": "10.17605/OSF.IO/7PEYQ", "provider": "external" }, "oai": { "identifier": "oai:zenodo.org:3941387", "provider": "oai" } }, "revision_id": 8, "stats": { "all_versions": { "data_volume": 1215495935689.0, "downloads": 49184, "unique_downloads": 21912, "unique_views": 15101, "views": 17113 }, "this_version": { "data_volume": 1201711536186.0, "downloads": 48608, "unique_downloads": 21814, "unique_views": 14821, "views": 16807 } }, "status": "published", "updated": "2020-10-16T22:39:49.972723+00:00", "versions": { "index": 1, "is_latest": true } }