Data · dataset · 2019
Data for: An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit
Listed in Teesside University Research Data Repository
Topic labelled online social network (OSN) data sets are useful to evaluate topic modelling and document clustering tasks.
Description
We provide three data sets with topic labels from two online social networks: Twitter and Reddit. To comply with Twitter’s terms and conditions, we only publish the tweet identifiers along with the topic label.
The Reddit data is supplied with the full text and the topic label. The first Twitter data set was collected from the Twitter API by filtering for the hashtag #Auspol, used to tag political discussion tweets in Australia. The second Twitter data set was originally used in the RepLab 2013 competition and contains expert annotated topics.
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The Reddit data set consists of 40,000 Reddit parent comments from May 2015 belonging to 5 subreddit pages, which are used as topic labels.
Links
Where it is published
- DOI doi.org/10.17632/85njyhj45m.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Humanities · Machine learning · Natural language processing · Social Science
- Inferred from text
- Text 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/85njyhj45m.1 | 15 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:460208 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Natural Language Processing'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:text | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |