Datasets:
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
- nl
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-label-classification
pretty_name: Dutch Social Media Collection
dataset_info:
features:
- name: full_text
dtype: string
- name: text_translation
dtype: string
- name: screen_name
dtype: string
- name: description
dtype: string
- name: desc_translation
dtype: string
- name: location
dtype: string
- name: weekofyear
dtype: int64
- name: weekday
dtype: int64
- name: month
dtype: int64
- name: year
dtype: int64
- name: day
dtype: int64
- name: point_info
dtype: string
- name: point
dtype: string
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: altitude
dtype: float64
- name: province
dtype: string
- name: hisco_standard
dtype: string
- name: hisco_code
dtype: string
- name: industry
dtype: bool_
- name: sentiment_pattern
dtype: float64
- name: subjective_pattern
dtype: float64
- name: label
dtype:
class_label:
names:
'0': neg
'1': neu
'2': pos
config_name: dutch_social
splits:
- name: train
num_bytes: 105569586
num_examples: 162805
- name: test
num_bytes: 35185351
num_examples: 54268
- name: validation
num_bytes: 34334756
num_examples: 54269
download_size: 68740666
dataset_size: 175089693
Dataset Card for Dutch Social Media Collection
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: Dutch Social Media Collection
- Repository:
- Paper: (in-progress) https://doi.org/10.5072/FK2/MTPTL7
- Leaderboard:
- Point of Contact: Aakash Gupta
Dataset Summary
The dataset contains 10 files with around 271,342 tweets. The tweets are filtered via the official Twitter API to contain tweets in Dutch language or by users who have specified their location information within Netherlands geographical boundaries. Using natural language processing we have classified the tweets for their HISCO codes. If the user has provided their location within Dutch boundaries, we have also classified them to their respective provinces The objective of this dataset is to make research data available publicly in a FAIR (Findable, Accessible, Interoperable, Reusable) way. Twitter's Terms of Service Licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (2020-10-27)
Supported Tasks and Leaderboards
sentiment analysis, multi-label classification, entity-extraction
Languages
The text is primarily in Dutch with some tweets in English and other languages. The BCP 47 code is nl and en
Dataset Structure
Data Instances
An example of the data field will be:
{
"full_text": "@pflegearzt @Friedelkorn @LAguja44 Pardon, wollte eigentlich das zitieren: \nhttps://t.co/ejO7bIMyj8\nMeine mentions sind inzw komplett undurchschaubar weil da Leute ihren supporterclub zwecks Likes zusammengerufen haben.",
"text_translation": "@pflegearzt @Friedelkorn @ LAguja44 Pardon wollte zitieren eigentlich das:\nhttps://t.co/ejO7bIMyj8\nMeine mentions inzw sind komplett undurchschaubar weil da Leute ihren supporter club Zwecks Likes zusammengerufen haben.",
"created_at": 1583756789000,
"screen_name": "TheoRettich",
"description": "I ❤️science, therefore a Commie. ☭ FALGSC: Part of a conspiracy which wants to achieve world domination. Tankie-Cornucopian. Ecology is a myth",
"desc_translation": "I ❤️science, Therefore a Commie. ☭ FALGSC: Part of a conspiracy How many followers wants to Achieve World Domination. Tankie-Cornucopian. Ecology is a myth",
"weekofyear": 11,
"weekday": 0,
"day": 9,
"month": 3,
"year": 2020,
"location": "Netherlands",
"point_info": "Nederland",
"point": "(52.5001698, 5.7480821, 0.0)",
"latitude": 52.5001698,
"longitude": 5.7480821,
"altitude": 0,
"province": "Flevoland",
"hisco_standard": null,
"hisco_code": null,
"industry": false,
"sentiment_pattern": 0,
"subjective_pattern": 0
}
Data Fields
| Column Name | Description |
|---|---|
| full_text | Original text in the tweet |
| text_translation | English translation of the full text |
| created_at | Date of tweet creation |
| screen_name | username of the tweet author |
| description | description as provided in the users bio |
| desc_translation | English translation of user's bio/ description |
| location | Location information as provided in the user's bio |
| weekofyear | week of the year |
| weekday | Day of the week information; Monday=0....Sunday = 6 |
| month | Month of tweet creation |
| year | year of tweet creation |
| day | day of tweet creation |
| point_info | point information from location columnd |
| point | tuple giving lat, lon & altitude information |
| latitude | geo-referencing information derived from location data |
| longitude | geo-referencing information derived from location data |
| altitude | geo-referencing information derived from location data |
| province | Province given location data of user |
| hisco_standard | HISCO standard key word; if available in tweet |
| hisco_code | HISCO standard code as derived from hisco_standard |
| industry | Whether the tweet talks about industry (True/False) |
| sentiment_score | Sentiment score -1.0 to 1.0 |
| subjectivity_score | Subjectivity scores 0 to 1 |
Missing values are replaced with empty strings or -1 (-100 for missing sentiment_score).
Data Splits
Data has been split into Train: 60%, Validation: 20% and Test: 20%
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
The tweets were hydrated using Twitter's API and then filtered for those which were in Dutch language and/or for users who had mentioned that they were from within Netherlands geographical borders.
Who are the source language producers?
The language producers are twitter users who have identified their location within the geographical boundaries of Netherland. Or those who have tweeted in the dutch language!
Annotations
Using Natural language processing, we have classified the tweets on industry and for HSN HISCO codes. Depending on the user's location, their provincial information is also added. Please check the file/column for detailed information.
The tweets are also classified on the sentiment & subjectivity scores. Sentiment scores are between -1 to +1 Subjectivity scores are between 0 to 1
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
As of writing this data card no anonymization has been carried out on the tweets or user data. As such, if the twitter user has shared any personal & sensitive information, then it may be available in this dataset.
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
Additional Information
Dataset Curators
Aakash Gupta Th!nkEvolve Consulting and Researcher at CoronaWhy
Licensing Information
CC BY-NC 4.0
Citation Information
@data{FK2/MTPTL7_2020, author = {Gupta, Aakash}, publisher = {COVID-19 Data Hub}, title = {{Dutch social media collection}}, year = {2020}, version = {DRAFT VERSION}, doi = {10.5072/FK2/MTPTL7}, url = {https://doi.org/10.5072/FK2/MTPTL7} }
Contributions
Thanks to @skyprince999 for adding this dataset.