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README.md

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----
-annotations_creators:
-- crowdsourced
-language_creators:
-- found
-language:
-- en
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets: []
-task_categories:
-- text-classification
-task_ids:
-- fact-checking
-pretty_name: RumourEval 2019
-tags:
-- stance-detection
----
-
-
-
-# Dataset Card for "rumoureval_2019"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://competitions.codalab.org/competitions/19938](https://competitions.codalab.org/competitions/19938)
-- **Repository:** [https://figshare.com/articles/dataset/RumourEval_2019_data/8845580](https://figshare.com/articles/dataset/RumourEval_2019_data/8845580)
-- **Paper:** [https://aclanthology.org/S19-2147/](https://aclanthology.org/S19-2147/), [https://arxiv.org/abs/1809.06683](https://arxiv.org/abs/1809.06683)
-- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
-- **Size of downloaded dataset files:** 
-- **Size of the generated dataset:** 
-- **Total amount of disk used:**  
-
-### Dataset Summary
-
-Stance prediction task in English. The goal is to predict whether a given reply to a claim either supports, denies, questions, or simply comments on the claim. Ran as a SemEval task in 2019.
-
-### Supported Tasks and Leaderboards
-
-* SemEval 2019 task 1
-
-### Languages
-
-English of various origins, bcp47: `en`
-
-## Dataset Structure
-
-### Data Instances
-
-#### polstance
-
-An example of 'train' looks as follows.
-
-```
-{
-	'id': '0', 
-	'source_text': 'Appalled by the attack on Charlie Hebdo in Paris, 10 - probably journalists - now confirmed dead. An attack on free speech everywhere.', 
-	'reply_text': '@m33ryg @tnewtondunn @mehdirhasan Of course it is free speech, that\'s the definition of "free speech" to openly make comments or draw a pic!', 
-	'label': 3
-}
-```
-
-
-### Data Fields
-
-- `id`: a `string` feature.
-- `source_text`: a `string` expressing a claim/topic.
-- `reply_text`: a `string` to be classified for its stance to the source.
-- `label`: a class label representing the stance the text expresses towards the target. Full tagset with indices:
-
-```
-                            0: "support",
-                            1: "deny",
-                            2: "query",
-                            3: "comment"
-```
-- `quoteID`: a `string` of the internal quote ID.
-- `party`: a `string` describing the party affiliation of the quote utterer at the time of utterance.
-- `politician`: a `string` naming the politician who uttered the quote.
-
-### Data Splits
-
-|  name   |instances|
-|---------|----:|
-|train|7 005|
-|dev|2 425|
-|test|2 945|
-
-## Dataset Creation
-
-### Curation Rationale
-
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-
-#### Who are the source language producers?
-
-Twitter users
-
-### Annotations
-
-#### Annotation process
-
-Detailed in [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0150989)
-
-#### Who are the annotators?
-
-
-### Personal and Sensitive Information
-
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-
-### Discussion of Biases
-
-
-### Other Known Limitations
-
-## Additional Information
-
-### Dataset Curators
-
-The dataset is curated by the paper's authors.
-
-### Licensing Information
-
-The authors distribute this data under Creative Commons attribution license, CC-BY 4.0. 
-
-### Citation Information
-
-```
-@inproceedings{gorrell-etal-2019-semeval,
-    title = "{S}em{E}val-2019 Task 7: {R}umour{E}val, Determining Rumour Veracity and Support for Rumours",
-    author = "Gorrell, Genevieve  and
-      Kochkina, Elena  and
-      Liakata, Maria  and
-      Aker, Ahmet  and
-      Zubiaga, Arkaitz  and
-      Bontcheva, Kalina  and
-      Derczynski, Leon",
-    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
-    month = jun,
-    year = "2019",
-    address = "Minneapolis, Minnesota, USA",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/S19-2147",
-    doi = "10.18653/v1/S19-2147",
-    pages = "845--854",
-}
-```
-
-
-### Contributions
-
-Author-added dataset [@leondz](https://github.com/leondz)

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dataset_infos.json

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-{"RumourEval2019": {"description": "This new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "source_text": {"dtype": "string", "id": null, "_type": "Value"}, "reply_text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 4, "names": ["support", "query", "deny", "comment"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "rumour_eval2019", "config_name": "RumourEval2019", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1242200, "num_examples": 4879, "dataset_name": "rumour_eval2019"}, "validation": {"name": "validation", "num_bytes": 412707, "num_examples": 1440, "dataset_name": "rumour_eval2019"}, "test": {"name": "test", "num_bytes": 491431, "num_examples": 1675, "dataset_name": "rumour_eval2019"}}, "download_checksums": {"rumoureval2019_train.csv": {"num_bytes": 1203917, "checksum": "134c036e34da708f0edb22b3cc688054d6395d1669eef78e4afa0fd9a4ed4c43"}, "rumoureval2019_val.csv": {"num_bytes": 402303, "checksum": "6cc859c2eff320ba002866e0b78f7e956b78d58e9e3a7843798b2dd9c23de201"}, "rumoureval2019_test.csv": {"num_bytes": 479250, "checksum": "7d103bfb55cdef3b0d26c481ceb772159ae824aa15bf26e8b26dc87a58c55508"}}, "download_size": 2085470, "post_processing_size": null, "dataset_size": 2146338, "size_in_bytes": 4231808}}

rumoureval_2019.py

@@ -1,122 +0,0 @@
-# Copyright 2022 Mads Kongsbak and Leon Derczynski
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# TODO: Address all TODOs and remove all explanatory comments
-"""RumourEval 2019: Stance Prediction"""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@inproceedings{gorrell-etal-2019-semeval,
-    title = "{S}em{E}val-2019 Task 7: {R}umour{E}val, Determining Rumour Veracity and Support for Rumours",
-    author = "Gorrell, Genevieve  and
-      Kochkina, Elena  and
-      Liakata, Maria  and
-      Aker, Ahmet  and
-      Zubiaga, Arkaitz  and
-      Bontcheva, Kalina  and
-      Derczynski, Leon",
-    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
-    month = jun,
-    year = "2019",
-    address = "Minneapolis, Minnesota, USA",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/S19-2147",
-    doi = "10.18653/v1/S19-2147",
-    pages = "845--854",
-}
-
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-
-Stance prediction task in English. The goal is to predict whether a given reply to a claim either supports, denies, questions, or simply comments on the claim. Ran as a SemEval task in 2019.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "cc-by-4.0"
-
-class RumourEval2019Config(datasets.BuilderConfig):
-
-    def __init__(self, **kwargs):
-        super(RumourEval2019Config, self).__init__(**kwargs)
-
-class RumourEval2019(datasets.GeneratorBasedBuilder):
-    """RumourEval2019 Stance Detection Dataset formatted in triples of (source_text, reply_text, label)"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        RumourEval2019Config(name="RumourEval2019", version=VERSION, description="Stance Detection Dataset"),
-    ]
-    
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "source_text": datasets.Value("string"),
-                "reply_text": datasets.Value("string"),
-                "label": datasets.features.ClassLabel(
-                    names=[
-                        "support",
-                        "deny",
-                        "query",
-                        "comment"
-                    ]
-                )
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        train_text = dl_manager.download_and_extract("rumoureval2019_train.csv")
-        validation_text = dl_manager.download_and_extract("rumoureval2019_val.csv")
-        test_text = dl_manager.download_and_extract("rumoureval2019_test.csv")
-        
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN,      gen_kwargs={"filepath": train_text, "split": "train"}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_text, "split": "validation"}),
-            datasets.SplitGenerator(name=datasets.Split.TEST,       gen_kwargs={"filepath": test_text, "split": "test"}),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter=",")
-            guid = 0
-            for instance in reader:
-                instance["source_text"] = instance.pop("source_text")
-                instance["reply_text"] = instance.pop("reply_text")
-                instance["label"] = instance.pop("label")
-                instance['id'] = str(guid)
-                yield guid, instance
-                guid += 1