![](https://cdn-avatars.huggingface.co/v1/production/uploads/1676194700112-6261cfe4bbcbd1c34f19c338.png)
kubota/luke-large-defamation-detection-japanese
Text Classification
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Updated
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34
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5
id
stringlengths 19
19
| target
sequence | label
sequence | user_id_list
sequence |
---|---|---|---|
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SNSにおける誹謗中傷検出のためのデータセットです.
5,000件の日本語のツイートに,それぞれ以下で定義している誹謗中傷の対象者と内容をアノテーションしています.アノテーションは,3人のクラウドワーカーにより行われています.2022年2月15日から2022年6月30日までのツイートです. 元のツイートは含まれていないため,Twitter APIを用いてデータセットを収集してください.
中傷対象(target)と中傷内容(label)の2項目がアノテーションされています.
文として成立しておらず意味の取れないものはラベルC(0)としています.
target | 対象 | 例 |
---|---|---|
A1(1) | (人種・性別・職業・思想などを共通とする)グループ | (人種・性別・職業・思想などを共通とする)グループ |
A2(2) | 個人(著名人や知人など) | 〇〇大統領,芸能人の〇〇さん,おまえ |
A3(3) | 対象がはっきりしないもの | |
C(0) | 文として成立しておらず意味が取れない | |
label | 誹謗中傷の種類 | 侵害されるもの | 例 |
---|---|---|---|
B1(1) | 生命を脅かす,精神的・身体的な危害を加える | 私生活の平穏 | • 殺害予告などの脅迫発言 • ◯◯なんていなくなればいいのにな |
B2(2) | 容姿,人格などをけなしている | 名誉感情 | • 太っているくせにカッコいいと勘違いしている • 田舎育ちだからファッション感覚がない |
B3(3) | 社会から客観的に受ける価値を低下させる | 名誉権 | • ◯◯さんは過去に事件を起こして逮捕されたことがある • ◯◯さんは会社の同僚と不倫をしている |
B4(4) | B1-B3のどれにも当てはまらず中傷性がない | ||
C(0) | 文として成立しておらず意味が取れない |
id
Twitter IDtarget
: 3名のアノテータのカテゴリAの回答 values: C(0), A1(1), A2(2), A3(3)label
: 3名のアノテータのカテゴリBの回答 values: C(0), B1(1), B2(2), B3(3), B4(4)user_id_list
: 匿名化された回答者のID# sample code from https://github.com/twitterdev/Twitter-API-v2-sample-code/blob/main/Tweet-Lookup/get_tweets_with_bearer_token.py
import requests
import os
import json
from datasets import load_dataset
# To set your enviornment variables in your terminal run the following line:
# export 'BEARER_TOKEN'='<your_bearer_token>'
bearer_token = os.environ.get("BEARER_TOKEN")
def create_url(ids: list):
tweet_fields = "tweet.fields=created_at"
ids = f"ids={','.join(ids)}"
url = "https://api.twitter.com/2/tweets?{}&{}".format(ids, tweet_fields)
return url
def bearer_oauth(r):
"""
Method required by bearer token authentication.
"""
r.headers["Authorization"] = f"Bearer {bearer_token}"
r.headers["User-Agent"] = "v2TweetLookupPython"
return r
def connect_to_endpoint(url):
response = requests.request("GET", url, auth=bearer_oauth)
if response.status_code != 200:
raise Exception(
"Request returned an error: {} {}".format(
response.status_code, response.text
)
)
return response.json()
def get_text_data(examples):
url = create_url(examples["id"])
json_response = connect_to_endpoint(url)
# print(json_response["data"])
text_dict = {data["id"]: data["text"] for data in json_response["data"]}
time_dict = {data["id"]: data["created_at"] for data in json_response["data"]}
return {
"text": [text_dict.get(id) for id in examples["id"]],
"created_at": [time_dict.get(id) for id in examples["id"]],
}
dataset = load_dataset("kubota/defamation-japanese-twitter")
dataset = dataset.map(get_text_data, batched=True, batch_size=100)
dataset["train"].to_pandas().head()
Thanks to @kubotaissei for adding this dataset.