README.md

Dataset Card: Multi-Task German Text Classification Dataset

Dataset Description

This dataset contains German text samples labeled for three tasks:

1. Fake News Detection (is_fake)
2. Hate Speech Detection (is_hate_speech)
3. Toxicity Detection (is_toxic)

Each entry in the dataset has binary or missing labels for the respective tasks:

• 0: Negative
• 1: Positive
• -1: Not labeled for the task

The dataset is useful for training and evaluating models on multi-task learning objectives in the context of online German text.

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Dataset Structure

Column	Description
text	The German text sample
is_fake	Binary label for fake news detection (0, 1 or -1)
is_hate_speech	Binary label for hate speech detection (0, 1, or -1)
is_toxic	Binary label for toxicity detection (0, 1, or -1)

Sample Data

This is only top few item of dataset where fake news is given as 0 or 1 but other 2 which are going to be hidden are in -1. similarly for hatespeech and toxicity , other 2 are -1

Text	is_fake	is_hate_speech	is_toxic
Im Studio von @1730Sat1live sprechen wir heute mit Stefan Schulte, dem Vorstandsvorsitzenden der @FraportAG, über die Lage am Frankfurter Flughafen...	0	-1	-1
gigi hat einfach überhaupt keine lust mehr auf lucas. gigi 🤝 wir alle #ibes	0	-1	-1
In Österreich 🇦🇹 ist Bundespräsident @vanderbellen für eine 2. Amtszeit angelobt worden...	0	-1	-1
Was ist der Mindestlohn pro Stunde in Vietnam? https://t.co/VQof8kwRlL	0	-1	-1
#GretaThunberg will jetzt den #Kapitalismus überwinden...	0	-1	-1

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Usage

This dataset is designed for training and evaluating multi-task classification models in German. The tasks include:

• Fake news detection
• Hate speech identification
• Toxicity assessment

Example Usage in Python

import pandas as pd

# Load the dataset
df = pd.read_csv("path_to_dataset.csv")

# Access text and labels
for index, row in df.iterrows():
    text = row['text']
    is_fake = row['is_fake']
    is_hate_speech = row['is_hate_speech']
    is_toxic = row['is_toxic']

    # Example: Print the data
    print(f"Text: {text}, Fake: {is_fake}, Hate Speech: {is_hate_speech}, Toxic: {is_toxic}")