Twitter Bitcoin Spam Detection

🚀 Twitter bitcoin related spam detection

This model is designed to classify bitcoin or crypto - related tweets as "human", "spam", or "bot", eliminating the prejudice in classifying such tweets.

🚀 Quick Start

This model aims to classify tweets related to bitcoin or crypto topics as "human", "spam", or "bot". There are already many models available, but bitcoin - related tweets are often quickly classified as "spam" because they are usually associated with phishing sites or scams. This model, trained on a bitcoin - related dataset, removes this prejudice, enabling effective work with bitcoin - related tweets.

The model is a fine - tuned version of [vinai/bertweet - base](https://huggingface.co/vinai/bertweet - base), a roBERTa - based model fine - tuned with 850M English Tweets, and it's trained for emotion classification over a bitcoin - related dataset.

✨ Features

• Classify bitcoin or crypto - related tweets into "human", "spam", or "bot".
• Eliminate the prejudice in classifying bitcoin - related tweets.
• Based on a fine - tuned version of [vinai/bertweet - base](https://huggingface.co/vinai/bertweet - base).

💻 Usage Examples

Basic Usage

BERTweet was trained over normalized tweets such as: tweet = "DHEC confirms HTTPURL via @USER :crying_face:" So for better results, it's recommended to normalize the texts before applying the spam detection. What it does is converting user mentions and web/url links into special tokens @USER and HTTPURL, respectively, and other preprocessing modifications. To do so, copy or download the TweetNormalizer provided at the BERTweet webpage:

!git clone https://github.com/VinAIResearch/BERTweet.git
!pip install emoji

from transformers import pipeline
import sys

# (Optional to improve accuracy)
sys.path.append('/content/BERTweet') # Or whatever directory the folder got downloaded in
from TweetNormalizer import normalizeTweet

classifier = pipeline("text - classification",
                      model="sandiumenge/twitter - bitcoin - spam - detection",
                      tokenizer="sandiumenge/twitter - bitcoin - spam - detection",
                      truncation=True,
                      padding=True,
                      max_length=128
)

tweet = "I'm winning iPhone XS，BTC，ETH and other Awards. Join with us!@freecoinhunt https://t.co/VIUwLmdy4n"
normalized_tweet = normalizeTweet(tweet)
# "I 'm winning iPhone XS ， BTC ， ETH and other Awards . Join with us ! @USER HTTPURL"

print(classifier(normalized_tweet))

>> [{'label': 'spam', 'score': 0.9803344011306763}]

Advanced Usage

The model achieves the following results on the evaluation set:

• Loss: 0.4793
• Accuracy: 0.8755
• F1: 0.8767
• Precision: 0.8792
• Recall: 0.8755


📄 License

This project is licensed under the MIT license.