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Climate Change Fake News Detector

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by ashu31 dot icon Updated: Aug 5, 2022

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import pandas as pd import numpy as np import re import string from nltk.corpus import stopwords from nltk.tokenize import word_tokenize df_text = pd.read_csv(‘fake_or_real_news.csv’, encoding=‘latin-1’) df_text.columns = [‘id’, ‘title’, ‘text’, ‘label’]df_text.drop([‘id’, ‘title’], axis=1) Use Case1 # Remove urls text = re.sub(r”http\S+|www\S+|https\S+”, ”, text, flags=re.MULTILINE) # Remove user @ references and ‘#’ from text text = re.sub(r’\@\w+|\#’,”, text) Use Case2 removing the punctuations text = text.translate(str.maketrans(”, ”, string.punctuation)) Use Case 3 make every sentence into a list of words or tokens tokens = word_tokenize(text) words = [w for w in tokens if not w in stop_words] Use Case 4 TF-IDF Implementation from sklearn.feature_extraction.text import TfidfVectorizer tf_vector = TfidfVectorizer(sublinear_tf=True) tf_vector.fit(df_text[‘text’]) Use Case 5 To transform the label texts into numbered targets. For example, assume that we have a list of labels like this: [‘real’, ‘fake’, ‘fake’, ‘fake’] What we essentially require is a list like this: [1, 0, 0, 0]. What label encoder does is, it takes all the distinct labels and makes a list. For our example, the list would be [‘fake’, ‘real’]. X_text = tf_vector.transform(df_text[‘text’].ravel()) y_values = np.array(df_text[‘label’].ravel()) from sklearn import preprocessing le = preprocessing.LabelEncoder() le.fit(y_values)le.transform(y_values) Use Case 6 The training and testing splits are done. It can be achieved by using sklearn’s preprocessing package and importing the train test split function. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X_text, y_values, test_size=0.15, random_state=120) Use Case 7 from sklearn.metrics import accuracy_score from sklearn.linear_model import LogisticRegression model = LogisticRegression(solver=‘lbfgs’) model.fit(X_train, y_train) y_predict = model.predict(X_test) print(accuracy_score(y_test, y_predict))

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🤖 Flag and Detect Fake News with the help of AI

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                                                              The Pytorch Implementation of GCNN model from paper Fake News Detection on Social Media using Geometric Deep Learning

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                                                                                            University project: News search engine with fake news detection
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                                                                                                                                              Smart News Detector Bot is a Chrome extension that attaches itself to your Twitter timeline and provides a one-click solution to retrieving diverse perspectives on an issue.

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                                                                                                                                                                  Fake news detector based on the content and users associated with it using BERT and Graph Attention Networks (GAT).

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                                                                                                                                                                            Fake news detector based on the content and users associated with it using BERT and Graph Attention Networks (GAT).
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                                                                                                                                                                                                                                        Fake news generator and detector using keras
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                                                                                                                                                                                                                                                                      Fake NEWS detector using LIAR dataset.

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                                                                                                                                                                                                                                                                                Fake NEWS detector using LIAR dataset.
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                                                                                                                                                                                                                                                                                                              I built my own Fake News Detector using NLP and Classification Models.

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                                                                                                                                                                                                                                                                                                                        I built my own Fake News Detector using NLP and Classification Models.
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                                                                                                                                                                                                                                                                                                                                  Fake news is misinformation or manipulated news that is spread across the social media with an intention to damage a person, agency and organisation. Due to the dissemination of fake news, there is a need for computational methods to detect them. Fake news detection aims to help users to expose varieties of fabricated news. To achieve this goal, first we have taken the datasets which contains both fake and real news and conducted various experiments to organize fake news detector. We used natural processing, machine learning and deep learning techniques to classify the datasets. We yielded a comprehensive audit of detecting fake news by including fake news categorization, existing algorithms from machine learning techniques. In this project, we explored different machine learning models like Naïve Bayes, K nearest neighbors, decision tree, random forest and deep learning networks like Shallow Convolutional Neural Networks (CNN), Deep Convolutional Neural Network (VDCNN), Long Short-Term Memory Network (LSTM), Gated Recurrent Unit Network (GRU), Combination of Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network with Gated Recurrent Unit (CNN-LSTM).

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                                                                                                                                                                                                                                                                                                                                            Fake news is misinformation or manipulated news that is spread across the social media with an intention to damage a person, agency and organisation. Due to the dissemination of fake news, there is a need for computational methods to detect them. Fake news detection aims to help users to expose varieties of fabricated news. To achieve this goal, first we have taken the datasets which contains both fake and real news and conducted various experiments to organize fake news detector. We used natural processing, machine learning and deep learning techniques to classify the datasets. We yielded a comprehensive audit of detecting fake news by including fake news categorization, existing algorithms from machine learning techniques. In this project, we explored different machine learning models like Naïve Bayes, K nearest neighbors, decision tree, random forest and deep learning networks like Shallow Convolutional Neural Networks (CNN), Deep Convolutional Neural Network (VDCNN), Long Short-Term Memory Network (LSTM), Gated Recurrent Unit Network (GRU), Combination of Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network with Gated Recurrent Unit (CNN-LSTM).
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                                                                                                                                                                                                                                                                                                                                                                                                                                                                    deep-learning-with-python-notebooksby fchollet

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Jupyter notebooks for the code samples of the book "Deep Learning with Python"
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                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        data-science-ipython-notebooksby donnemartin

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Group Name 9

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Python Data Science Handbook: full text in Jupyter Notebooks

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            PythonDataScienceHandbookby jakevdp

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Python Data Science Handbook: full text in Jupyter Notebooks
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