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"_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.svm import LinearSVC\n", "\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix" ], "metadata": { "id": "HuscT6DbUheg" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Step 2: Dataset Description and Sources**\n", "\n", "This study utilizes an Arabic dataset consisting of both real and fake news articles for the purpose of training and evaluating machine learning models for fake news detection.\n", "\n", "\n", "**Real News Sources** (1,925 real news articles)\n", "The real news articles were collected from official Saudi media platforms to ensure authenticity and credibility. The primary sources include:\n", "\n", "*Saudi Broadcasting Authority (SBA)\n", "\n", "*Saudia official website\n", "\n", "*SBC official website\n", "\n", "These platforms are officially recognized and operate under regulated media frameworks, ensuring that the collected news content represents verified and reliable information.\n", "\n", "**Fake News Source**(1,000 fake news articles)\n", "\n", "The fake news samples were obtained from a publicly available dataset published on Kaggle,\n", "\n", "The selected dataset contains pre-labeled fake news articles that have been used in misinformation detection studies, ensuring transparency and reproducibility in experimental evaluation.\n", "\n", "**Final Dataset Composition**\n", "\n", "After merging and performing data cleaning (removing null values, filtering empty texts, and standardizing labels), the final dataset Total: 2,198 news samples\n" ], "metadata": { "id": "LYsd3Ei6ZA3k" } }, { "cell_type": "code", "source": [ "from google.colab import files\n", "uploaded = files.upload()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 73 }, "id": "7pu2YElPZ7RX", "outputId": "2cf7c10b-41e0-440a-c78b-17a8ef8b4ba4" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving 2925 real and fake news dataset.csv to 2925 real and fake news dataset (1).csv\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Step 3: Reading the Dataset**\n", "\n", "\n", "After uploading the dataset, it is loaded into a pandas DataFrame for further processing.\n", "\n", "The dataset is displayed to understand the size of the data before cleaning.\n", "\n", "Additionally, the first five rows are printed to inspect the structure of the dataset, including column names and sample content.\n" ], "metadata": { "id": "8IVAaCULbjru" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "FILE_NAME = list(uploaded.keys())[0]\n", "df = pd.read_csv(FILE_NAME, encoding=\"utf-8-sig\")\n", "\n", "print(\"Uploaded file:\", FILE_NAME)\n", "print(\"Dataset Shape (Before Cleaning):\", df.shape)\n", "\n", "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 211 }, "id": "2rk0nYBrcy8h", "outputId": "74f95daa-52b5-42df-8ad7-6e8a54fff644" }, "execution_count": null, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'uploaded' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-741822501.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mFILE_NAME\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFILE_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"utf-8-sig\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Uploaded file:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFILE_NAME\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'uploaded' is not defined" ] } ] }, { "cell_type": "markdown", "source": [ "**Step 4: Verify Dataset Structure and Columns**\n", "\n", "\n", "This step confirms that the dataset was loaded correctly by checking:\n", "\n", "\n", "* The dataset shape (rows and columns)\n", "* Column names\n", "* Quick summary of missing values\n", "\n", "The expected output is the number of rows/columns and a list of column names.\n", "\n" ], "metadata": { "id": "WZ0wqGsMk2zz" } }, { "cell_type": "code", "source": [ "print(\"Dataset Shape:\", df.shape)\n", "print(\"\\nColumns:\", df.columns.tolist())\n", "\n", "print(\"\\nMissing values per column:\")\n", "print(df.isnull().sum())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-2gDwfddl09l", "outputId": "41847097-4def-4dfb-d994-1bbce2bb6959" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset Shape: (2198, 6)\n", "\n", "Columns: ['id', 'title', 'content', 'source', 'date', 'label']\n", "\n", "Missing values per column:\n", "id 0\n", "title 0\n", "content 2\n", "source 0\n", "date 1000\n", "label 0\n", "dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "# Keep only relevant columns\n", "df = df[[\"content\", \"label\"]]\n", "\n", "print(\"Dataset Shape after dropping unnecessary columns:\", df.shape)\n", "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 224 }, "id": "Kms1U_zxo4Gj", "outputId": "b22079b6-cd53-4bf1-e898-6faeedc202a8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset Shape after dropping unnecessary columns: (2198, 2)\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " content label\n", "0 كشفت قناة السعودية عن باقة مميزة لبرامج ومسلسل... real\n", "1 كشفت قناة SBC عن باقة مميزة لبرامج ومسلسلات... real\n", "2 تبث قناة \"السعودية\" لقاءً خاصاً مع سمو ولي الع... real\n", "3 أكد صاحب السمو الملكي الأمير محمد بن سلمان ب... real\n", "4 قال د. أحمد الحارثي وكيل عمادة التعلم الإلكت... real" ], "text/html": [ "\n", "
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contentlabel
0كشفت قناة السعودية عن باقة مميزة لبرامج ومسلسل...real
1كشفت قناة SBC عن باقة مميزة لبرامج ومسلسلات...real
2تبث قناة \"السعودية\" لقاءً خاصاً مع سمو ولي الع...real
3أكد صاحب السمو الملكي الأمير محمد بن سلمان ب...real
4قال د. أحمد الحارثي وكيل عمادة التعلم الإلكت...real
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\\u0648\\u0627\\u0644\\u062c\\u0646\\u0648\\u0628 \\u062d\\u064a\\u062b \\u064a\\u062a\\u0645 \\u062f\\u0641\\u0639 \\u0623\\u062c\\u0648\\u0631 \\u0627\\u0644\\u0646\\u0627\\u0634\\u0637\\u064a\\u0646 \\u0647\\u064f\\u0646\\u0627 \\u0648\\u0628\\u0623\\u0645\\u0648\\u0627\\u0644 \\u0637\\u0627\\u0626\\u0644\\u0629 \\u062c\\u062f\\u0622 \\u060c\\u0643\\u0645\\u0627 \\u0648\\u064a\\u0638\\u0647\\u0631 \\u0641\\u064a \\u0627\\u0644\\u0645\\u0642\\u0637\\u0639 \\u0627\\u0646 \\u0642\\u0644\\u0628 \\u0627\\u0644\\u0634\\u064a\\u062e \\u062d\\u0632\\u064a\\u0646 \\u062c\\u062f\\u0622 \\u0639\\u0644\\u0649 \\u0627\\u0644\\u0645\\u062d\\u0627\\u0641\\u0638\\u0627\\u062a \\u0627\\u0644\\u062c\\u0646\\u0648\\u0628\\u064a\\u0629\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"fake\",\n \"real\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "markdown", "source": [ "**Step 6: Remove Missing , Empty Text Rows Remove duplicates**\n", "\n", "\n", "In this step, i remove rows that contain missing or empty text values in the content column.\n", "\n", "Machine learning models require meaningful textual input.\n", "\n", "Rows with null or empty content do not provide useful information and may cause errors during feature extraction.\n", ":" ], "metadata": { "id": "TSCSZ50ppcpK" } }, { "cell_type": "code", "source": [ "# Keep only needed columns\n", "df = df[[\"content\", \"label\"]]\n", "\n", "# Remove missing values\n", "df = df.dropna(subset=[\"content\"])\n", "df = df[df[\"content\"].str.strip().ne(\"\")]\n", "\n", "# Standardize labels\n", "df[\"label\"] = df[\"label\"].astype(str).str.strip().str.lower()\n", "\n", "# Remove duplicates\n", "print(\"Rows before removing duplicates:\", df.shape[0])\n", "df = df.drop_duplicates(subset=[\"content\"])\n", "print(\"Rows after removing duplicates:\", df.shape[0])\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aWALUovcqRb7", "outputId": "a3d757da-31c5-4dc9-dd93-f9e3082e1e8a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Rows before removing duplicates: 2135\n", "Rows after removing duplicates: 2135\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Step 8: Define Features (X) and Target (y)**\n", "\n", "In supervised learning, the dataset must be divided into:\n", "\n", "1. X (Features): the input text (news content)\n", "\n", "2. y (Target): the corresponding label (real or fake)\n", "\n", "This step prepares the dataset for splitting into training and testing sets." ], "metadata": { "id": "yJkhhTaMrSen" } }, { "cell_type": "code", "source": [ "X = df[\"content\"]\n", "y = df[\"label\"]\n", "\n", "print(\"Sample text preview:\\n\")\n", "print(X.iloc[0][:300])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "piL_sH94r4dO", "outputId": "27aa291b-92b5-4f52-9396-2be54b4e33a2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Sample text preview:\n", "\n", "كشفت قناة السعودية عن باقة مميزة لبرامج ومسلسلات موسم رمضان 2021، والتي تضم مجموعة من أكبر نجوم المملكة والعالم العربي. وتشهد المسلسلات تنوعاً ضخماً بين الكوميدي والدراما، كما تُشبع البرامج جميع اهتمامات المشاهد، فمنها الديني والوثائقي والحواري والكوميدي، وكذلك برامج المسابقات والطبخ. 8 برام\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Step 9: Train/Test Split**\n", "\n", "In this step, the dataset is divided into training and testing sets.\n", "\n", "1. The training set (80%) is used to train the model.\n", "2. The testing set (20%) is used to evaluate model performance on unseen data.\n", "\n", "I use stratify=y to preserve the same class distribution (real vs fake) in both training and testing sets.\n", "\n", "This ensures fair and reliable evaluation" ], "metadata": { "id": "_rby94_4sErm" } }, { "cell_type": "code", "source": [ "X_train, X_test, y_train, y_test = train_test_split(\n", " X,\n", " y,\n", " test_size=0.2,\n", " random_state=42,\n", " stratify=y\n", ")\n", "\n", "print(\"Training size:\", len(X_train))\n", "print(\"Testing size:\", len(X_test))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HYszZli9sq9q", "outputId": "e1a7dc41-ca4a-47ad-986d-30b24ab8300c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Training size: 1708\n", "Testing size: 427\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Step 10 : TF-IDF Feature Extraction**\n", "\n", "In this step, i convert textual data into numerical features using TF-IDF (Term Frequency–Inverse Document Frequency).\n", "\n", "TF-IDF assigns higher weights to important words that appear frequently in a document but less frequently across the entire dataset.\n", "I use:\n", "\n", "1. max_features=5000 to limit feature size\n", "2. ngram_range=(1,2) to include both single words (unigrams) and word pairs (bigrams)" ], "metadata": { "id": "VfaMbU_ztMMp" } }, { "cell_type": "code", "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "vectorizer = TfidfVectorizer(\n", " max_features=5000,\n", " ngram_range=(1, 2)\n", ")\n", "\n", "X_train_tfidf = vectorizer.fit_transform(X_train)\n", "X_test_tfidf = vectorizer.transform(X_test)\n", "\n", "print(\"TF-IDF Training Shape:\", X_train_tfidf.shape)\n", "print(\"TF-IDF Testing Shape:\", X_test_tfidf.shape)\n", "print(\"Number of Features:\", len(vectorizer.get_feature_names_out()))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LVCsLIA7tmg4", "outputId": "602230f4-9bc4-428a-d243-92f270c376e7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TF-IDF Training Shape: (1708, 5000)\n", "TF-IDF Testing Shape: (427, 5000)\n", "Number of Features: 5000\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Model 1 : Logistic Regression**\n", "\n", "In this step, we train a Logistic Regression classifier on TF-IDF features.\n", "Logistic Regression is a strong baseline for text classification because it performs well with high-dimensional sparse inputs such as TF-IDF vectors.\n", "We evaluate the model on the test set using accuracy, classification report, and confusion matrix.\n" ], "metadata": { "id": "7KUZfatW6GcG" } }, { "cell_type": "code", "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n", "\n", "lr_model = LogisticRegression(max_iter=2000)\n", "lr_model.fit(X_train_tfidf, y_train)\n", "\n", "y_pred_lr = lr_model.predict(X_test_tfidf)\n", "lr_accuracy = accuracy_score(y_test, y_pred_lr)\n", "\n", "print(\"TF-IDF + Logistic Regression Accuracy:\", round(lr_accuracy, 4))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_lr))\n", "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred_lr))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XRMk9_ULISeb", "outputId": "49f57308-6567-47ed-b5e2-978bb4fee5f2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TF-IDF + Logistic Regression Accuracy: 0.9813\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " fake 0.98 0.98 0.98 195\n", " real 0.98 0.98 0.98 232\n", "\n", " accuracy 0.98 427\n", " macro avg 0.98 0.98 0.98 427\n", "weighted avg 0.98 0.98 0.98 427\n", "\n", "\n", "Confusion Matrix:\n", " [[191 4]\n", " [ 4 228]]\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Model 2 — TF-IDF + Multinomial Naive Bayes**\n", "\n", "In this step, we train Multinomial Naive Bayes using TF-IDF features.\n", "Naive Bayes is widely used in text classification because it is fast and often effective with frequency-based representations.\n", "We evaluate the model using the same metrics to compare it fairly with Logistic Regression and SVM." ], "metadata": { "id": "i8M1akPjIZlx" } }, { "cell_type": "code", "source": [ "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n", "\n", "nb_model = MultinomialNB()\n", "nb_model.fit(X_train_tfidf, y_train)\n", "\n", "y_pred_nb = nb_model.predict(X_test_tfidf)\n", "nb_accuracy = accuracy_score(y_test, y_pred_nb)\n", "\n", "print(\"TF-IDF + Naive Bayes Accuracy:\", round(nb_accuracy, 4))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_nb))\n", "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred_nb))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KEDbootcIgj2", "outputId": "80aa0a83-35cc-4b5b-88da-bfcff987f3a3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TF-IDF + Naive Bayes Accuracy: 0.9485\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " fake 0.99 0.90 0.94 195\n", " real 0.92 0.99 0.95 232\n", "\n", " accuracy 0.95 427\n", " macro avg 0.95 0.94 0.95 427\n", "weighted avg 0.95 0.95 0.95 427\n", "\n", "\n", "Confusion Matrix:\n", " [[175 20]\n", " [ 2 230]]\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Model 3 — TF-IDF + SVM (LinearSVC)**\n", "\n", "In this step, we train a Support Vector Machine (LinearSVC) classifier on TF-IDF features.\n", "SVMs are strong for text classification because they handle large sparse feature spaces effectively and learn a robust decision boundary between classes.\n", "LinearSVC is efficient for TF-IDF and is commonly used as a top classical baseline." ], "metadata": { "id": "2kRR4gaYI4Iv" } }, { "cell_type": "code", "source": [ "from sklearn.svm import LinearSVC\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n", "\n", "svm_model = LinearSVC()\n", "svm_model.fit(X_train_tfidf, y_train)\n", "\n", "y_pred_svm = svm_model.predict(X_test_tfidf)\n", "svm_accuracy = accuracy_score(y_test, y_pred_svm)\n", "\n", "print(\"TF-IDF + SVM Accuracy:\", round(svm_accuracy, 4))\n", "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_svm))\n", "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred_svm))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "q7eqhuEAJD-A", "outputId": "d7f97d31-075e-45a5-d8e2-1550591ab3f5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TF-IDF + SVM Accuracy: 0.9813\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " fake 0.98 0.98 0.98 195\n", " real 0.98 0.98 0.98 232\n", "\n", " accuracy 0.98 427\n", " macro avg 0.98 0.98 0.98 427\n", "weighted avg 0.98 0.98 0.98 427\n", "\n", "\n", "Confusion Matrix:\n", " [[191 4]\n", " [ 4 228]]\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Model 4: AraBERT Embeddings + Logistic Regression**\n", "\n", "In this step, I use a pre-trained Arabic language model (AraBERT) to generate contextual embeddings for each news article.\n", "\n", "Unlike TF-IDF, which is frequency-based, AraBERT captures:\n", "1. Context\n", "2. Semantic meaning\n", "3. Word relationships\n", "\n", "We extract the CLS token embedding for each text and use it as input features for a Logistic Regression classifier." ], "metadata": { "id": "7AcXCE3ixyV_" } }, { "cell_type": "code", "source": [ "# Install Transformers\n", "!pip -q install transformers" ], "metadata": { "id": "_HeYF7wUyQzO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Load AraBERT Model\n", "import torch\n", "from transformers import AutoTokenizer, AutoModel\n", "\n", "model_name = \"aubmindlab/bert-base-arabertv02\"\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "bert_model = AutoModel.from_pretrained(model_name)\n", "\n", "bert_model.eval()\n", "device = torch.device(\"cpu\")\n", "bert_model.to(device)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "7f7c3deb38384bf98e1599e45e6dad49", "17df5d090d9a4148b26818f2a1512a27", "0f1af1b9b8a448c38b939e18f80fea78", "6647876fbc5447f7bb6fcde8e4b64ec9", "d23b86e993964853be25fa0123151cb9", "5970067289174a01870a1871182a1855", "74078600e21a44f3bec57fcc6cb070f0", "fe60d3d9617e4ef1942bc03da685327c", "034f1309489b4c9dac9cb7488a7eb12a", "98fde43d80fd429280c777acd5bc4cd8", "b9a2bb0e0b2f43e38892f60b77eb59d2", "909316e4e308489f94d401dc6be089e5", "824cf71ae5144200a37614b194fd6d82", "7eb5f5deaca8482ebd1ead2ab555a777", "c38b3286c898412aba55af1d6b337e65", "4be0d86b34944c169721428de8bd302a", "555271ce87b54cae84c6aaf550d6306b", "6cfd6bc234d742b4aaf39ab95c421340", "36eb73ff02cd4d299f355d731f38b9e8", "73b64428422d4ae29b97b3f371c529fd", "391ce27e9d2a49b5bc6dc1095f67dfb8", "b9476278243f4f2b9b1cbd42e4dd6b0a", "11f0d426f45349b68caad10df7574596", "a9c13fdb16274cc18f7e3961008e4888", "a0db392ed076408ca9af662242f65055", "ef5a232ef1184921b5acaf074986c5d2", "ca3fd2dfd26c427f83dbc22df1f9585c", "2ec08c40b72543568f5c5205bdb3e2ba", "90d7eceb6e9c4be8bd22ffbb4be5e00b", "f477e6eb8b3e4cadb0ecf6ab2731f731", "82bc6fbef7074056a757ef5b27c3bba9", "381a9efff83947c394ce59ebc792a87d", "0f8e166bd5f246c1b6c3058893edc1ac", "989219a1cdd441c8aaaf2e777326d573", "d1403363194d4c3c82672928b7005c8a", "bb72583bb43e417e816adf28d9ac4a73", "c4c523179726422ebff436d9e6a8903f", "50b77d4e8dff42bf9b53c6d136e93f99", "281466ff4d7a4677902b21a4972ba6eb", "1da382b5de5d4cc68eff6d0975ef94df", "2c66c12aedac4c2bae3232a3a94ad2d3", "cf220a9b4b544b089ab430b13624ec76", "a0242a9199b14d869f8fd332aae818b4", "2144ba99020742e9a042b202a7da7248", "e4663380b8a1406ab290b4a4d637c20c", "71e9b06cfa9d48e6a5a60c5d594cce23", "95c2d7216e6f4c51a983b95f97dbb334", "2fe1596177544b839d9fa41a4a84c469", "27e0462dc57e49438ac38748f737bd55", "f7f380e9b80d4cdd99a409651e243d7f", "74f11fe8e76247b8b82a5157da6e8888", "68435f04eeed42c891b528ed8a2a6e3a", "c73582cd9ab14a34a9586703dc30547b", "5555a72f295a4ee1a20c34c621895ea4", "21118d10acc146acacd16b011a7faf64", "7574cadcf628481ebb35ff24a3dce20c", "dd0bf850c33542878e9cf1058ea71bc7", "572959525946468393c8f4067c8c43a9", "6ff5d6840e334de9b536cbb6409a9188", "3d67f4c7ce85468ab5b83838e3011ef7", "14a9cd0a0d274f62a437d22b6db2f419", "f95512c9a9814d3bb5f79ee3aa4191a6", "73dbabfd1d674b68a0ae5e23b3b09981", "93393797ced644a68a2265a0ab5d07f5", "6138d18f461f4b17b18d15aaebc53e72", "19d0fd6189c441838c9895c303d91058", "8e90f83cb1e24c1aa03fc22f2e5a4560", "44e399dd2bd64672acbbafcc74d2af14", "a4487eb01a4b470b91e005ff1bcbd301", "7ff4dd8e19004694a6212670411c0993", "72190d19f96a4ed7a4754804294fbc9c", "af126faf38d3498f9e12e250c9fd7924", "fc1c2905d53b4426bc7eee51457df417", "894f95a4c46941cda501d3bbafd66024", "55ce665363104a18a6e654c4185a8777", "36f8e9fbf6a942f8806fd14eca210105", "8dd75fed00574a838fef06403beaafb4" ] }, "id": "j-4GdzXyyfX1", "outputId": "3dc9b05a-0371-48fd-8f8c-4c5eb4caa4ca" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n", "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/384 [00:00" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Final Comparison (All 4 Models)**\n", "\n", "This step summarizes and compares the accuracy of all four models in one table.\n", "This provides a clear view of which approach performs best for Arabic fake news detection." ], "metadata": { "id": "l6duidfTL6Uk" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "comparison = pd.DataFrame({\n", " \"Model\": [\n", " \"TF-IDF + Logistic Regression\",\n", " \"TF-IDF + Naive Bayes\",\n", " \"TF-IDF + SVM\",\n", " \"AraBERT + Logistic Regression\"\n", " ],\n", " \"Accuracy\": [\n", " lr_accuracy,\n", " nb_accuracy,\n", " svm_accuracy,\n", " bert_accuracy\n", " ]\n", "}).sort_values(\"Accuracy\", ascending=False)\n", "\n", "comparison" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "id": "4c4s5z96MEVh", "outputId": "61e18aa2-f725-418e-d0f5-ed5ae2f12593" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Model Accuracy\n", "3 AraBERT + Logistic Regression 0.995316\n", "0 TF-IDF + Logistic Regression 0.981265\n", "2 TF-IDF + SVM 0.981265\n", "1 TF-IDF + Naive Bayes 0.948478" ], "text/html": [ "\n", "
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ModelAccuracy
3AraBERT + Logistic Regression0.995316
0TF-IDF + Logistic Regression0.981265
2TF-IDF + SVM0.981265
1TF-IDF + Naive Bayes0.948478
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "comparison", "summary": "{\n \"name\": \"comparison\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"TF-IDF + Logistic Regression\",\n \"TF-IDF + Naive Bayes\",\n \"AraBERT + Logistic Regression\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.019871853335453295,\n \"min\": 0.9484777517564403,\n \"max\": 0.9953161592505855,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.9953161592505855,\n 0.9812646370023419,\n 0.9484777517564403\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 59 } ] }, { "cell_type": "code", "source": [ "accuracies = [\n", " lr_accuracy * 100,\n", " nb_accuracy * 100,\n", " svm_accuracy * 100,\n", " bert_accuracy * 100\n", "]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 211 }, "id": "S-d_Q2uAO2Uc", "outputId": "ec348c6d-516f-4238-ac8a-9297c60475b6" }, "execution_count": null, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'lr_accuracy' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-2684992925.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m accuracies = [\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlr_accuracy\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mnb_accuracy\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msvm_accuracy\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mbert_accuracy\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'lr_accuracy' is not defined" ] } ] }, { "cell_type": "markdown", "source": [ "**Final Model Comparison and Results**\n", "\n", "In this study, four different models were evaluated for Arabic fake news detection. After data cleaning (removal of missing values and duplicate entries) and applying a stratified train/test split, the final accuracy results were as follows:\n", "\n", "1. TF-IDF + Logistic Regression: 98.13%\n", "\n", "2. TF-IDF + Naive Bayes: 94.85%\n", "\n", "3. TF-IDF + Support Vector Machine (SVM): 98.13%\n", "\n", "4. AraBERT + Logistic Regression: 99.53%\n", "\n", "The results indicate that classical TF-IDF-based models achieved strong performance, with both Logistic Regression and SVM reaching 98.13% accuracy. Naive Bayes achieved slightly lower performance at 94.85%.\n", "\n", "AraBERT combined with Logistic Regression achieved the highest performance at 99.53%, demonstrating the effectiveness of contextual embeddings in capturing semantic meaning and linguistic structure in Arabic text.\n", "\n", "Importantly, duplicate samples were removed before final training to prevent potential data leakage. The AraBERT model maintained its high accuracy even after duplicate removal, confirming the robustness and reliability of the results.\n", "\n", "These findings highlight that transformer-based models significantly outperform traditional frequency-based approaches in Arabic fake news detection tasks." ], "metadata": { "id": "sLA6lBArNW-c" } } ] }