fake_articels_LSTM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "taB4j1M8DgZ1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.utils import plot_model\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import itertools\n",
        "import seaborn as sns\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Now you can use the file path to access specific files\n",
        "file_path = '/content/drive/MyDrive/gpt fakearticles/Fake.csv'  # Replace with your file's path\n",
        "file_path = '/content/drive/MyDrive/gpt fakearticles/True.csv'\n",
        "# You can then open and read the file as you would with a local file\n",
        "with open(file_path, 'r') as file:\n",
        "    content = file.read()\n",
        "\n",
        "print(content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quPdwkRGEeez",
        "outputId": "d2ef0e6c-12b8-4692-90a0-4ecd20c3e7f8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_true1 = pd.read_csv(\"/content/drive/MyDrive/gpt fakearticles/True.csv\")\n",
        "df_fake1 = pd.read_csv(\"/content/drive/MyDrive/gpt fakearticles/Fake.csv\")"
      ],
      "metadata": {
        "id": "UfaA_yMrEehd"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_true1[\"text\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIwtGpYtEekZ",
        "outputId": "341d430d-8a96-4a83-b82a-945abed40d4e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WASHINGTON (Reuters) - The head of a conservative Republican faction in the U.S. Congress, who voted this month for a huge expansion of the national debt to pay for tax cuts, called himself a “fiscal conservative” on Sunday and urged budget restraint in 2018. In keeping with a sharp pivot under way among Republicans, U.S. Representative Mark Meadows, speaking on CBS’ “Face the Nation,” drew a hard line on federal spending, which lawmakers are bracing to do battle over in January. When they return from the holidays on Wednesday, lawmakers will begin trying to pass a federal budget in a fight likely to be linked to other issues, such as immigration policy, even as the November congressional election campaigns approach in which Republicans will seek to keep control of Congress. President Donald Trump and his Republicans want a big budget increase in military spending, while Democrats also want proportional increases for non-defense “discretionary” spending on programs that support education, scientific research, infrastructure, public health and environmental protection. “The (Trump) administration has already been willing to say: ‘We’re going to increase non-defense discretionary spending ... by about 7 percent,’” Meadows, chairman of the small but influential House Freedom Caucus, said on the program. “Now, Democrats are saying that’s not enough, we need to give the government a pay raise of 10 to 11 percent. For a fiscal conservative, I don’t see where the rationale is. ... Eventually you run out of other people’s money,” he said. Meadows was among Republicans who voted in late December for their party’s debt-financed tax overhaul, which is expected to balloon the federal budget deficit and add about $1.5 trillion over 10 years to the $20 trillion national debt. “It’s interesting to hear Mark talk about fiscal responsibility,” Democratic U.S. Representative Joseph Crowley said on CBS. Crowley said the Republican tax bill would require the  United States to borrow $1.5 trillion, to be paid off by future generations, to finance tax cuts for corporations and the rich. “This is one of the least ... fiscally responsible bills we’ve ever seen passed in the history of the House of Representatives. I think we’re going to be paying for this for many, many years to come,” Crowley said. Republicans insist the tax package, the biggest U.S. tax overhaul in more than 30 years,  will boost the economy and job growth. House Speaker Paul Ryan, who also supported the tax bill, recently went further than Meadows, making clear in a radio interview that welfare or “entitlement reform,” as the party often calls it, would be a top Republican priority in 2018. In Republican parlance, “entitlement” programs mean food stamps, housing assistance, Medicare and Medicaid health insurance for the elderly, poor and disabled, as well as other programs created by Washington to assist the needy. Democrats seized on Ryan’s early December remarks, saying they showed Republicans would try to pay for their tax overhaul by seeking spending cuts for social programs. But the goals of House Republicans may have to take a back seat to the Senate, where the votes of some Democrats will be needed to approve a budget and prevent a government shutdown. Democrats will use their leverage in the Senate, which Republicans narrowly control, to defend both discretionary non-defense programs and social spending, while tackling the issue of the “Dreamers,” people brought illegally to the country as children. Trump in September put a March 2018 expiration date on the Deferred Action for Childhood Arrivals, or DACA, program, which protects the young immigrants from deportation and provides them with work permits. The president has said in recent Twitter messages he wants funding for his proposed Mexican border wall and other immigration law changes in exchange for agreeing to help the Dreamers. Representative Debbie Dingell told CBS she did not favor linking that issue to other policy objectives, such as wall funding. “We need to do DACA clean,” she said.  On Wednesday, Trump aides will meet with congressional leaders to discuss those issues. That will be followed by a weekend of strategy sessions for Trump and Republican leaders on Jan. 6 and 7, the White House said. Trump was also scheduled to meet on Sunday with Florida Republican Governor Rick Scott, who wants more emergency aid. The House has passed an $81 billion aid package after hurricanes in Florida, Texas and Puerto Rico, and wildfires in California. The package far exceeded the $44 billion requested by the Trump administration. The Senate has not yet voted on the aid. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = tf.keras.preprocessing.text.Tokenizer()"
      ],
      "metadata": {
        "id": "BSo09TVdIAcU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fake_articlesdf = pd.read_csv(\"/content/drive/MyDrive/gpt fakearticles/chatgpt_fake_articles (1).csv\", encoding='unicode_escape')\n",
        "\n",
        "fake_articles = fake_articlesdf[\"text\"].to_list()\n",
        "\n",
        "fake_articlesdf2 = pd.read_csv(\"/content/drive/MyDrive/gpt fakearticles/chatgpt_fake_articles.csv\", encoding='unicode_escape')\n",
        "\n",
        "fake_articles2 = fake_articlesdf2[\"text\"].to_list()\n",
        "\n",
        "\n",
        "\n",
        "true_titles = df_true1[\"text\"].to_list()\n",
        "\n",
        "fake_titles = df_fake1[\"text\"].to_list()\n",
        "\n",
        "texts = true_titles + fake_titles + fake_articles\n",
        "\n",
        "#add a new dataset to this one to test (so the tokenizer has seen the words before)\n",
        "textswithfakearticles = true_titles + fake_titles + fake_articles + fake_articles2\n",
        "\n",
        "# Create labels\n",
        "true_labels = [1] * len(true_titles)\n",
        "fake_labels = [0] * len(fake_titles)\n",
        "fake_article_labels = [0] * len(fake_articles)\n",
        "\n",
        "labels = true_labels + fake_labels + fake_article_labels\n",
        "\n",
        "\n",
        "tokenizer.fit_on_texts(textswithfakearticles)\n",
        "\n",
        "tokenized = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "\n",
        "\n",
        "print(max(tokenizer.word_index.values()))\n",
        "\n",
        "vocab_size = max(tokenizer.word_index.values())+1\n",
        "\n",
        "print(vocab_size)\n",
        "\n",
        "\n",
        "#print(tokenized)\n",
        "\n",
        "print(len(tokenized))\n",
        "print(len(labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SS17hpHjIAeo",
        "outputId": "340fe7d0-44e3-4000-c197-63112718342f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "138053\n",
            "138054\n",
            "44948\n",
            "44948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word_counts = tokenizer.word_counts\n",
        "sorted_word_counts = sorted(word_counts.items(), key=lambda x: x[1], reverse=True)\n",
        "most_common_words = sorted_word_counts[:100]\n",
        "words, counts = zip(*most_common_words)\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.bar(words, counts)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "o2lgtw3wIAh2",
        "outputId": "9dcf81e7-7b7f-403e-c9f6-287540778e3a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lengths = [len(item) for item in tokenized]\n",
        "\n",
        "max_length = max(lengths)\n",
        "min_length = min(lengths)\n",
        "\n",
        "max_length_index = lengths.index(max_length)\n",
        "min_length_index = lengths.index(min_length)\n",
        "\n",
        "true_tokenized = tokenizer.texts_to_sequences(true_titles)\n",
        "\n",
        "fake_tokenized = tokenizer.texts_to_sequences(fake_titles)\n",
        "\n",
        "true_lengths = [len(item) for item in true_tokenized]\n",
        "\n",
        "fake_lengths = [len(item) for item in fake_tokenized]\n",
        "\n",
        "print(\"Distribution of article lengths\")\n",
        "\n",
        "#sns.kdeplot(lengths, label='Overall')\n",
        "sns.kdeplot(true_lengths, label='Real')\n",
        "sns.kdeplot(fake_lengths, label='Fake')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Qbtv056tIAk6",
        "outputId": "87f08176-7d11-40ca-ab3f-aa7004d5da72"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribution of article lengths\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "\n",
        "padded_sequences = pad_sequences(tokenized, padding='post', maxlen=max_length)\n",
        "\n",
        "print(padded_sequences)\n",
        "\n",
        "lengths = [len(item) for item in padded_sequences]\n",
        "\n",
        "max_length = max(lengths)\n",
        "min_length = min(lengths)\n",
        "\n",
        "max_length_index = lengths.index(max_length)\n",
        "min_length_index = lengths.index(min_length)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXPh_fimIAnz",
        "outputId": "630d54ee-3cd2-4ef6-e519-4b35775f8e04"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 107   67    1 ...    0    0    0]\n",
            " [ 107   67 1549 ...    0    0    0]\n",
            " [ 107   67    1 ...    0    0    0]\n",
            " ...\n",
            " [   1   15  155 ...    0    0    0]\n",
            " [   1  163   21 ...    0    0    0]\n",
            " [   1   15  155 ...    0    0    0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_length = 44\n",
        "\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler\n",
        "\n",
        "def scheduler(epoch, lr):\n",
        "    if epoch < 1:\n",
        "        return lr\n",
        "    else:\n",
        "        return lr * (10**(-epoch/2))\n",
        "callbacks = [LearningRateScheduler(scheduler, verbose=1)]\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Embedding(vocab_size, embedding_length, input_length=max_length))\n",
        "model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(256, return_sequences = False)))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(256, activation = \"relu\"))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(2, activation = \"softmax\"))\n",
        "input_array = np.random.randint(1000, size=(32, max_length))\n",
        "model.compile(tf.keras.optimizers.AdamW(weight_decay=0.001), 'binary_focal_crossentropy', metrics=[tf.keras.metrics.BinaryAccuracy()])\n",
        "output_array = model.predict(input_array)\n",
        "print(output_array.shape)\n",
        "model.summary()\n",
        "plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        },
        "id": "zBQJtfycEem-",
        "outputId": "dc52dc7d-1998-48ff-e58a-c57c09a51c23"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 6s 6s/step\n",
            "(32, 2)\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 8375, 44)          6074376   \n",
            "                                                                 \n",
            " bidirectional (Bidirection  (None, 512)               616448    \n",
            " al)                                                             \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 2)                 514       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6822666 (26.03 MB)\n",
            "Trainable params: 6822666 (26.03 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(padded_sequences[20])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m71OMUEJEeqC",
        "outputId": "2ea2bfd0-0475-4f58-aec9-ca16b4cc2005"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[107  67   4 ...   0   0   0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = np.array(labels)\n",
        "\n",
        "one_hot_labels = to_categorical(labels)\n",
        "\n",
        "print(one_hot_labels[0])\n",
        "\n",
        "sequences_train, sequences_test, labels_train, labels_test = train_test_split(padded_sequences, one_hot_labels, test_size=0.2, random_state=43)\n",
        "\n",
        "model.fit(sequences_train, labels_train, epochs = 4, batch_size = 64, validation_data=(sequences_test, labels_test), callbacks=callbacks)\n",
        "model.evaluate(sequences_test, labels_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Rm4mdTwEesb",
        "outputId": "c528a9a5-9c5a-47dc-dae1-df931325c10e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 1.]\n",
            "\n",
            "Epoch 1: LearningRateScheduler setting learning rate to 0.0010000000474974513.\n",
            "Epoch 1/4\n",
            "562/562 [==============================] - 670s 1s/step - loss: 0.0081 - binary_accuracy: 0.9888 - val_loss: 9.6108e-04 - val_binary_accuracy: 0.9992 - lr: 0.0010\n",
            "\n",
            "Epoch 2: LearningRateScheduler setting learning rate to 0.00031622778103685084.\n",
            "Epoch 2/4\n",
            "562/562 [==============================] - 640s 1s/step - loss: 3.1969e-04 - binary_accuracy: 0.9997 - val_loss: 6.4466e-04 - val_binary_accuracy: 0.9993 - lr: 3.1623e-04\n",
            "\n",
            "Epoch 3: LearningRateScheduler setting learning rate to 3.162277862429619e-05.\n",
            "Epoch 3/4\n",
            "562/562 [==============================] - 636s 1s/step - loss: 1.4560e-04 - binary_accuracy: 0.9999 - val_loss: 6.6588e-04 - val_binary_accuracy: 0.9992 - lr: 3.1623e-05\n",
            "\n",
            "Epoch 4: LearningRateScheduler setting learning rate to 1.0000000409520217e-06.\n",
            "Epoch 4/4\n",
            "562/562 [==============================] - 658s 1s/step - loss: 1.1623e-04 - binary_accuracy: 0.9999 - val_loss: 6.6761e-04 - val_binary_accuracy: 0.9992 - lr: 1.0000e-06\n",
            "281/281 [==============================] - 80s 283ms/step - loss: 6.6761e-04 - binary_accuracy: 0.9992\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0006676142802461982, 0.9992213845252991]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm,classes,normalize=False,title='Confusion Matrix',cmap=plt.cm.Dark2):\n",
        "  plt.imshow(cm,interpolation='nearest',cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks=np.arange(len(classes))\n",
        "  plt.xticks(tick_marks,classes,rotation=45)\n",
        "  plt.yticks(tick_marks,classes)\n",
        "  if normalize:\n",
        "    cm=cm.astype('float')/cm.sum(axis=1)[:,np.newaxis]\n",
        "    print(\"normalize confusion matrix\")\n",
        "  else:\n",
        "    print('confusion matrix,without normalization')\n",
        "\n",
        "  thresh = cm.max() / 2\n",
        "  for i,j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"yellow\" if cm[i, j] > thresh else \"red\")\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "\n",
        "pred_lr=model.predict(sequences_test)\n",
        "score = model.evaluate(sequences_test, labels_test)\n",
        "pred_lr = (pred_lr > 0.5).astype(int)\n",
        "labels_test_single = np.argmax(labels_test, axis=1)\n",
        "pred_lr_single = np.argmax(pred_lr, axis=1)\n",
        "print(f'Accuracy: {round(score[1]*100,2)}%')\n",
        "cm = metrics.confusion_matrix(labels_test_single,pred_lr_single)\n",
        "plot_confusion_matrix(cm, classes=['FAKE NEWS', 'REAL NEWS'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "HWQhu_zoI39s",
        "outputId": "b56828de-06ec-4d65-b83e-3a4ee790eb9c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "281/281 [==============================] - 78s 276ms/step\n",
            "281/281 [==============================] - 82s 291ms/step - loss: 6.6761e-04 - binary_accuracy: 0.9992\n",
            "Accuracy: 99.92%\n",
            "confusion matrix,without normalization\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "misclassified_indices = np.where(pred_lr_single != labels_test_single)[0]\n",
        "\n",
        "incorrect_labels = []\n",
        "for i in misclassified_indices:\n",
        "    incorrect_labels.append(pred_lr_single[i])\n",
        "\n",
        "misclassified_sequences = []\n",
        "for i in misclassified_indices:\n",
        "    misclassified_sequences.append(sequences_test[i])\n",
        "misclassified_texts = tokenizer.sequences_to_texts(misclassified_sequences)\n",
        "\n",
        "for i in range(len(misclassified_texts)):\n",
        "    if incorrect_labels[i] == 1:\n",
        "        print(\"Predicted Real\\n\")\n",
        "    if incorrect_labels[i] == 0:\n",
        "        print(\"Predicted Fake\\n\")\n",
        "    print(misclassified_texts[i])\n",
        "    print()\n",
        "\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d79v8wwwI4Ab",
        "outputId": "0a74864e-00bd-450a-bfa5-018da6c81f3b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Real\n",
            "\n",
            "according to reuters a grand jury in oklahoma found that jail staff did not verify what drugs they were using for lethal injections and were unaware when the wrong drugs were administered this report is another damning piece of evidence that shows that there are many flaws in the supposedly humane systems in place for carrying out capital punishments in 2014 the news broke that the execution of an inmate named clayton lockett who was sentenced to death for murder an executioner failed to properly put an i v line into lockett the needle used to inject the lethal drugs into lockett began to spray all over the execution chamber that he was in he died after 45 minutes of agony not because the toxins that were pumped into his body worked as intended but because they caused lockett to die of a heart attack after a massive outcry from the public which included a scathing condemnation from amnesty international oklahoma made some minor changes to their policies regarding executions in 2015 two more botched executions were performed one of those executed was charles werner his final words were my body is on fire one of the major factors contributing to these botched executions is due to the absence of sodium thiopental which is the executioner s drug of choice for lethal injections in the u s the drug was taken off from the market in 2011 due to increasing international regulations on lethal injections states who have yet to abolish capital punishment have been scrambling to find alternatives that means once again in its history the u s is desperately clinging to a practice that the rest of the world is moving towards abolishing there are many good reasons for abolishing the death penalty but there is one reason in particular that even the staunchest advocates of the deathly penalty must admit is terrifying a recent study found that 1 in 25 prisoners who are sentenced to death are actually innocent no matter what protocols are put in place we can never know for sure if they are truly innocent i share the same stance as many capital punishment abolitionists that capital punishment is always wrong no matter what whether the convicted individual is guilty or innocent the idea that innocent people might be subjected to what is increasingly becoming not only a death sentence but a sentence to living their final moments in a state of hell my body is on fire is unconscionable oklahoma you have already proven that reforming your policies on capital punishment are a facade it s time to for you and the rest of the states that have yet to abolish the death penalty to give up this barbaric practice right now all executions are on hold in oklahoma since then not a single execution has been botched and not a single innocent person has been murdered by the state it s sounds as if they have found the perfect solution to their problem now they just need to make it permanent featured image from photo by joe raedle newsmakers\n",
            "\n",
            "Predicted Fake\n",
            "\n",
            "vatican city reuters if you ever wondered how to say fake news in latin it s nuntii fallaces and pope francis is writing a document on just that francis announced it himself in a tweet to his nearly 40 million followers on friday saying the theme of his message for the roman catholic church s next world day of social communications will be the truth will set you free fake news and journalism for peace in latin one of the nine languages the pope uses to tweet that would be veritas liberavit vos nuntii fallaces et diurniariorum opus ad pacem a vatican statement said the issue was important enough for the pope to address because fake news contributes to generating and nurturing a strong polarisation of opinions a distortion of facts it said can have repercussions at the level of individual and collective behaviour francis said in an interview last year that media that focus on scandals and spread fake news to smear politicians risk becoming like people who have a morbid fascination with excrement the vatican statement said the leader of the 1 2 billion member church wanted to offer a reflection on the causes the logic and the consequences of disinformation in the media and helping to promote professional journalism which always seeks the truth the church s next world day of social communications will be celebrated on may 13 the pope s message will be released on january 24 feast of st francis de sales the patron of journalists this version of the story corrected day of day of social communications to may 13 from jan 24\n",
            "\n",
            "Predicted Fake\n",
            "\n",
            "the democratic and republican nominees for the nov 8 presidential election are decided in a series of state by state contests the key to winning the nomination for each party is ultimately not about the popular vote but about securing the number of delegates needed to win the nomination at each party’s convention july 18 21 in cleveland for the republicans and july 25 28 in philadelphia for the democrats the following is a guide to the nominating process q is the delegate selection process the same for the republican and democratic parties a no the parties set their own rules one thing that is the same is that at each party convention a candidate needs to reach only a simple majority of the delegate votes to win the nomination q how many delegates are there a the democratic convention will be attended by about 4 763 delegates with 2 382 delegates needed to win the nomination the republican convention will be attended by 2 472 delegates with 1 237 delegates needed to win q i keep hearing about “superdelegates ” are they different from other delegates do both the republicans and democrats have superdelegates a superdelegates officially known as unpledged delegates are a sort of wild card in the nominating process but only the democrats have them the category was created for the 1984 democratic convention and according to political scientists they are a legacy of the 1980 convention when there was a fight for the nomination between president jimmy carter who was seeking a second term in the white house and senator edward kennedy of massachusetts members of congress were frustrated by their lack of influence because delegates elected to support one candidate could not switch to support another so democratic members of the house of representatives led an effort to win a role for themselves that resulted in the creation of superdelegates unlike other delegates superdelegates may change what candidate they are supporting right up to the convention there is no fixed number of superdelegates because the group is defined by various categories whose members change from one election cycle to another here is who gets to be a superdelegate all democratic members of the house of representatives and the senate the democratic governors the democratic president and vice president of the united states former democratic presidents and vice presidents former democratic leaders of the u s senate former democratic speakers of the house and former democratic minority leaders throw in the members of the democratic national committee and the former chairs of the dnc and you finally have the whole pool of superdelegates q what about the other delegates do they get to choose which candidate to support a both the democratic and republican parties send delegates to their conventions based on the popular vote in the primary elections and caucuses held in each of the 50 states but the parties have different rules on how delegates are allotted to a candidate the democratic party applies uniform rules to all states in each state delegates are allocated in proportion to the percentage of the primary or caucus vote in each district but a candidate must win at least 15 percent of the vote to be allocated any delegates the republican party lets states determine their own rules although it does dictate some things some states award delegates proportionate to the popular vote although most such states have a minimum percentage that a candidate must reach to win any delegates some other states use the winner take all method in which the candidate with the highest percentage of the popular vote is awarded all the delegates other states use a combination of the two methods states that use the proportionate method may instead use the winner take all method if one candidate wins more than 50 percent of the popular vote in addition the republican party requires that all states with nominating contests held between march 1 and march 14 use the proportional method meaning that all the states holding votes on super tuesday will have to award delegates proportionally q what happens to delegates if a candidate drops out of the race a another good question because we have certainly seen that happen this year for the democratic party in every state delegates are reallocated to the remaining candidates for the republican party it varies by state in some states delegates are required to stick with their original candidate at least through the first ballot at the republican national convention in some other states if a candidate drops out his or her delegates may immediately pledge to another candidate there is also a middle ground in which those delegates are reallocated to the remaining candidates republican national committee here here democratic national committee here battlopedia here 2016 vote for bernie here www realclearpolitics com here here www demconvention com\n",
            "\n",
            "Predicted Fake\n",
            "\n",
            "skopje macedonia s parliament has adopted a 2018 draft budget lowering the deficit to 2 7 percent of national output from 2 9 this year and forecasting growth of 3 2 percent the budget totaling 221 7 billion denars 4 28 billion and adopted late on friday allocated spending equivalent to about 400 million euros 474 28 million for capital investment while average annual inflation was expected to be 1 7 percent revenues were forecast at 193 5 billion denars the main opposition vmro dpmne party boycotted the budget vote in protest at the arrest of a former interior minister several lawmakers and political activists on charges related to violence in parliament in april which pushed the nation into a political crisis macedonia s central bank revised its economic growth forecast for 2017 down to 0 5 percent from 2 5 percent citing a poor economic performance in the first half of the year because of political instability macedonia s two year crisis over a wiretapping scandal that toppled the government of nationalist prime minister nikola gruevski ended in june with the appointment of a pro western government of the social democrat prime minister zoran zaev finance minister dragan tevdovski told parliament that the government planned to finance the deficit through a combination of domestic and foreign loans the borrowing abroad may be done through a eurobond or favorable foreign loans tevdovski said the economy of the landlocked balkan country grew by 2 4 percent in 2016 but national output contracted 1 9 percent in the second quarter of 2017 the first decline since 2012 macedonia which won independence from former yugoslavia in 1991 has made little progress towards membership of the european union and the nato alliance due to a name dispute with greece 1 0 8434 euros\n",
            "\n",
            "Predicted Fake\n",
            "\n",
            "baltimore the prospect of running for re election with donald trump at the top of the republican ticket was worrying some party lawmakers as they huddled for an annual retreat where a few forecast trouble if trump becomes the party’s presidential nominee “heaven help us ” representative peter roskam said when asked about it by a reporter but the illinois republican quickly added “i’m not going to comment on the presidential candidates at this point ” that reticence was shared by many lawmakers asked about running on trump’s coattails during an event in baltimore where senate and house of representatives republicans gathered to talk through their priorities for a coming year whose highlight is the november election some were not reluctant to express concerns trump would not help republicans if he topped the ticket despite his appeal to americans fed up with politicians said representative justin amash a favorite of the conservative tea party movement who has endorsed kentucky republican senator rand paul for president “he does appeal to the anger ” amash said of trump the outspoken real estate mogul who is front runner to be the party’s presidential nominee but he said trump’s “policy prescriptions will take us in the wrong direction and it won’t be long before many of those people are angry at him and angry at republicans again ” the idea of trump capturing the republican nomination seemed highly unlikely just months ago but it was sinking in as an actual possibility this week across the republican party in addition to voting for a new president americans will elect members of the senate and the house on nov 8 in u s politics a party’s presidential nominee has a big impact on the campaigns of candidates for both chambers of congress at the party gathering republican leaders were keen to talk policy not presidential politics “what happens above us on the presidential ballot you really don’t control ” senator john thune a senior party leader from south dakota told reporters house speaker paul ryan of wisconsin said “we are not sitting here thinking about who the nominee is going to be we don’t have time to think about that ” but in the hallways of the retreat at a hotel on baltimore’s restored waterfront trump was often the hot topic he is leading national opinion polls ahead of the feb 1 iowa caucus the first step in the nominating process representative jason chaffetz said it was important to have a presidential nominee who will not “embarrass” the rest of the party’s candidates as they defend the republican majorities in both congressional chambers chaffetz who is from utah has endorsed marco rubio a republican senator from florida polls show rubio in third place behind trump and texas senator ted cruz in iowa “marco rubio is not going to embarrass us he’s not as sensational in his comments as the others but there’s a good quality to that and i think that’s also something you look for in a president ” chaffetz said among other proposals trump has called for a temporary ban on muslims entering the united states and a wall along the u s border with mexico to stop the flow of illegal immigrants representative tom cole said republicans’ fate will be linked closely to the republican nominee whoever it is trump would be a wild card said cole of oklahoma “i don’t know what he trump will do i can see a situation where he could be a great asset because i do think he will bring some people out to vote who have not been brought before ” representative peter king of new york agreed that trump is “more of a gamble” at the top of the ticket “the conventional wisdom is that he would hurt more than he would help ” said king who was unable to attend the retreat but spoke by telephone “but on the other hand he is tapping into something among voters out there ” editing by kevin drawbaugh caren bohan and frances kerry sap is the sponsor of this coverage which is independently produced by the staff of reuters news agency\n",
            "\n",
            "Predicted Real\n",
            "\n",
            "vc row vc column width 1 1 td block trending now limit 5 td block big grid sort featured vc column vc row vc row vc column width 2 3 td block 5 limit 24 custom title hot off the press td filter default txt all ajax pagination load more border top no border top vc column vc column width 1 3 vc widget sidebar sidebar id td default vc column vc row vc row vc column width 1 1 td block 14 limit 3 custom title even more news td filter default txt all ajax pagination next prev header color 288abf vc column vc row\n",
            "\n",
            "Predicted Fake\n",
            "\n",
            "scroll to the bottom of the page for an interactive graphic   washington reuters william pryor in urging the u s supreme court in 2003 to uphold a texas law banning gay sex argued against the notion that the u s constitution should safeguard a person’s choice of partners pryor who then was alabama’s attorney general and now serves on a federal appeals court was one of 11 conservative jurists who presumptive republican presidential nominee donald trump has named as people he would consider nominating to the supreme court if elected “a constitutional right that protects ‘the choice of one’s partner’ and ‘whether and how to connect sexually’ must logically extend to activities like prostitution adultery necrophilia bestiality possession of child pornography and even incest and pedophilia ” pryor said in a legal filing to the supreme court pryor has a record of provocative remarks as do some others on trump’s list but many have established solid conservative judicial records that likely would appeal to republicans in the senate which has the power to confirm supreme court nominees and have steered clear of inflammatory rhetoric trump’s list unveiled on wednesday included judges who have indicated support for various conservative causes range in age from 41 to 58 and hail primarily from conservative and republican governed states the eight men and three women all are white the list’s release seems to have reassured some conservatives who may have doubted trump would name a genuine conservative to the high court which has a vacancy following the february death of conservative justice antonin scalia “i’ve heard nothing but positive about this people in the senate say this is going to relieve a lot of people ” said republican jeff sessions of alabama a key trump supporter in the senate the republican led senate has refused to consider democratic president barack obama’s nominee merrick garland arguing the winner of the nov 8 election to determine obama’s successor should get to fill the post scalia’s replacement could tip the ideological balance of the court now evenly divided with four conservative justices and four liberals don willett of texas is one of five state supreme court judges on the list his job is an elected one “don willett helped defend the right of texas to display the bible’s ten commandments and fought the liberals who tried to remove the words ‘under god’ from our pledge” of allegiance his campaign said in a 2012 advertisement before becoming a judge willett was part of texas’ legal team that won a supreme court battle to display the ten commandments on a monument in the state capitol despite opponents’ concerns that it amounted to government endorsement of a religion during pryor’s stint as alabama attorney general from 1997 to 2004 he described the 1973 supreme court ruling legalizing abortion nationwide as “the worst abomination in the history of constitutional law ” some of the judges on trump’s list have ruled against abortion and birth control rights federal appeals court judges diane sykes steven colloton and pryor all ruled in favor of christian objections to the mandate under obama’s healthcare law that health insurance covers birth control for women federal appeals court judge raymond gruender wrote a 2012 ruling upholding a south dakota law that requires doctors to inform patients that women who have abortions are more likely to commit suicide colloton who sits on the same court joined the opinion as on abortion and contraception the u s supreme court is closely divided on the scope of the individual right to bear arms under the constitution’s second amendment in a 2013 case thomas hardiman another federal appeals court judge dissented when the majority on his court upheld a new jersey law regulating the possession of handguns in public hardiman endorsed a broad reading of second amendment gun rights that would protect carrying weapons outside the home for self defense the list also includes david stras a member of minnesota’s supreme court in 2014 that court waded into the contentious subject of when a person who is incapacitated may be taken off life support and allowed to die the court ruled in the case of a 57 year old man with irreversible brain damage that a guardian who was given medical consent power could authorize the removal of life sustaining treatment when “interested parties” agreed it would be in the incapacitated person’s best interest stras dissented writing that because the man had already died when the case was heard the matter was moot and the court should have stayed out of it  \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position = 21416\n",
        "\n",
        "position = 21417\n",
        "\n",
        "input_array=padded_sequences[position]\n",
        "print(texts[position])\n",
        "print(input_array)\n",
        "\n",
        "input_array = np.expand_dims(input_array, 0)\n",
        "print(input_array)\n",
        "\n",
        "output_array = model.predict(input_array)\n",
        "print(output_array)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCnAfff8I4DX",
        "outputId": "be503794-1868-42f5-a791-629724988c25"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Donald Trump just couldn t wish all Americans a Happy New Year and leave it at that. Instead, he had to give a shout out to his enemies, haters and  the very dishonest fake news media.  The former reality show star had just one job to do and he couldn t do it. As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year,  President Angry Pants tweeted.  2018 will be a great year for America! As our Country rapidly grows stronger and smarter, I want to wish all of my friends, supporters, enemies, haters, and even the very dishonest Fake News Media, a Happy and Healthy New Year. 2018 will be a great year for America!  Donald J. Trump (@realDonaldTrump) December 31, 2017Trump s tweet went down about as welll as you d expect.What kind of president sends a New Year s greeting like this despicable, petty, infantile gibberish? Only Trump! His lack of decency won t even allow him to rise above the gutter long enough to wish the American citizens a happy new year!  Bishop Talbert Swan (@TalbertSwan) December 31, 2017no one likes you  Calvin (@calvinstowell) December 31, 2017Your impeachment would make 2018 a great year for America, but I ll also accept regaining control of Congress.  Miranda Yaver (@mirandayaver) December 31, 2017Do you hear yourself talk? When you have to include that many people that hate you you have to wonder? Why do the they all hate me?  Alan Sandoval (@AlanSandoval13) December 31, 2017Who uses the word Haters in a New Years wish??  Marlene (@marlene399) December 31, 2017You can t just say happy new year?  Koren pollitt (@Korencarpenter) December 31, 2017Here s Trump s New Year s Eve tweet from 2016.Happy New Year to all, including to my many enemies and those who have fought me and lost so badly they just don t know what to do. Love!  Donald J. Trump (@realDonaldTrump) December 31, 2016This is nothing new for Trump. He s been doing this for years.Trump has directed messages to his  enemies  and  haters  for New Year s, Easter, Thanksgiving, and the anniversary of 9/11. pic.twitter.com/4FPAe2KypA  Daniel Dale (@ddale8) December 31, 2017Trump s holiday tweets are clearly not presidential.How long did he work at Hallmark before becoming President?  Steven Goodine (@SGoodine) December 31, 2017He s always been like this . . . the only difference is that in the last few years, his filter has been breaking down.  Roy Schulze (@thbthttt) December 31, 2017Who, apart from a teenager uses the term haters?  Wendy (@WendyWhistles) December 31, 2017he s a fucking 5 year old  Who Knows (@rainyday80) December 31, 2017So, to all the people who voted for this a hole thinking he would change once he got into power, you were wrong! 70-year-old men don t change and now he s a year older.Photo by Andrew Burton/Getty Images.\n",
            "[70 15 82 ...  0  0  0]\n",
            "[[70 15 82 ...  0  0  0]]\n",
            "1/1 [==============================] - 0s 179ms/step\n",
            "[[9.999255e-01 7.455887e-05]]\n"
          ]
        }
      ]
    }
  ]
}