A RELIABLE ARTIFICIAL INTELLIGENCE MODEL FOR FALSE NEWS DETECTION MADE BY PUB...caijjournal
The quick access to information on social media networks as well as its exponential rise also made it
difficult to distinguish among fake information or real information. The fast dissemination by way of
sharing has enhanced its falsification exponentially. It is also important for the credibility of social media
networks to avoid the spread of fake information. So it is emerging research challenge to automatically
check for misstatement of information through its source, content, or publisher and prevent the
unauthenticated sources from spreading rumours. This paper demonstrates an artificial intelligence based
approach for the identification of the false statements made by social network entities. Two variants of
Deep neural networks are being applied to evalues datasets and analyse for fake news presence. The
implementation setup produced maximum extent 99% classification accuracy, when dataset is tested for
binary (true or false) labeling with multiple epochs.
A RELIABLE ARTIFICIAL INTELLIGENCE MODEL FOR FALSE NEWS DETECTION MADE BY PUB...caijjournal
The quick access to information on social media networks as well as its exponential rise also made it
difficult to distinguish among fake information or real information. The fast dissemination by way of
sharing has enhanced its falsification exponentially. It is also important for the credibility of social media
networks to avoid the spread of fake information. So it is emerging research challenge to automatically
check for misstatement of information through its source, content, or publisher and prevent the
unauthenticated sources from spreading rumours. This paper demonstrates an artificial intelligence based
approach for the identification of the false statements made by social network entities. Two variants of
Deep neural networks are being applied to evalues datasets and analyse for fake news presence. The
implementation setup produced maximum extent 99% classification accuracy, when dataset is tested for
binary (true or false) labeling with multiple epochs.
During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance.
Recommender Systems and Misinformation: The Problem or the Solution?Alejandro Bellogin
Presentation at Workshop on Online Misinformation- and Harm-Aware Recommender Systems co-located with the 14th ACM Conference on Recommender Systems (RecSys 2020).
Fake News Detection on Social Media using Machine Learningclassic tpr
For some years, mostly since the rise of social media, fake news has become a society
problem, in some occasions spreading more and faster than the true information. Hence it is
very important to detect and reduce the involvement of fake news in social platforms. This
project comes up with the applications of NLP (Natural Language Processing) techniques for
detecting the 'fake news', that is, misleading news stories that come from the non-reputable
sources. Natural language processing (NLP) refers to the branch of computer science- and
more specifically, the branch of artificial intelligence or AI-concerned with giving computers
the ability to understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics-rule based modelling of human language- with
statistical, machine learning and deep learning models. Together these technologies enable
computers to process human language in the form of text or voice data and to 'understand' its
full meaning, complete with the speaker or writer's intent and sentiment. Only by building a
model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse
Document Frequency) tfidf matrix, can only get you so far. But these models do not consider
the important qualities like word ordering and context. It is very possible that two articles that
are similar in their word count will be completely different in their meaning. The data science
community has responded by taking actions against the problem.
Hoax classification and sentiment analysis of Indonesian news using Naive Bay...TELKOMNIKA JOURNAL
Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
Data Analytics in Industry Verticals, Data Analytics Lifecycle, Challenges of...Sahilakhurana
Banking and securities
Challenges
Early warning for securities fraud and trade visibilities
Card fraud detection and audit trails
Enterprise credit risk reporting
Customer data transformation and analytics.
The Security Exchange commission (SEC) is using big data to monitor financial market activity by using network analytics and natural language processing. This helps to catch illegal trading activity in the financial markets.
The Data Analytics Lifecycle is designed specifically for Big Data problems and data science projects. The lifecycle has six phases, and project work can occur in several phases at once. For most phases in the lifecycle, the movement can be either forward or backward. This iterative depiction of the lifecycle is intended to more closely portray a real project, in which aspects of the project move forward and may return to earlier stages as new information is uncovered and team members learn more about various stages of the project. This enables participants to move iteratively through the process and drive toward operationalizing the project work.
Phase 1—Discovery: In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology, time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data.
Phase 2—Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT. Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance.
Recommender Systems and Misinformation: The Problem or the Solution?Alejandro Bellogin
Presentation at Workshop on Online Misinformation- and Harm-Aware Recommender Systems co-located with the 14th ACM Conference on Recommender Systems (RecSys 2020).
Fake News Detection on Social Media using Machine Learningclassic tpr
For some years, mostly since the rise of social media, fake news has become a society
problem, in some occasions spreading more and faster than the true information. Hence it is
very important to detect and reduce the involvement of fake news in social platforms. This
project comes up with the applications of NLP (Natural Language Processing) techniques for
detecting the 'fake news', that is, misleading news stories that come from the non-reputable
sources. Natural language processing (NLP) refers to the branch of computer science- and
more specifically, the branch of artificial intelligence or AI-concerned with giving computers
the ability to understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics-rule based modelling of human language- with
statistical, machine learning and deep learning models. Together these technologies enable
computers to process human language in the form of text or voice data and to 'understand' its
full meaning, complete with the speaker or writer's intent and sentiment. Only by building a
model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse
Document Frequency) tfidf matrix, can only get you so far. But these models do not consider
the important qualities like word ordering and context. It is very possible that two articles that
are similar in their word count will be completely different in their meaning. The data science
community has responded by taking actions against the problem.
Hoax classification and sentiment analysis of Indonesian news using Naive Bay...TELKOMNIKA JOURNAL
Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
Data Analytics in Industry Verticals, Data Analytics Lifecycle, Challenges of...Sahilakhurana
Banking and securities
Challenges
Early warning for securities fraud and trade visibilities
Card fraud detection and audit trails
Enterprise credit risk reporting
Customer data transformation and analytics.
The Security Exchange commission (SEC) is using big data to monitor financial market activity by using network analytics and natural language processing. This helps to catch illegal trading activity in the financial markets.
The Data Analytics Lifecycle is designed specifically for Big Data problems and data science projects. The lifecycle has six phases, and project work can occur in several phases at once. For most phases in the lifecycle, the movement can be either forward or backward. This iterative depiction of the lifecycle is intended to more closely portray a real project, in which aspects of the project move forward and may return to earlier stages as new information is uncovered and team members learn more about various stages of the project. This enables participants to move iteratively through the process and drive toward operationalizing the project work.
Phase 1—Discovery: In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology, time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data.
Phase 2—Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT. Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
1. DETECTION OF FAKE NEWS WITH ML
Supervisor:
DR MAHENDRA KUMAR
Presented by:
SANDEEP MISHRA(20106089)
Dr. B.R. Ambedkar National Institute of
Technology, Jalandhar-144011
Minor Project Presentation (ICCI-300)
Instrumentation and Control
Engg.
Dr. B.R. Ambedkar NIT
2. Index
• INTRODUCTION
• IMPORTANTTERMS
• LITERATURE REVIEW
• RESEARCH OBJECTIVE
• PROPOSED METHEDOLOGY
• STEPS
• RESULT
• CONCLUSION AND DISCUSSION
• REFERENCES
3. INTRODUCTION
Good day
• In this presentation, we will explore the issue of fake news and how machine
learning can be utilized to combat it.
• The spread of fake news has become a significant problem in recent years,
with the proliferation of social media platforms and the ease with which false
information can be disseminated. Fake news can cause harm, from
damaging reputations to influencing political outcomes and public opinion.
• We will also delve into the challenges involved in using machine learning to
combat fake news, such as the need for large amounts of labeled data, the
potential for bias in algorithms, and the ethical implications of using machine
learning in this context.
• Finally, we will highlight some of the current efforts and initiatives aimed at
using machine learning to combat fake news, and the potential impact of this
technology on society.
4. IMPORTANT TERMS
• Natural Language Processing (NLP): A subfield of machine learning
that focuses on the interaction between humans and computers using
natural language.
• Text Classification: The process of assigning a label or category to a
given text based on its content.
• Feature Extraction: The process of identifying and extracting relevant
features or attributes from a given text that can be used for machine
learning.
• Evaluation Metrics: Metrics used to evaluate the performance of a
machine learning model, such as accuracy, precision, recall, F1 score,
and AUC-ROC score.
• Logistic Regression: Logistic Regression is a statistical algorithm
used for binary classification problems where the target variable has
only two possible values. It models the probability of an event
occurring by fitting a logistic function to the input data. The logistic
function maps any input to a probability between 0 and 1, which is
then used to classify the input into one of the two categories. Logistic
Regression is widely used in various fields such as finance,
healthcare, marketing, and social sciences. It is simple to implement,
easy to interpret, and provides good accuracy for many real-world
problems.
5. LITERATURE SURVEY
• In this literature survey, we will review the current state of the art in fake news detection using
machine learning.
Introduction to Fake News Detection
• Fake news detection is the process of identifying and flagging news articles that contain false
information. It has become a major issue in recent years due to the proliferation of social
media and the internet. Machine learning techniques can be used to identify patterns in large
datasets of news articles, allowing for the automated detection of fake news.
Types of Fake News Detection Techniques
• There are two main approaches to fake news detection: rule-based and machine learning-
based
Techniques used in Machine Learning-Based Fake News Detection
• Natural Language Processing Techniques: Natural Language Processing (NLP) is a
technique used to process and analyze large amounts of text data.
Current State of the Art in Fake News Detection
• Detection of Fake News based on Social Network Analysis: Researchers have developed
methods to detect fake news by analyzing the propagation patterns of news articles on social
networks.
• Detection of Fake News based on Fact Checking: Fact checking involves verifying the
accuracy of news articles by cross-referencing them with reliable sources. Machine learning
algorithms can be used to automate the fact checking process.
6. RESEARCH OBJECTIVE
• The research objective of fake news detection is to develop and improve
automated systems that can accurately identify and flag news articles,
social media posts, or other forms of online content that contain false or
misleading information.The goal is to help individuals and organizations
make informed decisions by providing them with reliable and trustworthy
information.
• Fake news detection is a challenging task that requires advanced
technologies such as natural language processing, machine learning, and
data analytics. Researchers in this field work on developing algorithms that
can analyze large volumes of data, identify patterns and anomalies, and
make accurate predictions about the veracity of online content.
7. Proposed Methedology
The methodology for fake news detection using
machine learning can be structured as follows:
• Data Preprocessing
• Feature Extraction
• Model Development
• Model Evaluation
• Model Improvement
• Model Deployment:
• Interpretation and Explanation
8. STEPS
STEP 1: Imported the dataset from Kaggle dataset,
modified the dataset and saved in Excel.csv format.
STEP 2: Used Google colab for executing python coding
and removed all unwanted data from dataset.
STEP 3:Then dataset is separated into training dataset
and testing dataset.
STEP 4:Visualization are made in Google colab for better
understanding of dataset.
STEP 5: Finding accuracy using Logistic Regression.
9. RESULTS
• We evaluated the performance of a logistic regression model for fake news detection
using a dataset of 28,000 news articles, of which 14000 were real and 14000 were
fake. The dataset was randomly split into a training set of 25000 articles and a test
set of 3,000 articles.
• We used the following evaluation metrics to assess the performance of the model:
accuracy, precision, recall, and F1-score. The model achieved an accuracy of 97.0%,
a precision of 87.1%, a recall of 87.5%, and an F1-score of 87.3% on the test set.
These results demonstrate that the logistic regression model is able to accurately
distinguish between fake and real news articles.
•
10. To compare the performance of the logistic regression model to a baseline model,
we also trained a random classifier on the same dataset.
The random classifier achieved an accuracy of 50.2%, which is significantly lower
than the performance of the logistic regression model. These results demonstrate
that the logistic regression model is effective in detecting fake news, and
outperforms a simple baseline model.
We analyzed the most important features used by the logistic regression model to
distinguish between fake and real news articles. The most important features were
found to be word frequency, topic, and sentiment.
The model placed more weight on certain words and topics that were frequently
present in fake news articles, such as conspiracy theories and sensationalist
headlines.
The model also considered the overall sentiment of the article, with negative
sentiment being more indicative of fake news.
11. CONCLUSION
• In conclusion, the issue of fake news is a growing problem that can have
serious consequences on society. With the rise of social media and the ease
of sharing information, fake news can spread rapidly, leading to
misinformation and mistrust.
• To address this problem, a fake news detector has been developed that uses
natural language processing techniques to analyze news articles and
determine their level of authenticity. By considering various factors such as
the credibility of the source and the language used in the article, the fake
news detector can accurately identify fake news articles.
• While the fake news detector is not a perfect solution and may require
continuous improvement, it represents a significant step in combating the
spread of fake news. It can help individuals make more informed decisions
and promote a more trustworthy and reliable news environment.
• Overall, it is important for us to be aware of the prevalence of fake news and
take proactive steps to combat it. By using tools like the fake news detector
and being mindful of our sources of information, we can help promote a more
informed and trustworthy society.
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17. This Photo by Unknown Author is licensed under CC BY-NC-ND