This document discusses fake news detection through machine learning models. It introduces the topics of fake news characterization, TF-IDF vectorization for feature extraction, and the passive-aggressive algorithm for classification. An example of fake news is provided. The conclusion states that most fake news detection focuses on unsupervised and supervised learning using textual content features, and there is a tradeoff between precision and recall for these models.
Fake news has a negative impact on individuals and society, hence the detection of fake news is becoming a bigger field of interest for data scientists. Attempts to leverage artificial intelligence technologies particularly machine/deep learning techniques and natural language processing (NLP) to automatically detect fake news and prevent its viral spread have recently been actively discussed.
Large technology companies have begun to take steps to address this trend. For example, Google has adjusted its news rankings to prioritize well-known sites and has banned sites with a history of spreading fake news. Facebook has integrated fact checking organizations into its platform.
This SlideShare explores the concept of NLP for detecting fake news in brief.
Recently, fake news has been incurring many problems to our society. As a result, many researchers have been working on identifying fake news. Most of the fake news detection systems utilize the linguistic feature of the news. However, they have difficulty in sensing highly ambiguous fake news which can be detected only after identifying meaning and latest related information. In this paper, to resolve this problem, we shall present a new Korean fake news detection system using fact DB which is built and updated by human's direct judgement after collecting obvious facts. Our system receives a proposition, and search the semantically related articles from Fact DB in order to verify whether the given proposition is true or not by comparing the proposition with the related articles in fact DB. To achieve this, we utilize a deep learning model, Bidirectional Multi Perspective Matching for Natural Language Sentence BiMPM , which has demonstrated a good performance for the sentence matching task. However, BiMPM has some limitations in that the longer the length of the input sentence is, the lower its performance is, and it has difficulty in making an accurate judgement when an unlearned word or relation between words appear. In order to overcome the limitations, we shall propose a new matching technique which exploits article abstraction as well as entity matching set in addition to BiMPM. In our experiment, we shall show that our system improves the whole performance for fake news detection. Prasanth. K | Praveen. N | Vijay. S | Auxilia Osvin Nancy. V ""Fake News Detection using Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30014.pdf
Paper Url : https://www.ijtsrd.com/engineering/information-technology/30014/fake-news-detection-using-machine-learning/prasanth-k
Fake news has a negative impact on individuals and society, hence the detection of fake news is becoming a bigger field of interest for data scientists. Attempts to leverage artificial intelligence technologies particularly machine/deep learning techniques and natural language processing (NLP) to automatically detect fake news and prevent its viral spread have recently been actively discussed.
Large technology companies have begun to take steps to address this trend. For example, Google has adjusted its news rankings to prioritize well-known sites and has banned sites with a history of spreading fake news. Facebook has integrated fact checking organizations into its platform.
This SlideShare explores the concept of NLP for detecting fake news in brief.
Recently, fake news has been incurring many problems to our society. As a result, many researchers have been working on identifying fake news. Most of the fake news detection systems utilize the linguistic feature of the news. However, they have difficulty in sensing highly ambiguous fake news which can be detected only after identifying meaning and latest related information. In this paper, to resolve this problem, we shall present a new Korean fake news detection system using fact DB which is built and updated by human's direct judgement after collecting obvious facts. Our system receives a proposition, and search the semantically related articles from Fact DB in order to verify whether the given proposition is true or not by comparing the proposition with the related articles in fact DB. To achieve this, we utilize a deep learning model, Bidirectional Multi Perspective Matching for Natural Language Sentence BiMPM , which has demonstrated a good performance for the sentence matching task. However, BiMPM has some limitations in that the longer the length of the input sentence is, the lower its performance is, and it has difficulty in making an accurate judgement when an unlearned word or relation between words appear. In order to overcome the limitations, we shall propose a new matching technique which exploits article abstraction as well as entity matching set in addition to BiMPM. In our experiment, we shall show that our system improves the whole performance for fake news detection. Prasanth. K | Praveen. N | Vijay. S | Auxilia Osvin Nancy. V ""Fake News Detection using Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30014.pdf
Paper Url : https://www.ijtsrd.com/engineering/information-technology/30014/fake-news-detection-using-machine-learning/prasanth-k
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
DETECTION OF FAKE ACCOUNTS IN INSTAGRAM USING MACHINE LEARNINGijcsit
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
Detailed Research on Fake News: Opportunities, Challenges and MethodsMilap Bhanderi
This paper is submitted at Dalhousie University for Technology Innovation course as a deliverable. This paper focuses on the opportunities, challenges and methods for Fake news.
IAMAI Factly Report: People below age 20 or above 50 more susceptible to fake...Social Samosa
An extensive survey based study titled, ‘Countering Misinformation (Fake News) in India’ by Internet and Mobile Association of India (IAMAI) and Factly has found that people below the age of 20 or those above the age of 50 are most susceptible to be swayed by fake news.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
DETECTION OF FAKE ACCOUNTS IN INSTAGRAM USING MACHINE LEARNINGijcsit
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
With the advent of the Internet and social media, while hundreds of people have benefitted from the vast sources of information available, there has been an enormous increase in the rise of cyber-crimes, particularly targeted towards women. According to a 2019 report in the [4] Economics Times, India has witnessed a 457% rise in cybercrime in the five year span between 2011 and 2016. Most speculate that this is due to impact of social media such as Facebook, Instagram and Twitter on our daily lives. While these definitely help in creating a sound social network, creation of user accounts in these sites usually needs just an email-id. A real life person can create multiple fake IDs and hence impostors can easily be made. Unlike the real world scenario where multiple rules and regulations are imposed to identify oneself in a unique manner (for example while issuing one’s passport or driver’s license), in the virtual world of social media, admission does not require any such checks. In this paper, we study the different accounts of Instagram, in particular and try to assess an account as fake or real using Machine Learning techniques namely Logistic Regression and Random Forest Algorithm.
Detailed Research on Fake News: Opportunities, Challenges and MethodsMilap Bhanderi
This paper is submitted at Dalhousie University for Technology Innovation course as a deliverable. This paper focuses on the opportunities, challenges and methods for Fake news.
IAMAI Factly Report: People below age 20 or above 50 more susceptible to fake...Social Samosa
An extensive survey based study titled, ‘Countering Misinformation (Fake News) in India’ by Internet and Mobile Association of India (IAMAI) and Factly has found that people below the age of 20 or those above the age of 50 are most susceptible to be swayed by fake news.
FAKE INFORMATION & WORD-OF-MOUTH BEHAVIORDisha Ghoshal
As part of an assignment of a course in Brand Management taught by well renowned Prof. Sridhar Samu and S Bhardwaj who are ace in the field of Market Research and Brand Management and teach at Great Lakes Institute of Management Chennai
Information was complied by the data available on the Internet, personal interviews, a social experiment and I have tried my best to maintain correctness and credits as much as possible.
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.
Demystifying Online Misinformation, with Dr. Claire Wardle, co-founder and Ex...Damian Radcliffe
In this Q&A based on an episode of the Hearst Demystifying Media podcast, we talk to Dr. Claire Wardle about the rapidly evolving strategies that promoters of disinformation are using to influence public opinion--and what journalists can do about it.
Claire Wardle is the co-founder and Executive Chair of First Draft, the world’s foremost nonprofit focused on research and practice to address mis- and disinformation. In 2017 she co-authored a report for the Council of Europe entitled, Information Disorder: Toward an interdisciplinary framework for research and policymaking. Previously, she was a Research Fellow at the Shorenstein Center for Media, Politics and Public Policy, and also the Research Director at the Tow Center for Digital Journalism at Columbia Journalism School.
LINKS
https://clairewardle.com/
https://twitter.com/cward1e
https://firstdraftnews.org/
Media literacy in the age of information overloadGmeconline
We live in the most interesting times as far as the media is concerned. In fact as I approach the topic.These lines from Charles Dickens signifying the scenario of the French revolution came instantly to my mind – yes there is an upheaval going on in the media too..and it is marked with opposing views on the continuum-... Read More
How social media is redefining the approach to research.
For more white papers and webinars, go to http://www.sldesignlounge.com
Or visit us at http://www.sld.com
Existence of Social Media in Pandemic Boon or Baneijtsrd
This article aims to highlight the role and accountability of media and social networking sites in the pandemic situation. In the contemporary world, where everything is being advanced, the role and position of media and social networking sites have also been changed and become more strong. The year 2020 has marked its name in history due to the lockdown and closing of all the borders and states. This kind of lockdown has never ever been happened in the society and in the world. This is due to the virus namely Corona Virus, due to which this type of situation has occurred. Now, in such hard time, the role of media has also increased. Their role is just not to protect the life of people but also ensure that no wrong information be shared with the people which resulted in misleading the folk. Along with that, the role of media is to be the safeguard of the society and help the authorities to know the position of people living in countryside and urban areas. Hence, this article would try to analyze the role played by media and tries to find out whether social networking and media are boon for the society or bane in this alarming situation. Gurpreet Kaur "Existence of Social Media in Pandemic: Boon or Bane" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30849.pdf Paper Url :https://www.ijtsrd.com/humanities-and-the-arts/social-science/30849/existence-of-social-media-in-pandemic-boon-or-bane/gurpreet-kaur
BBC's shoddy analysis about fake news spread in India
PS: Fake news is being spread, there is NO doubt about that.
But there is no easy way to arrive at the outlandish conclusions they have arrived at. Take a look :-) They start off with some "data analysis" and call it qualitative research.
Health Care Social Media for Medical Device Manufacturers - FDA - Presentatio...David Harlow
Health Care Social Media in the Face of Continued FDA Regulatory Uncertainty for Medical Device Manufacturers, Presented at MassMEDIC conference 05 13 2011
"Media and Information Literacy consists of the knowledge, the attitudes, and the sum of the skills needed to know when and what information is needed; where and how to obtain that information; how to evaluate it critically and organise it once it is found; and how to use it in an ethical way. The concept extends beyond communication and information technologies to encompass learning, critical thinking, and interpretative skills across and beyond professional and educational boundaries. Media and Information Literacy includes all types of information resources: oral, print, and digital. Media and Information Literacy is a basic human right in an increasingly digital, interdependent, and global world, and promotes greater social inclusion. It can bridge the gap between the information rich and the information poor. Media and Information Literacy empowers and endows individuals with knowledge of the functions of the media and information systems and the conditions under which these functions are performed" (IFLA, 2011).
"We live in a world where the quality of information we receive largely determines our choices and ensuing actions, including our capacity to enjoy fundamental freedoms and the ability for self-determination and development. Driven by technological improvements in telecommunications, there is also a proliferation of media and other information providers through which vast amounts of information and knowledge are accessed and shared by citizens. Adding to and emanating from this phenomenon is the challenge to assess the relevance and the reliability of the information" (UNESCO, p. 11, 2011).
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
2. CONTENTS
• INTRODUCTION
• WHAT IS FAKE NEWS..?
• FAKE NEWS CHARACTERIZATION
• FAKE NEWS DETECTION
• WHAT IS TFIDFVECTORIZER
• WHAT IS PASSIVE AGGRESSIVE ALGORITHM
• EXAMPLE
• CONCLUSION
3. INTRODUCTION
• FAKE NEWS SPREADS LIKE A WILDLIFE AND THIS IS A BIG ISSUE IN THIS ERA.
• FOR SOME YEARS, MOSTLY SINCE THE RISE OF SOCIAL MEDIA, FAKE NEWS HAVE BECOME
A SOCIETY PROBLEM, IN SOME OCCASION SPREADING MORE AND FASTER THAN THE TRUE
INFORMATION. IN THIS PAPER I EVALUATE THE PERFORMANCE OF ATTENTION
MECHANISM FOR FAKE NEWS DETECTION ON TWO DATASETS, ONE CONTAINING
TRADITIONAL ONLINE NEWS ARTICLES AND THE SECOND ONE NEWS FROM VARIOUS
SOURCES.
• IT SHOWS THAT ATTENTION MECHANISM DOES NOT WORK AS WELL AS EXPECTED. IN
ADDITION, I MADE CHANGES TO ORIGINAL ATTENTION MECHANISM PAPER, BY USING
WORD2VEC EMBEDDING, THAT PROVES TO WORKS BETTER ON THIS PARTICULAR CASE.
4. WHAT IS FAKE NEWS..?
• A type of yellow journalism,fake news encapsulates pieces of news that may be hoaxes and is generally spread
through social media and other online media. This is often done to further or impose certain ideas and is often
achieved with political agendas.Such news items may contain false or exaggerated claims, and may end up being
viralized by algorithms, and users may end up in a filter bubble.
• Fake news has quickly become a society problem, being used to propagate false or rumour information in order
to change peoples behaviour.
• In order to work on fake news detection, it is important to understand what is fake news and how they are
characterized. The following is based on Fake News Detection on Social Media: A Data Mining Perspective.
• The first is characterization or what is fake news and the second is detection. In order to build detection models, it
is need to start by characterization, indeed, it is need to understand what is fake news before trying to detect them.
5. FAKE NEWS CHARACTERIZATION
Fake news definition is made of two parts: authenticity and intent. Authenticity means that fake
news content false information that can be verified as such, which means that conspiracy theory
is not included in fake news as there are difficult to be proven true or false in most cases. The
second part, intent, means that the false information has been written with the goal of
misleading the reader.
Fake news on social media: from characterization to detection.
6. WHAT IS TFIDFVECTORIZER
TF (Term Frequency): The number of times a word appears in a document is its Term Frequency. A
higher value means a term appears more often than others, and so, the document is a good match
when the term is part of the search terms.
IDF (Inverse Document Frequency): Words that occur many times a document, but also occur many
times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire
corpus.
The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features.
7. WHAT IS PASSIVE AGGRESSIVE ALGORITHM
Passive Aggressive algorithms are online learning algorithms. Such an
algorithm remains passive for a correct classification outcome, and turns
aggressive in the event of a miscalculation, updating and adjusting. Unlike
most other algorithms, it does not converge. Its purpose is to make updates
that correct the loss, causing very little change in the norm of the weight
vector.
8. EXAMPLE
Finally a INDIAN student from PONDICHERRY university, named RAMU found a home
remedy cure for Covid-19 which is for the very first time accepted by WHO.
He proved that by adding 1 tablespoon of black pepper powder to 2 table spoons of
honey
and some ginger juice for consecutive 5 days would suppress the effects of corona.
And eventually go away 100%
Entire world is starting to accept this remedy.
Finally a good news In 2020!!
PLEASE CIRCULATE THIS INFORMATION TO ALL YOUR FAMILY MEMBERS
AND FRIENDS.
9. CONCLUSION
This works focus on textual news content features. Indeed, other features related to
social media are difficult to acquire. For example, users information is difficult to
obtain on Facebook, as well as post information. In addition, the different datasets
that have been presented at style-based model does not provide any other information
than textual ones.
Looking at Different approaches to fake news detection it can be seen that the main
focus will be made on unsupervised and supervised learning models using textual
news content. It should be noted that machine learning models usually comes with a
trade-off between precision and recall and thus that a model which is very good at
detected fake news might have a high false positive rate as opposite to a model with a
low false positive rate which might not be good at detecting them. This cause ethical
questions such as automatic censorship that will not be discussed here.