International Journal of HRM and Organizational Behavior (IJHRMOB) is an online International Journal published half yearly. It is a peer reviewed journal aiming to communicate high quality original research work, reviews, in the fields of Human Resource Management and Organizational Behavior. The Journal publishes research and review articles.
https://ijhrmob.com/
Hyderabad Telangana
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.
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.
Artificial Intelligence For Investigative ReportingJennifer Strong
This paper describes an artificial intelligence system called the "Story Discovery Engine" that is designed to help journalists, especially novice investigators, identify original investigative story ideas by analyzing large datasets. The system functions like an expert system, applying formal rules and informal journalistic expertise to uncover anomalies and patterns in data. The author developed a prototype that analyzed Philadelphia education data and produced several impactful investigative stories on inequities. This suggests the Story Discovery Engine could help newsrooms generate more compelling, data-driven public affairs reporting by augmenting human creativity with computational analysis.
The document proposes a fake news detection system that uses machine learning algorithms. It involves modules for importing libraries and datasets, preprocessing the data, splitting it into train and test sets, using algorithms like XGBoost, AdaBoost and Random Forest for prediction, and matching the predictions to a manual input. The system is designed to detect and filter misleading websites. It will analyze factors like the title and content to identify fake news reliably. The methodology, architecture, algorithms and metrics are discussed in detail with references to related work on the topic.
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.
Fake News Detection Using Machine LearningIRJET Journal
This document proposes a machine learning approach for detecting fake news using support vector machines. It discusses preprocessing news data using techniques like TF-IDF, extracting features related to text, date, source and author, and training a support vector machine classifier on the preprocessed data. The proposed system architecture involves preprocessing, training a model on the training data, validating it on test data, adjusting parameters to improve accuracy, and then using the model to classify new unlabeled news. Prior research that used techniques like n-gram analysis, naive Bayes classifiers and linear support vector machines for fake news detection are also reviewed. The conclusion is that the proposed approach using support vector machines can help identify fake news effectively.
Intelligent Models for predicting diseases whether building a model to help the doctor or even preventing its spread in an area globally, is increasing day by day. Here we present a noble approach to predict the disease prone area using the power of Text Analysis and Machine Learning. Epidemic Search model using the power of the social network data analysis and then using this data to provide a probability score of the spread and to analyse the areas whether going to suffer from any epidemic spread-out, is the main focus of this work. We have tried to analyse and showcase how the model with different kinds of pre-processing and algorithms predict the output. We have used the combination of words-n grams, word embeddings and TFIDF with different data mining and deep learning algorithms like SVM, Naïve Bayes and RNN-LSTM. Naïve Bayes with TF-IDF performed better in comparison to others.
EPIDEMIC OUTBREAK PREDICTION USING ARTIFICIAL INTELLIGENCEijcsit
Intelligent Models for predicting diseases whether building a model to help the doctor or even preventing its spread in an area globally, is increasing day by day. Here we present a noble approach to predict the disease prone area using the power of Text Analysis and Machine Learning. Epidemic Search model using the power of the social network data analysis and then using this data to provide a probability score of the spread and to analyse the areas whether going to suffer from any epidemic spread-out, is the main focus of this work. We have tried to analyse and showcase how the model with different kinds of pre-processing and algorithms predict the output. We have used the combination of words-n grams, word embeddings and TFIDF with different data mining and deep learning algorithms like SVM, Naïve Bayes and RNN-LSTM. Naïve Bayes with TF-IDF performed better in comparison to others.
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.
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.
Artificial Intelligence For Investigative ReportingJennifer Strong
This paper describes an artificial intelligence system called the "Story Discovery Engine" that is designed to help journalists, especially novice investigators, identify original investigative story ideas by analyzing large datasets. The system functions like an expert system, applying formal rules and informal journalistic expertise to uncover anomalies and patterns in data. The author developed a prototype that analyzed Philadelphia education data and produced several impactful investigative stories on inequities. This suggests the Story Discovery Engine could help newsrooms generate more compelling, data-driven public affairs reporting by augmenting human creativity with computational analysis.
The document proposes a fake news detection system that uses machine learning algorithms. It involves modules for importing libraries and datasets, preprocessing the data, splitting it into train and test sets, using algorithms like XGBoost, AdaBoost and Random Forest for prediction, and matching the predictions to a manual input. The system is designed to detect and filter misleading websites. It will analyze factors like the title and content to identify fake news reliably. The methodology, architecture, algorithms and metrics are discussed in detail with references to related work on the topic.
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.
Fake News Detection Using Machine LearningIRJET Journal
This document proposes a machine learning approach for detecting fake news using support vector machines. It discusses preprocessing news data using techniques like TF-IDF, extracting features related to text, date, source and author, and training a support vector machine classifier on the preprocessed data. The proposed system architecture involves preprocessing, training a model on the training data, validating it on test data, adjusting parameters to improve accuracy, and then using the model to classify new unlabeled news. Prior research that used techniques like n-gram analysis, naive Bayes classifiers and linear support vector machines for fake news detection are also reviewed. The conclusion is that the proposed approach using support vector machines can help identify fake news effectively.
Intelligent Models for predicting diseases whether building a model to help the doctor or even preventing its spread in an area globally, is increasing day by day. Here we present a noble approach to predict the disease prone area using the power of Text Analysis and Machine Learning. Epidemic Search model using the power of the social network data analysis and then using this data to provide a probability score of the spread and to analyse the areas whether going to suffer from any epidemic spread-out, is the main focus of this work. We have tried to analyse and showcase how the model with different kinds of pre-processing and algorithms predict the output. We have used the combination of words-n grams, word embeddings and TFIDF with different data mining and deep learning algorithms like SVM, Naïve Bayes and RNN-LSTM. Naïve Bayes with TF-IDF performed better in comparison to others.
EPIDEMIC OUTBREAK PREDICTION USING ARTIFICIAL INTELLIGENCEijcsit
Intelligent Models for predicting diseases whether building a model to help the doctor or even preventing its spread in an area globally, is increasing day by day. Here we present a noble approach to predict the disease prone area using the power of Text Analysis and Machine Learning. Epidemic Search model using the power of the social network data analysis and then using this data to provide a probability score of the spread and to analyse the areas whether going to suffer from any epidemic spread-out, is the main focus of this work. We have tried to analyse and showcase how the model with different kinds of pre-processing and algorithms predict the output. We have used the combination of words-n grams, word embeddings and TFIDF with different data mining and deep learning algorithms like SVM, Naïve Bayes and RNN-LSTM. Naïve Bayes with TF-IDF performed better in comparison to others.
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
IRJET- Fake News Detection and Rumour Source IdentificationIRJET Journal
This document discusses methods for detecting fake news and identifying the source of rumors on social media. It proposes using Bayesian classification to classify information into real or fake categories based on the outputs. If the combined outputs from the classes do not match, then the information is considered fake. It also discusses using a reverse dissemination strategy to identify a group of suspects for the original rumor source, rather than examining each individual. This addresses issues with identifying sources. The method aims to identify the source node based on which nodes have accepted the rumor. Machine learning and natural language processing techniques are used to detect fake news from article content.
This research proposal aims to analyze the linguistic patterns in selected controversial news articles and social media posts to determine if they contain factual information or fake news. The study will use content analysis to examine 50 sentences from 10 online articles and social media posts published from 2020 to 2023. The sentences will be classified as stating facts or opinions, and their semantic structure and linguistic patterns will be analyzed. The results intend to help improve media literacy and identify cues that can point to fake news.
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.
Development of a Web Application for Fake News Classification using Machine l...IRJET Journal
This document summarizes a research paper that proposes a machine learning model to classify news articles as real or fake. It discusses developing a web application using logistic regression for fake news classification. The paper reviews previous research on fake news detection methods and feature extraction techniques. It then outlines the proposed system's workflow, including data collection and preprocessing, model training, and classification. The system aims to predict if new articles are real or fake by comparing text to an existing dataset. If the accuracy is over 90%, the news is labeled real; otherwise it is labeled fake. The results suggest the model can accurately classify news at a 95% rate.
1. The document presents a study on automated detection of fake news from social media.
2. It proposes using account features like number of hashtags, URLs, mentions, followers etc. and a random forest classifier to detect fake news with 99.8% precision.
3. The random forest approach is compared to other classifiers like decision trees, Naive Bayes and neural networks, which achieve lower precision of 98.4%, 92.6% and 62.7% respectively.
With the spread of social media platforms and the proliferation of misleading news, misinformation
detection within microblogging platforms has become a real challenge. During the Covid-19 pandemic,
many fake news and rumors were broadcasted and shared daily on social media. In order to filter out these
fake news, many works have been done on misinformation detection using machine learning and sentiment
analysis in the English language. However, misinformation detection research in the Arabic language on
social media is limited. This paper introduces a misinformation verification system for Arabic COVID-19
related news using an Arabic rumors dataset on Twitter. We explored the dataset and prepared it using
multiple phases of preprocessing techniques before applying different machine learning classification
algorithms combined with a semantic analysis method. The model was applied on 3.6k annotated tweets
achieving 93% best overall accuracy of the model in detecting misinformation. We further build another
dataset of Covid-19 related claims in Arabic to examine how our model performs with this new set of
claims. Results show that the combination of machine learning techniques and linguistic analysis achieves
the best scores reaching 92% best accuracy in detecting the veracity of sentences of the new dataset.
COMBINING MACHINE LEARNING AND SEMANTIC ANALYSIS FOR EFFICIENT MISINFORMATION...ijcsit
With the spread of social media platforms and the proliferation of misleading news, misinformation
detection within microblogging platforms has become a real challenge. During the Covid-19 pandemic,
many fake news and rumors were broadcasted and shared daily on social media. In order to filter out these
fake news, many works have been done on misinformation detection using machine learning and sentiment
analysis in the English language. However, misinformation detection research in the Arabic language on
social media is limited. This paper introduces a misinformation verification system for Arabic COVID-19
related news using an Arabic rumors dataset on Twitter. We explored the dataset and prepared it using
multiple phases of preprocessing techniques before applying different machine learning classification
algorithms combined with a semantic analysis method. The model was applied on 3.6k annotated tweets
achieving 93% best overall accuracy of the model in detecting misinformation. We further build another
dataset of Covid-19 related claims in Arabic to examine how our model performs with this new set of
claims. Results show that the combination of machine learning techniques and linguistic analysis achieves
the best scores reaching 92% best accuracy in detecting the veracity of sentences of the new dataset.
Epidemiological Modeling of News and Rumors on TwitterParang Saraf
Abstract: Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
1) Your Research Project on the surveillance state consists of .docxcroftsshanon
1)
Your Research Project on the surveillance state consists of two parts:
1 a Powerpoint presentation consisting of at least 12 slides not including title and references.
2. 750 word research paper with at least 3 sources.
You must include at least 3 quotes from your sources enclosed in quotation marks and cited in-line.
There should be no lists - bulleted, numbered or otherwise.
Write in essay format with coherent paragraphs not in outline format.
Do your own work. Zero points will be awarded if you copy other's work and do not cite your source or you use word replacement software.
The topic must be appropriate for graduate level. Find a topic that we covered in the course and dig deeper or find something that will help you in your work or in a subject area of interest related to the course topic. Use academically appropriate resources which you can find in the
Danforth Library Research Databases.
Topic :(Identity Theft in internet)
Bernal, P. (2016). Data gathering, surveillance and human rights: recasting the debate.
Journal of Cyber Policy
,
1
(2), 243-264.
Bernal in this journal attempts to determine how surveillance affects human rights which has raised a lot of concerns of late among many workers. Looking at how surveillance has gained a lot of significance in many companies, most people have raised concerns regarding how this technology denies them their privacy. The author determines the various issues raised by gathering data from different organizations and determining the way they respond to this issue. He goes on to summarize the various issues and how they can be mitigated.
This resource is important as it will allow me develop a strong thesis statement and at the same time develop a good discussion. The resource will provide a good insight on how surveillance impacts on people’s privacy and the way they respond to the situation. Drawing from this resource I will be able to acquire a convincing study which will allow readers to clearly understand how different people think and respond to the situation.
Gangadharan, S. P. (2017). The downside of digital inclusion: Expectations and experiences of privacy and surveillance among marginal Internet users.
New Media & Society
,
19
(4), 597-615.
The author of this article draws his study as a fully-published analyst, and also as a close research concerning how the inclusion of digital technology relate to individual privacy, to determine how it impacts, explain the way it affects people’s privacy, who are affected more, implication’s on persons and how it can be controlled. The author goes on to denote significant assumptions supporting the notion that this situation has raised concern and it should be addressed.
Thus, by using this resource I will be able to develop my paper majoring on the implications of surveillance technology to workers and other individuals. The author of this article has also applied other resources which will be supp.
Detection of Fake News Using Machine LearningIRJET Journal
This document summarizes a literature review on detecting fake news using machine learning. It discusses how machine learning classifiers can be trained to automatically identify fake news. Specifically, it addresses three research questions: 1) Why machine learning is needed to detect fake news, 2) Which machine learning classifiers can be used, and 3) How the classifiers are trained. It finds that common classifiers like support vector machines, naive Bayes, logistic regression, random forests, and recurrent neural networks have been effectively used to detect fake news by analyzing content. Accuracy of the classifiers depends on how well they are trained on labeled datasets.
How does fakenews spread understanding pathways of disinformation spread thro...Araz Taeihagh
What are the pathways for spreading disinformation on social media platforms? This article addresses this question by collecting, categorising, and situating an extensive body of research on how application programming interfaces (APIs) provided by social media platforms facilitate the spread of disinformation. We first examine the landscape of official social media APIs, then perform quantitative research on the open-source code repositories GitHub and GitLab to understand the usage patterns of these APIs. By inspecting the code repositories, we classify developers' usage of the APIs as official and unofficial, and further develop a four-stage framework characterising pathways for spreading disinformation on social media platforms. We further highlight how the stages in the framework were activated during the 2016 US Presidential Elections, before providing policy recommendations for issues relating to access to APIs, algorithmic content, advertisements, and suggest rapid response to coordinate campaigns, development of collaborative, and participatory approaches as well as government stewardship in the regulation of social media platforms.
Clustering analysis on news from health OSINT data regarding CORONAVIRUS-COVI...ALexandruDaia1
Our primarly goal was to detect clusters via gensim libraries in news data consisting ofinformation regarding health and threats. We identified clusters for the periodscorresponding: i) Jannuary 2006 until the end of 2019, as December 2019 is considered thefirst month in which information about CORONVIRUS COVID-19 was made public; ii)between the 1st of Jannuary 2019 and 31st December 2019; and iii) between the 31st ofDecember 2019 and the 14th of April 2020. We conducted experiments using naturallanguage on open source intelligence data offered generously by brica.de, a providerspecialized in Business Risk Intelligence & Cyberthreat Awareness.
Amani Channel's research: "Gatekeeping and Citizen Journalism: A Qualitative Examination of Participatory Media." Presented 8/5/10 at AEJMC 2010, Denver.
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Detection and resolution of rumours in social mediaObedullahFahad
This document provides a survey of research on the detection and resolution of rumors on social media. It discusses characteristics of rumors, how they spread on social media, challenges they pose, and approaches that have been studied for rumor detection, tracking, stance classification, and veracity classification. Key points include defining rumors and their temporal characteristics, how early studies differed from current social media analysis, challenges rumors pose in domains like news, crises, public opinion and stock markets, and machine learning approaches that have been applied to these rumor analysis tasks.
A Brief Survey Of Text Mining And Its ApplicationsCynthia King
This document discusses text mining and its applications, specifically in the context of analyzing large amounts of text data related to COVID-19. It first provides background on the growth of research articles published about COVID-19 since the start of the pandemic. It then discusses how text mining techniques like text clustering, text categorization, and document summarization could help scientists and researchers more efficiently analyze this vast amount of COVID-19 text data and find answers to high-priority questions. The document reviews common text mining techniques and algorithms and their potential applications to COVID-19 research, with the goal of simplifying searches through the large COVID-19 text data corpus.
Information disorder: Toward an interdisciplinary framework for research and ...friendscb
A comprehensive examination of information disorder including filter bubbles, echo chambers and information pollution published by the Council of Europe.
The document summarizes a presentation on challenges and future scope in fake news detection. It discusses how researchers have used various machine learning and deep learning techniques like SVM, logistic regression, neural networks to detect fake news. However, deep learning approaches have provided better results. It also notes limitations in existing approaches and need for hybrid models combining different techniques. The presentation outlines challenges like exploring unused data parameters, developing hybrid approaches, enabling real-time identification, detecting fake news across languages and tracking user credibility.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
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
IRJET- Fake News Detection and Rumour Source IdentificationIRJET Journal
This document discusses methods for detecting fake news and identifying the source of rumors on social media. It proposes using Bayesian classification to classify information into real or fake categories based on the outputs. If the combined outputs from the classes do not match, then the information is considered fake. It also discusses using a reverse dissemination strategy to identify a group of suspects for the original rumor source, rather than examining each individual. This addresses issues with identifying sources. The method aims to identify the source node based on which nodes have accepted the rumor. Machine learning and natural language processing techniques are used to detect fake news from article content.
This research proposal aims to analyze the linguistic patterns in selected controversial news articles and social media posts to determine if they contain factual information or fake news. The study will use content analysis to examine 50 sentences from 10 online articles and social media posts published from 2020 to 2023. The sentences will be classified as stating facts or opinions, and their semantic structure and linguistic patterns will be analyzed. The results intend to help improve media literacy and identify cues that can point to fake news.
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.
Development of a Web Application for Fake News Classification using Machine l...IRJET Journal
This document summarizes a research paper that proposes a machine learning model to classify news articles as real or fake. It discusses developing a web application using logistic regression for fake news classification. The paper reviews previous research on fake news detection methods and feature extraction techniques. It then outlines the proposed system's workflow, including data collection and preprocessing, model training, and classification. The system aims to predict if new articles are real or fake by comparing text to an existing dataset. If the accuracy is over 90%, the news is labeled real; otherwise it is labeled fake. The results suggest the model can accurately classify news at a 95% rate.
1. The document presents a study on automated detection of fake news from social media.
2. It proposes using account features like number of hashtags, URLs, mentions, followers etc. and a random forest classifier to detect fake news with 99.8% precision.
3. The random forest approach is compared to other classifiers like decision trees, Naive Bayes and neural networks, which achieve lower precision of 98.4%, 92.6% and 62.7% respectively.
With the spread of social media platforms and the proliferation of misleading news, misinformation
detection within microblogging platforms has become a real challenge. During the Covid-19 pandemic,
many fake news and rumors were broadcasted and shared daily on social media. In order to filter out these
fake news, many works have been done on misinformation detection using machine learning and sentiment
analysis in the English language. However, misinformation detection research in the Arabic language on
social media is limited. This paper introduces a misinformation verification system for Arabic COVID-19
related news using an Arabic rumors dataset on Twitter. We explored the dataset and prepared it using
multiple phases of preprocessing techniques before applying different machine learning classification
algorithms combined with a semantic analysis method. The model was applied on 3.6k annotated tweets
achieving 93% best overall accuracy of the model in detecting misinformation. We further build another
dataset of Covid-19 related claims in Arabic to examine how our model performs with this new set of
claims. Results show that the combination of machine learning techniques and linguistic analysis achieves
the best scores reaching 92% best accuracy in detecting the veracity of sentences of the new dataset.
COMBINING MACHINE LEARNING AND SEMANTIC ANALYSIS FOR EFFICIENT MISINFORMATION...ijcsit
With the spread of social media platforms and the proliferation of misleading news, misinformation
detection within microblogging platforms has become a real challenge. During the Covid-19 pandemic,
many fake news and rumors were broadcasted and shared daily on social media. In order to filter out these
fake news, many works have been done on misinformation detection using machine learning and sentiment
analysis in the English language. However, misinformation detection research in the Arabic language on
social media is limited. This paper introduces a misinformation verification system for Arabic COVID-19
related news using an Arabic rumors dataset on Twitter. We explored the dataset and prepared it using
multiple phases of preprocessing techniques before applying different machine learning classification
algorithms combined with a semantic analysis method. The model was applied on 3.6k annotated tweets
achieving 93% best overall accuracy of the model in detecting misinformation. We further build another
dataset of Covid-19 related claims in Arabic to examine how our model performs with this new set of
claims. Results show that the combination of machine learning techniques and linguistic analysis achieves
the best scores reaching 92% best accuracy in detecting the veracity of sentences of the new dataset.
Epidemiological Modeling of News and Rumors on TwitterParang Saraf
Abstract: Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
1) Your Research Project on the surveillance state consists of .docxcroftsshanon
1)
Your Research Project on the surveillance state consists of two parts:
1 a Powerpoint presentation consisting of at least 12 slides not including title and references.
2. 750 word research paper with at least 3 sources.
You must include at least 3 quotes from your sources enclosed in quotation marks and cited in-line.
There should be no lists - bulleted, numbered or otherwise.
Write in essay format with coherent paragraphs not in outline format.
Do your own work. Zero points will be awarded if you copy other's work and do not cite your source or you use word replacement software.
The topic must be appropriate for graduate level. Find a topic that we covered in the course and dig deeper or find something that will help you in your work or in a subject area of interest related to the course topic. Use academically appropriate resources which you can find in the
Danforth Library Research Databases.
Topic :(Identity Theft in internet)
Bernal, P. (2016). Data gathering, surveillance and human rights: recasting the debate.
Journal of Cyber Policy
,
1
(2), 243-264.
Bernal in this journal attempts to determine how surveillance affects human rights which has raised a lot of concerns of late among many workers. Looking at how surveillance has gained a lot of significance in many companies, most people have raised concerns regarding how this technology denies them their privacy. The author determines the various issues raised by gathering data from different organizations and determining the way they respond to this issue. He goes on to summarize the various issues and how they can be mitigated.
This resource is important as it will allow me develop a strong thesis statement and at the same time develop a good discussion. The resource will provide a good insight on how surveillance impacts on people’s privacy and the way they respond to the situation. Drawing from this resource I will be able to acquire a convincing study which will allow readers to clearly understand how different people think and respond to the situation.
Gangadharan, S. P. (2017). The downside of digital inclusion: Expectations and experiences of privacy and surveillance among marginal Internet users.
New Media & Society
,
19
(4), 597-615.
The author of this article draws his study as a fully-published analyst, and also as a close research concerning how the inclusion of digital technology relate to individual privacy, to determine how it impacts, explain the way it affects people’s privacy, who are affected more, implication’s on persons and how it can be controlled. The author goes on to denote significant assumptions supporting the notion that this situation has raised concern and it should be addressed.
Thus, by using this resource I will be able to develop my paper majoring on the implications of surveillance technology to workers and other individuals. The author of this article has also applied other resources which will be supp.
Detection of Fake News Using Machine LearningIRJET Journal
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International life Sciences
1.
2. 2
SUPERVISED LEARNING ESTIMATOR FOR CLASSIFYING FAKE NEWS
ARTICLES USING NATURAL LANGUAGE PROCESSING TO IDENTIFY IN-
ARTICLE
Dr.Mohammad Sanaullah Qaseem1
, Mohammed Khaja Iftequar Ali Khan
Professor1, Asst.Prof2
Department of cse
NAWAB SHAH ALAM KHAN COLLEGE OF ENGINEERING & TECHNOLOGY
NEW MALAKPET, HYDERABAD-500024
Attributes
Abstract
Intentionally false material disguised as respectable journalism is a global information accuracy and integrity concern that influences
opinion formation, decision making, and voting habits. Social media channels like Facebook and Twitter first propagate so-called 'fake
news,' which then spreads to conventional media outlets like television and radio news. Fake news articles spread through social media often
have similar language features, such as the overuse of unsupported exaggeration and the failure to properly identify referenced information,
when they first appear. Results of a fake news detection investigation are reported in this article to demonstrate the effectiveness of a fake
news classifier. There are many tools that may be utilised to construct a new kind of false news detector that utilises quoted attribution in a
Bayesian machine learning system as one of the primary features. To put it another way, it is 63.33% accurate in detecting bogus quotations
when used in articles. Influence mining is an innovative technology that may be used to identify bogus news and even propaganda, according
to the authors. The classifier performance and findings, as well as the research procedure, technical analysis, and technical linguistics, are
all discussed in this study. After discussing how the existing system would grow into an influence mining platform, the report ends.
Keywords-Components: Fake News, AI, Natural Language Processing, Attribution Classification, and Influence Analysis
INTRODUCTION
Intentionally misleading material disguised as real journalism (or "fake news") is a global issue that influences
opinion formation, decision-making, and voting habits. Most false news is first disseminated through social media
channels like Facebook and Twitter, before making its way to mainstream media outlets like television and radio
news. If you're looking for an example of how social media may be used to spread false news, look no further than
social media sites like Facebook. This article presents and discusses the findings of a fake news detection
investigation that demonstrates the effectiveness of a fake news classifier.
II. BACKGROUND AND RELATED WORK
There are several ways in which fake news may be harmful. Public perception and regional and national discussion
have been proven to be influenced by it [1–3]. Individuals have been hurt [5] and sometimes killed when they
replied to a hoax [6]. It's led to a backlash against media impartiality among some young people [7], and it's left
others unable to distinguish the difference between authentic and fabricated content [8]. Indeed, some believe it had
an impact on the 2016 U.S. presidential election [9]. Both humans and bot armies [10] may disseminate false
information, but only bot armies have the ability to reach a large number of people at once. In many situations, not
only papers are fabricated, but also photos, which are sometimes mislabeled or deceptively labelled. According to
some critics, the proliferation of false news is a "plague" on the digital infrastructure of our civilization. As a result,
a wide range of people are taking action. Peer-to-peer counter-propaganda (P2P) has been advocated by Farajtabar et
al. [13], while Haigh, Haigh, and Kozak [14] have argued for the usage of points. As previously mentioned, the
work provided here draws on a variety of previous efforts. The hallmarks of bogus news are examined in this
section. The previous attempts to identify bogus news are then examined. As a last point, we'll talk about how false
news spreads and who is to blame for it.
3. 3
Characteristics of Fake News
A variety of methods have been used to identify fake news. Fake news can be identified and debunked by fact-
checking, but this process is time-consuming and difficult to automate. Purpose libraries have been recommended by
Batchelor [15] for this task. There is a possibility that automated detection can take place at transmission speeds,
which would reduce the need for human intervention in some sections of the operation. Fake news has been found to
vary structurally and in other ways from authentic journalism. Horne and Adali [16] highlight that false and real
news vary in the length of their titles and the simplicity and repetition of the body material. There are two
approaches to analysing sarcastic cues: Rubin, et al. [17], and Volkova, & al. [18].
B. Automated Fake News Detection
Fake news poses a serious threat, hence a range of automated detection methods have been developed [19]. Among
the reasons cited by Chen, et al. [20] for the necessity for automated detection include speed and convenience,
among others. In contrast to crowdsourcing [21] and the use of human personnel for evaluation, automation may
result in near-instant conclusions and offers the necessary scalability. As an example, Riedel, et al. [22] suggested a
headline stance-based detection approach. Language analysis is used by Rashkin et al. [23], whereas "hierarchical
propagation" is proposed by Jin et al. [24], and data mining is used by Shu et al. [25]. Automated systems are also
being developed to turn this research into real-world applications. For example, Saez-Trumper [26] has created a
programme to assist detect Twitter users that spread false information. Using the "Hierarchical Propagation"
technique proposed by Jin, et al. [24], we can evaluate the believability of material. C. Credit and Modern
Communication Researchers have built systems that employ natural language procedures to recognise quotations
and their attributions. Machine learning classifiers created by Pareti, et al. [27] and O'Keefe, et al. [28] accurately
detected direct and indirect quotations. In addition, Muzny, et al. [29] created a multi-stage lexical sieve technique
for recognising quotation attributions. Many additional research have been done on hand-crafted attribution
detection systems, however the Pareti and Munzy techniques will be merged herein to produce a simple direct
quotation identification system.
III. METHODOLOGY
The methodologies used to research the fake news phenomena, develop the research database, and evolve the
qualitative model into a quantitative model are reviewed in this section.
Grounded Work and Theory Development
Using a combination of qualitative and quantitative methodologies, the research team analysed false news
documents before converting the qualitative model into a quantitative system. Glaser and Strauss' Grounded Theory
[30] methodologies for theory development and coding were used for the first false news observations and
handcrafted pattern analysis. Using pre-existing data, a social science research method known as "Grounded
Theory" constructs ideas and frameworks. A Grounded Theory study begins by monitoring the data and searching
for patterns, trends, and differences in order to build an understanding of a phenomenon under investigation.. Codes
and themes are used to organise the findings of the study. After some time, codes and motifs begin to develop their
own distinct categories. This is an example of how the researchers watching this pattern might gather enough data to
create a hypothesis that all false news documents begin with the line "believe me, I'm not lying to you" if it were
observed. In the end, the new theory would be tested. It was decided to employ Grounded Theory in order to help
create a theory inductively based on the existing evidence. Qualitative research revealed language tendencies that
were specific to the fake news pieces examined. In order to build a machine learning grammar and hypothesis, the
language patterns were utilised. The Corpus for Detection of Fake News Using a locally produced dataset, a new
fake news detection corpus was created for the investigation of false news technical language patterns. Over a
seven-month period, the research team built and tested the version of the corpus utilised in this study. There were
218 documents in the corpus when it was utilised for this study, drawn from over 40 distinct internet sources. Fake
and actual news stories are mixed in, and assertions, beliefs, and facts are all quoted. A group of ten researchers
worked together to compile the data that now makes up the corpus. Researchers on the study team assessed and
4. 4
evaluated each document's correctness on a weekly basis to determine whether or not it should be included to the
corpus. To put it simply, each document that was added to the corpus had to be evaluated and approved by
numerous researchers before it could be used. There were 421 quotes from media documents categorised as either
true or fraudulent at the time of this studycompletion. .'s Even though it wasn't created with quote attribution
machine learning research in mind, the corpus contains all text inside a document, including quotes, regardless of its
intended purpose. The corpus is separated into header and body sections for each text. We're currently working on a
more substantial corpus that can be released openly.
C. Machine Learning Grammar Development
As a result of the Grounded Theory research technique, inductive and iterative machine learning grammars were
developed. The grammars that developed were used as a starting point for developing hypotheses and conducting
experiments.
IV. EMERGED TOOL DESIGN
According to preliminary work done using Grounded Theory and the corpus, numerous significant language patterns
in the fake news pieces were found and utilised to construct a classifier model with supporting grammars by the
study team members. These grammars served as the basis for the custom classifier's feature extraction.
Attribution and Key Fake News
Features All types of media, but especially social media, are rife with fake news documents. Research began with an
examination of 30 papers containing fake information and 30 documents containing actual information in order for
participants and researchers to have a better grasp of the phenomenon and to begin formulating hypotheses about it.
There were 28 of the 30 analysed articles that featured quotations that either lacked appropriate attribution or
ascribed quotations to non-named organisations to state a fact in 28 of the 60 papers examined. Even while trends in
false content continue to be found, the most prevalent first false content signal was the absence of appropriate The
custom feature extractor is built using the following definition of an attribution to a quote: Any content span of
random length len( (for a quotation that has to be attributed) might be used. When double quotes are used to denote
the start or end of a content span, the attribution span measures the character space "d" away from those points. So,
for any quotation that has been correctly credited, please use the following format: attribution. Within fewer than 50
character spaces of the beginning or finish of the direct quotation, attribution was found in the papers that were
analysed and categorised as authentic news documents. A bespoke classifier based only on attribution was created
with these patterns and findings in mind. Measurement of the attribution inside a document (detailed in section VI)
is used to determine whether or not that document is legitimate or fraudulent. An Attribute Feature Extraction
Classifier that is customised There were several researchers who contributed to the attribution classifier and the one-
feature false news detecting system, which was built on their contributions. Pareti and O'Keefe's initial definitions
and constructions were expanded upon, as well as the definitions and concepts originally proposed by Pareti and his
colleagues. As established by the two previous study groups, attribution is a norm in which a verb or attribution cue
ties a source to a quoted passage of text known as content. There are three distinct spans of time that may be used to
attribute a quote: a source, cue, and content.
TABLE I. PARETI, ET AL. [27] ATTRIBUTION MODEL DEFINITIONS
The attribution construct components of Muzny, et al's [29] work were also used to design the custom attribution
machine learning classifier. The attribution constructs developed by Muzny, et al. using quote mention, mention
5. 5
quote, and mention entity linking to expand Pareti, et alcriteria .'s resulted in a straightforward classifier for
technical attribution, as shown in Figure 1.
The custom feature extractor is built using the following definition of an attribution to a quote: Any content span of
random length len( (for a quotation that has to be attributed) might be used. When double quotes are used to denote
the start or end of a content span, the attribution span measures the character space "d" away from those points. As a
result, when a quotation is correctly attributed:
The forward and trail attribution spans are searchable sub-spans of the attribution span. The classifier tool was
designed to search inside the forward and tail attribution space and to categorise the quotation as either credited or
unattributed, respectively. Labels are assigned according on whether or not the attribution spans include learnt
source and cue information. The custom classifier used named-entity recognition algorithms to identify a source for
a quotation. In order to correctly identify a cue, you must first acquire the related cueing verbs or cue information
from the training set. A "bag of words" paradigm for live attribution will include the majority of useful cue words
and phrases. Machine learning methods are used to extract attribution features from the forward and backward
attribution spans.
C. The Resultant Fake News Detection
Tool and Pseudocode The findings of the attribution classifications are used to apply a final label to the whole
document by the fake news detection programme. The attribution score was constructed using a basic scoring
approach, which is discussed in the following sub-section.
D. Fake News Detection Algorithm
The following is the algorithm used to identify false news stories. The number of paragraphs in each document in
the collection is tallied and tokenized. The quotations in each paragraph are also verified. Any quotations in a
paragraph will be handled using the custom attribution classifier (which uses the A-score algorithm). Negative
attributions are devalued by one point, whereas positive attributions are awarded two points for each instance in
question. For documents that have an A-score (the total of the positive and negative points) higher than or equal to
0, the label "actual" is applied. When a document's A-score falls below zero, it is considered to be a fake. As a
result, this algorithm's A-score threshold is a crucial area of possible setting. Based on the findings of the machine
6. 6
learning classification, the A-score method is used to identify quotations as true or false. Figure 2 shows the pseudo
code for both techniques.
V. EXPERIMENTAL DESIGN
The corpus was divided into training (60 percent of the available data), development (10 percent of the available
data), and test (30 percent of the available data) sets for testing the false news identification system. The algorithm is
trained to identify new inputs during the training phase.
an attribution field containing terms connected with actual or fraudulent news articles. It is important to remove
popular terms from pre-training data in order to avoid their influence on association scores. The corpus is not further
prepared for testing since common word data is not required for presentation to the classifier. The attribution span
might be tuned during testing, however it was chosen to execute a basic run with the attribution space at d=45 for
ease of implementation. Experimental validations included three distinct kinds of checks. Specifically, we looked at
the precision with which quotes were assigned to sources, as well as the custom quote attribution classifier's ability
to distinguish between authentic and fraudulent sources.
VI. INITIAL RESULTS AND ANALYSIS
Three experiments were undertaken to support the false news detection programme, and their findings are
summarised below.
VII. CONCLUSIONS AND FUTURE WORK
This report summarised the findings of a research that attempted to develop a crude system for detecting false news.
A full-spectrum research study that began with qualitative observations and ended with a workable quantitative
model is unusual in this area of research. Furthermore, the results given in this research show that machine learning
can effectively classify huge false news documents using just one extraction feature. Even further research and
development is underway to uncover and design other fake news grammars that can be used for both fake news and
direct quotations. Research efforts in the future will combine attribution feature extraction with other factors
discovered through research to produce tools that not only identify potential false content, but also influence based
content intended to persuade readers or target audiences to make wrong decisions or changes.
ACKNOWLEDGMENTS
Thank you to Alex Thielen, Zak Merrigan, Brian Kalvoda, Riley Abrahamson, DibyanshuTibrewell, William Fleck,
Ben Bernard, Brandon Stoick, and Bonnie Jan who gathered and categorised the news stories in the database utilised
for this project. In addition, NDSU researchers who have contributed to studies on false news identification,
cybersecurity, and information warfare are thanked. This study has undoubtedly benefitted from the contributions of
these projects.
7. 7
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