Using NLP for Fact Checking: A Survey

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
The Internet ◽  
Fake News ◽  
Fact Checking ◽  
The Many ◽  
Human Powered ◽  
The Web

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.

Detecting Fake News Using Deep Learning and NLP

2021 ◽  
pp. 117-133
Author(s):  
Uma Maheswari Sadasivam ◽  
Nitin Ganesan
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Reliable Information ◽  
Citizen Journalism ◽  
Fake News ◽  
Social Unrest ◽  
Safe Place ◽  
The Social

Fake news is the word making more talk these days be it election, COVID 19 pandemic, or any social unrest. Many social websites have started to fact check the news or articles posted on their websites. The reason being these fake news creates confusion, chaos, misleading the community and society. In this cyber era, citizen journalism is happening more where citizens do the collection, reporting, dissemination, and analyse news or information. This means anyone can publish news on the social websites and lead to unreliable information from the readers' points of view as well. In order to make every nation or country safe place to live by holding a fair and square election, to stop spreading hatred on race, religion, caste, creed, also to have reliable information about COVID 19, and finally from any social unrest, we need to keep a tab on fake news. This chapter presents a way to detect fake news using deep learning technique and natural language processing.

Detecting Linguistic Markers of Violent Extremism in Online Environments

2016 ◽  
pp. 374-390 ◽  
Author(s):  
Fredrik Johansson ◽  
Lisa Kaati ◽  
Magnus Sahlgren
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Terrorist Attacks ◽  
The Internet ◽  
Written Text ◽  
Linguistic Markers ◽  
Geographical Regions ◽  
Violent Extremism ◽  
Online Environments

The ability to disseminate information instantaneously over vast geographical regions makes the Internet a key facilitator in the radicalisation process and preparations for terrorist attacks. This can be both an asset and a challenge for security agencies. One of the main challenges for security agencies is the sheer amount of information available on the Internet. It is impossible for human analysts to read through everything that is written online. In this chapter we will discuss the possibility of detecting violent extremism by identifying signs of warning behaviours in written text – what we call linguistic markers – using computers, or more specifically, natural language processing.

scholarly journals Fake News Detection using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 226-228
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Deep Neural Network ◽  
Negative Impact ◽  
Research Area ◽  
Fake News ◽  
The World

News is a routine in everyone's life. It helps in enhancing the knowledge on what happens around the world. Fake news is a fictional information madeup with the intension to delude and hence the knowledge acquired becomes of no use. As fake news spreads extensively it has a negative impact in the society and so fake news detection has become an emerging research area. The paper deals with a solution to fake news detection using the methods, deep learning and Natural Language Processing. The dataset is trained using deep neural network. The dataset needs to be well formatted before given to the network which is made possible using the technique of Natural Language Processing and thus predicts whether a news is fake or not.

scholarly journals Natural Language to SQL query Generation

2021 ◽  
Vol 9 (VI) ◽  
pp. 5069-5072
Author(s):  
Kiran Raj R
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Regular Expression ◽  
Parts Of Speech ◽  
Query Generation ◽  
Sql Query ◽  
Speech Tagging ◽  
The Web

Today, everyone has a personal device to access the web. Every user tries to access the knowledge that they require through internet. Most of the knowledge is within the sort of a database. A user with limited knowledge of database will have difficulty in accessing the data in the database. Hence, there’s a requirement for a system that permits the users to access the knowledge within the database. The proposed method is to develop a system where the input be a natural language and receive an SQL query which is used to access the database and retrieve the information with ease. Tokenization, parts-of-speech tagging, lemmatization, parsing and mapping are the steps involved in the process. The project proposed would give a view of using of Natural Language Processing (NLP) and mapping the query in accordance with regular expression in English language to SQL.

scholarly journals Using of Natural Language Processing Techniques in Suicide Research

2017 ◽  
Vol 1 (2) ◽  
pp. 89 ◽  
Author(s):  
Azam Orooji ◽  
Mostafa Langarizadeh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Information ◽  
Inclusion Criteria ◽  
Data Set ◽  
Completed Suicide ◽  
Teenagers And Young Adults ◽  
Processing Techniques ◽  
The Web

It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research.

Related Blogs’ Summarization With Natural Language Processing

2020 ◽  
Author(s):  
Niyati Baliyan ◽  
Aarti Sharma
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The Web ◽  
Content Generation

Abstract There is plethora of information present on the web, on a given topic, in different forms i.e. blogs, articles, websites, etc. However, not all of the information is useful. Perusing and going through all of the information to get the understanding of the topic is a very tiresome and time-consuming task. Most of the time we end up investing in reading content that we later understand was not of importance to us. Due to the lack of capacity of the human to grasp vast quantities of information, relevant and crisp summaries are always desirable. Therefore, in this paper, we focus on generating a new blog entry containing the summary of multiple blogs on the same topic. Different approaches of clustering, modelling, content generation and summarization are applied to reach the intended goal. This system also eliminates the repetitive content giving savings on time and quantity, thereby making learning more comfortable and effective. Overall, a significant reduction in the number of words in the new blog generated by the system is observed by using the proposed novel methodology.

Computer Science Meets Education: Natural Language Processing for Automatic Grading of Open-Ended Questions in eBooks

2020 ◽  
Vol 58 (7) ◽  
pp. 1227-1255
Author(s):  
Glenn Gordon Smith ◽  
Robert Haworth ◽  
Slavko Žitnik
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Arts ◽  
Computer Science ◽  
Language Processing ◽  
Real World ◽  
Fourth Grade ◽  
Science Students ◽  
Web Based ◽  
Science Professor

We investigated how Natural Language Processing (NLP) algorithms could automatically grade answers to open-ended inference questions in web-based eBooks. This is a component of research on making reading more motivating to children and to increasing their comprehension. We obtained and graded a set of answers to open-ended questions embedded in a fiction novel written in English. Computer science students used a subset of the graded answers to develop algorithms designed to grade new answers to the questions. The algorithms utilized the story text, existing graded answers for a given question and publicly accessible databases in grading new responses. A computer science professor used another subset of the graded answers to evaluate the students’ NLP algorithms and to select the best algorithm. The results showed that the best algorithm correctly graded approximately 85% of the real-world answers as correct, partly correct, or wrong. The best NLP algorithm was trained with questions and graded answers from a series of new text narratives in another language, Slovenian. The resulting NLP algorithm model was successfully used in fourth-grade language arts classes for providing feedback to student answers on open-ended questions in eBooks.

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