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.

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.

scholarly journals Applying NLP techniques to malware detection in a practical environment

2021 ◽  
Author(s):  
Mamoru Mimura ◽  
Ryo Ito
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Dynamic Analysis ◽  
Language Processing ◽  
Malware Detection ◽  
Experimental Results ◽  
The Internet ◽  
Multiple Sources ◽  
Filtering Method

AbstractExecutable files still remain popular to compromise the endpoint computers. These executable files are often obfuscated to avoid anti-virus programs. To examine all suspicious files from the Internet, dynamic analysis requires too much time. Therefore, a fast filtering method is required. With the recent development of natural language processing (NLP) techniques, printable strings became more effective to detect malware. The combination of the printable strings and NLP techniques can be used as a filtering method. In this paper, we apply NLP techniques to malware detection. This paper reveals that printable strings with NLP techniques are effective for detecting malware in a practical environment. Our dataset consists of more than 500,000 samples obtained from multiple sources. Our experimental results demonstrate that our method is effective to not only subspecies of the existing malware, but also new malware. Our method is effective against packed malware and anti-debugging techniques.

scholarly journals Sentiment Analysis for Depression Based on Social Media Post by Using Natural Language Processing

2021 ◽  
pp. 134-139
Author(s):  
G. Neelavathi ◽  
D. Sowmiya ◽  
C. Sharmila ◽  
J. Vaishnavi
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Rich Source ◽  
Research Center ◽  
Depression Symptoms ◽  
Linguistic Markers

Presently Research Center expresses that, 72% of public uses some sort of social media. More than 300 million individual experiences the depression and despondency, just a small amount of them get sufficient treatment. Discouragement is the main source of incapacity worldwide and almost 800,000 individuals consistently loss their life because of suicide. Suicide is the subsequent driving reason for death among teenagers. Our idea is to suggest solution for this problem. Social Media gives an extraordinary chance to change early depressions, especially in youngsters. Consistently, around 6,000 Tweets are tweeted per second, 350,000 tweets per minute, 500 million tweets each day and around 200 billion tweets each year. By using this rich source of data and information, can efficient model which provides report of person’s depression symptoms will be designed. In this model an algorithm that can examine Tweets Expressing self-assessed negative features by analyzing linguistic markers in social media posts.

scholarly journals Online Assignment Plagiarism Detector

2021 ◽  
pp. 528-535
Author(s):  
Nikhil Paymode ◽  
Rahul Yadav ◽  
Sudarshan Vichare ◽  
Suvarna Bhoir
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Technique ◽  
The Internet ◽  
Plagiarism Detection ◽  
Efficient Manner ◽  
Detection Approach ◽  
Different Sources ◽  
Student Program

Plagiarism is a big intricacy for companies, Schools, Colleges, and those who published their document on the web. In-Schools and Colleges maximum students write their assignments and experiments by copying other documents. Using this system teachers and examiners can detect the documents and sheets either it is written by a respective student or it is copied from someone else. For checking plagiarism the system takes two or more documents as a input and after using string matching algorithms, NLP ( natural language processing) technique, as well as an NLTK toolkit (natural language toolkit), produces output. In the output, the system returns some score which is an interval of 0 to 1. Where 1 and 0 refer to exactly similar and nothing is similar (Unique) respectively. If a score between 0 to 1 then it shows only some part of the document is similar. The main objective of the system is to find the more accurate plagiarism content in the documents with similar meanings and concepts that are correctly identified in an efficient manner. It is very easy to copy the data from different sources which includes the internet, papers, books over the internet, newspapers, etc. there is a need of detecting plagiarism to increase and improve the learning of students. To solve this problem, a student program plagiarism detection approach is proposed based on Natural Language Processing.

scholarly journals An Introduction to Natural Language Processing: the Main Problems

Triangle ◽  
2018 ◽  
pp. 65
Author(s):  
Veronica Dahl
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Language Understanding ◽  
Human Language ◽  
Language Understanding ◽  
Written Text

Natural Language Processing aims to give computers the power to automatically process human language sentences, mostly in written text form but also spoken, for various purposes. This sub-discipline of AI (Artificial Intelligence) is also known as Natural Language Understanding.

scholarly journals Detection of Email Spam using Natural Language Processing Based Random Forest Approach

2021 ◽  
Author(s):  
Alanazi Rayan ◽  
Ahmed I. Taloba
Keyword(s):  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
The Internet ◽  
Natural Languages ◽  
Efficient Detection ◽  
Random Node ◽  
A Company ◽  
Email Spam

Abstract An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those temporary email addresses for sending free spam emails without revealing the spammers real account details. These attacks create major problems like theft of user credentials, lack of storage, etc. Hence it is essential to introduce an efficient detection mechanismthrough feature extraction and classification for detecting spam emails and temporary email addresses. This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach. With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.

scholarly journals An Innovative Recommender System for E-Commerce Websites using Natural Language Processing

2020 ◽  
Vol 8 (6) ◽  
pp. 4085-4089
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
Language Processing ◽  
Recommender System ◽  
Opinion Mining ◽  
The Internet

A Recommender System has become the go-to application for the internet generation these days. Mono-variate, bi-variate and multi-variate Recommender Systems are available to consumers of various products and services for the last 10 years or so only. In this paper, opinion mining dependent sentiment analysis using NLP tools will be used to recommend products to their purchasers on e-commerce websites. The application can be developed on the Python platform can be commercially used and will be precisely used to people who have to spend money without traditionally touching or feeling the item

scholarly journals Corpora Annotated with Negation: An Overview

2020 ◽  
Vol 46 (1) ◽  
pp. 1-52
Author(s):  
Salud María Jiménez-Zafra ◽  
Roser Morante ◽  
María Teresa Martín-Valdivia ◽  
L. Alfonso Ureña-López
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The Internet ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Annotation Scheme ◽  
Linguistic Phenomenon ◽  
The Way

Negation is a universal linguistic phenomenon with a great qualitative impact on natural language processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. Currently, most corpora have been annotated for English, but the presence of languages other than English on the Internet, such as Chinese or Spanish, is greater every day. In this study, we present a review of the corpora annotated with negation information in several languages with the goal of evaluating what aspects of negation have been annotated and how compatible the corpora are. We conclude that it is very difficult to merge the existing corpora because we found differences in the annotation schemes used, and most importantly, in the annotation guidelines: the way in which each corpus was tokenized and the negation elements that have been annotated. Differently than for other well established tasks like semantic role labeling or parsing, for negation there is no standard annotation scheme nor guidelines, which hampers progress in its treatment.

scholarly journals An Efficient Framework for Detecting Various Moods in Hinglish and English Dataset

2019 ◽  
Vol 9 (2) ◽  
pp. 1943-1946
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Major Goal ◽  
Research Papers ◽  
Written Text ◽  
Web Technologies ◽  
Mixed Language ◽  
Hindi Language

Due to the paced growth in web technologies and natural language processing, research on Sentiment Analysis (SA) has become very popular in recent times. In recent years most of the research papers have focused on sentiment analysis based on polarity (positive and negative sentiments). This paper presents an effective framework for identification of various moods of person from its written text or sentences. The paper focuses on the mood detection in given text written in mixed language called “Hinglish”. Hinglish is actually a fusion of two languages, English with the Hindi language. The major goal of this research is to propose a methodology for extracting information of emotions from a given text in Hinglish. The framework tested on 700 sentences containing Hinglish data. Seven emotions anger, happy, joy, confidence, sadness, tentativeness and fear have been used for generation of results. The proposed approach yielded an accuracy of 93.96%.

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