Combining Conceptual Graphs and Sentiment Analysis for Fake News Detection

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

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Fake News and Propaganda: Trump’s Democratic America and Hitler’s National Socialist (Nazi) Germany

Sustainability ◽

10.3390/su11195181 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5181

Author(s):

David E. Allen ◽

Michael McAleer

Keyword(s):

Data Mining ◽

Sentiment Analysis ◽

Power Law ◽

Common Language ◽

Fake News ◽

National Socialist ◽

Data Mining Techniques ◽

State Of The Union ◽

State Of The Economy ◽

National Mood

This paper features an analysis of President Trump’s two State of the Union addresses, which are analysed by means of various data mining techniques, including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. We also apply Zipf and Mandelbrot’s power law to assess the degree to which they differ from common language patterns. To provide a contrast and some parallel context, analyses are also undertaken of President Obama’s last State of the Union address and Hitler’s 1933 Berlin Proclamation. The structure of these four political addresses is remarkably similar. The three US Presidential speeches are more positive emotionally than is Hitler’s relatively shorter address, which is characterised by a prevalence of negative emotions. Hitler’s speech deviates the most from common speech, but all three appear to target their audiences by use of non-complex speech. However, it should be said that the economic circumstances in contemporary America and Germany in the 1930s are vastly different.

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Toxic Sentiment Identification Using R Programming

International Journal of Engineering Technology and Management Sciences ◽

10.46647/ijetms.2020.v04i05.014 ◽

2020 ◽

Vol 4 (5) ◽

pp. 76-81

Author(s):

Divya Bharathi G ◽

Jagan A ◽

Pradeep Kumar V

Keyword(s):

Sentiment Analysis ◽

Language Processing ◽

Text Messaging ◽

Business Processes ◽

Opinion Mining ◽

Instant Messaging ◽

Fake News ◽

R Programming ◽

False News ◽

Delivery Channel

Text messaging has become a universal staple. WhatsApp is regularly becoming a news delivery channel as users rely on its broadcast messages to share both local and international news. Today we are not utilizing and operating it, but it is operating us which can confirm to be very unsafe for us. Most of the fake news spread rapidly by WhatsApp. So, there is requirement to examine WhatsApp chat by user’s sentiment or opinion. WhatsApp is such an application which is used widely for transferring media, text, files as well as audio calling. WhatsApp is progressively becoming a turning point in numerous sectors like healthcare, education and business. So, there is requirement to inspect WhatsApp chat by user’s sentiment or opinion. The advent of the internet had played a huge role in expanding the usage of text messaging to instant messaging on mobile devices. WhatsApp chat sentiment analysis to increase improved insights regarding their employees and strive to stay away from unanticipated conflicts due to various redundancies and insufficiency of business processes. Sentiment analysis is most popular branches of textual analytics which with the aid of information and natural language processing observe and categorize the unorganized written data into different sentiments. It is as well as acknowledged as opinion mining. Most of the false news increase rapidly by WhatsApp. Therefore, there is call for to observe and examine WhatsApp chat to find user’s sentiment or opinion. Firstly, chat from WhatsApp is selected and exported to a system which is an easy task and can be done either by phone or WhatsApp for the computer system. Following this, the processes are fairly simple and have been explained with all the coding details needed to analyze the texts. In this project, chat of WhatsApp has been used as database by using R, sentiments and emotions are being analyzed.

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Text Categorization Based on Condensed Graph

INFORMACIONNYE TEHNOLOGII ◽

10.17587/it.27.138-146 ◽

2021 ◽

Vol 27 (3) ◽

pp. 138-146

Author(s):

A. O. Korney ◽

◽

E. N. Kryuchkova ◽

Keyword(s):

Sentiment Analysis ◽

Text Categorization ◽

False Negative ◽

Relaxation Method ◽

Fake News ◽

Lexical Diversity ◽

Aspect Extraction ◽

Relevant Today ◽

Automatic Text Analysis ◽

Automatic Text

The resonant world events of2020 led to an increase in the amount of information on the Internet, including criminal, fake news, and fake negative reviews. False negative information can spread very quickly, and methods are needed to suppress this process. The development of effective algorithms for automatic text analysis is especially relevant today. The most important subtasks include thematic catesorization, sentiment analysis, includins ABSA (aspect-based sentiment analysis). The paper proposes a combined semantic-statistical alsorithm for the aspect analysis of larse texts, based on the use of a semantic graph. The aspect extraction method contains the phases of selectins a set of sisnificant words, calculatins the weishts of the vertices of the semantic sraph by the relaxation method, filterins aspects based on the sradient method. The method proposed allows to extract domain-dependent aspect terms from trainins data. Different aspect term sets extracted from different domains have the same statistical features, and in the same time lexical diversity and structure are taken into account.

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Fake News Classification of Social Media Through Sentiment Analysis

Big Data – BigData 2020 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-59612-5_5 ◽

2020 ◽

pp. 52-67

Author(s):

Lixuan Ding ◽

Lanting Ding ◽

Richard O. Sinnott

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Fake News

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Sentiment Analysis for Fake News Detection

Electronics ◽

10.3390/electronics10111348 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1348

Author(s):

Miguel A. Alonso ◽

David Vilares ◽

Carlos Gómez-Rodríguez ◽

Jesús Vilares

Keyword(s):

Sentiment Analysis ◽

Well Being ◽

Political Polarization ◽

Text Analytics ◽

Fake News ◽

Huge Amount ◽

Design And Implementation ◽

Near Future ◽

Manual Verification ◽

Automatic Systems

In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

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