Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

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.

scholarly journals Sentiment analysis based on fuzzy propagation in online social networks: A case study on TweetScope

2014 ◽  
Vol 11 (1) ◽  
pp. 215-228 ◽  
Author(s):  
Duc Trung ◽  
Jason Jung
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Significant Influence ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Important Task ◽  
Propagation Modeling ◽  
Practical System

Understanding customers? opinion and subjectivity is regarded as an important task in various domains (e.g., marketing). Particularly, with many types of social media (e.g., Twitter and FaceBook), such opinions are propagated to other users and might make a significant influence on them. In this paper, we propose a fuzzy propagation modeling for opinion mining by sentiment analysis of online social networks. Thereby, a practical system, called TweetScope, has been implemented to efficiently collect and analyze all possible tweets from customers.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

scholarly journals Emotion shapes the diffusion of moralized content in social networks

2017 ◽  
Vol 114 (28) ◽  
pp. 7313-7318 ◽  
Author(s):  
William J. Brady ◽  
Julian A. Wills ◽  
John T. Jost ◽  
Joshua A. Tucker ◽  
Jay J. Van Bavel
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Political Debate ◽  
Political Issues ◽  
Emotional Words ◽  
Social Media Networks ◽  
Political Ideas ◽  
The Social ◽  
Network Methods

Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call “moral contagion.” Using a large sample of social media communications about three polarizing moral/political issues (n = 563,312), we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them. Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly immersed in social media networks.

scholarly journals Oversharenting and family life

2020 ◽  
Vol 8 (11) ◽  
pp. 155-170
Author(s):  
Renata Soares Martins ◽  
Suely Aparecida do Nascimento Mascarenhas ◽  
Gisele Cristina Resende
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Interpersonal Relationships ◽  
Family Life ◽  
Online Social Networks ◽  
The Internet ◽  
Communications Technology ◽  
The Social ◽  
The Family ◽  
High Degree

This article invites us to reflect on oversharenting and family life that, owing to the proliferation of communications technology and the internet, is intersected by digital cyberculture. The research was carried out on the social network, using the method of searching by hashtag. The results showed that during 2018 in two weeks, 20,781 posts were made using the hashtag “minidiva” and 1,679 with the hashtag “miniblogger”, from which three posts were collected each day. Netnography was used to analyze the images and categorize them: (1) oversharenting and family life, (2) social media and child consumption, (3) child adultization. It was concluded that online social networks (Instagram) are spaces where interpersonal relationships; it was seen that the act of consuming gained relevance in the family and that the child’s exposure occurs without awareness, which can cause a high degree of exposure and consequently have adverse effects for everyone.

Organizational Architecture and Online Social Networks

2016 ◽  
pp. 508-524
Author(s):  
André Grützmann ◽  
Cleber Carvalho de Castro ◽  
Anderson Antonio Freire de Moraes Meireles ◽  
Renan Carlos Rodrigues
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Interorganizational Networks ◽  
The Internet ◽  
Small Enterprises ◽  
Competitive Advantages ◽  
Global Marketplace ◽  
Complementary Resources ◽  
Organizational Architecture

The global marketplace is changing, forcing companies to establish stronger partnerships to compete at higher levels. Interorganizational networks are becoming a reality—not only for small enterprises, but also for larger companies. To face increasing global competition, companies need to strengthen their competitive advantages and solicit complementary resources from partners in order to get closer to customers. The Internet and its associated technologies, especially online social networks, can help with both tasks. Customers are using social media to share content, opinions, criticisms, and even compliments with companies and products. This chapter discusses the formation of interorganizational networks and points out some of the benefits that online social networks can contribute to these new organizational architectures.

scholarly journals Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection

2020 ◽  
Vol 17 (3) ◽  
pp. 1066 ◽  
Author(s):  
Pablo Lara-Navarra ◽  
Hervé Falciani ◽  
Enrique A. Sánchez-Pérez ◽  
Antonia Ferrer-Sapena
Keyword(s):  
Social Media ◽  
Open Data ◽  
The Internet ◽  
Web Pages ◽  
Fake News ◽  
Social Media Networks ◽  
Analytical Scheme ◽  
False News ◽  
The Right ◽  
Methodological Guidelines

Comments and information appearing on the internet and on different social media sway opinion concerning potential remedies for diagnosing and curing diseases. In many cases, this has an impact on citizens’ health and affects medical professionals, who find themselves having to defend their diagnoses as well as the treatments they propose against ill-informed patients. The propagation of these opinions follows the same pattern as the dissemination of fake news about other important topics, such as the environment, via social media networks, which we use as a testing ground for checking our procedure. In this article, we present an algorithm to analyse the behaviour of users of Twitter, the most important social network with respect to this issue, as well as a dynamic knowledge graph construction method based on information gathered from Twitter and other open data sources such as web pages. To show our methodology, we present a concrete example of how the associated graph structure of the tweets related to World Environment Day 2019 is used to develop a heuristic analysis of the validity of the information. The proposed analytical scheme is based on the interaction between the computer tool—a database implemented with Neo4j—and the analyst, who must ask the right questions to the tool, allowing to follow the line of any doubtful data. We also show how this method can be used. We also present some methodological guidelines on how our system could allow, in the future, an automation of the procedures for the construction of an autonomous algorithm for the detection of false news on the internet related to health.

scholarly journals An Enhanced Sybil Guard to Detect Bots in Online Social Networks

2021 ◽  
Author(s):  
Nisha P. Shetty ◽  
Balachandra Muniyal ◽  
Arshia Anand ◽  
Sushant Kumar
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Networking ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Easy Access ◽  
Classifier Combination ◽  
Fake News ◽  
Wide Scale ◽  
Set Up

Sybil accounts are swelling in popular social networking sites such as Twitter, Facebook etc. owing to cheap subscription and easy access to large masses. A malicious person creates multiple fake identities to outreach and outgrow his network. People blindly trust their online connections and fall into trap set up by these fake perpetrators. Sybil nodes exploit OSN’s ready-made connectivity to spread fake news, spamming, influencing polls, recommendations and advertisements, masquerading to get critical information, launching phishing attacks etc. Such accounts are surging in wide scale and so it has become very vital to effectively detect such nodes. In this research a new classifier (combination of Sybil Guard, Twitter engagement rate and Profile statistics analyser) is developed to combat such Sybil nodes. The proposed classifier overcomes the limitations of structure based, machine learning based and behaviour-based classifiers and is proven to be more accurate and robust than the base Sybil guard algorithm.

Review Classification Approach for User Sentiment Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 1670-1672
Author(s):  
Mr. Pratik S. Yawale
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Hate Speech ◽  
Modern Technology ◽  
The Internet ◽  
Quality Information ◽  
Fuzzy Approach ◽  
Classification Approach ◽  
Day By Day

Sentiment analysis or opinion mining is one of the fastest growing fields with its demand and benefits that is increasing day by day. With the availability of the internet and modern technology, there has been a tremendous growth in the amount of data. The text that has been posted by people to express their sentiment on social media ,can be analysed and used in order to draw benefits and quality information. In this paper, the focus is on cyber-hate classification based on for public opinion or views, since the spread of hate speech using social media can have disruptive impacts on social sentiment analysis. In particular, here proposing a modified fuzzy approach with two stage training for dealing with text ambiguity and classifying three type approach positive, negative and neutral sentiment, and compare its performance with those popular methods as well as some existing fuzzy approaches.

scholarly journals PREPROCESSING ARABIC DIALECT FOR SENTIMENT MINING: STATE OF ART

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 323-330
Author(s):  
Z. Nassr ◽  
N. Sael ◽  
F. Benabbou
Keyword(s):  
Social Networks ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
The Internet ◽  
Sentiment Mining ◽  
Arabic Dialects ◽  
Limited List ◽  
Processing Solution

Abstract. Sentiment Analysis concerns the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has become more crucial in the field of text mining research and has since been used to explore users’ opinions on various products or topics discussed on the Internet. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentiment Analysis studies, especially for sentiments written in non-structured or semi-structured languages. In this paper, we present a literature review on the pre-processing task on the field of sentiment analysis and an analytical and comparative study of different researches conducted in Arabic social networks. This study allowed as concluding that several works have dealt with the generation of stop words dictionary. In this context, two approaches are adopted: first, the manual one, which gives rise to a limited list, and second, the automatic, where the list of stop words is extracted from social networks based on defined rules. For stemming two, algorithms have been proposed to isolate prefixes and suffixes from words in dialects. However, few works have been interested in dialects directly without translation. The Moroccan dialect in particular is considered as the 5th dialect studied among Arabic dialects after Jordanian, Egyptian, Tunisian and Algerian dialects. Despite the significant lack in studies carried out on Arabic dialects, we were able to extract several conclusions about the difficulties and challenges encountered through this comparative study, as well as the possible ways and tracks to study in any dialects sentiment analysis pre-processing solution.

scholarly journals Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences

2019 ◽  
Vol 11 (16) ◽  
pp. 4459 ◽  
Author(s):  
Vasile-Daniel Păvăloaia ◽  
Elena-Mădălina Teodor ◽  
Doina Fotache ◽  
Magdalena Danileţ
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Emotional Reactions ◽  
User Preferences ◽  
Communication Strategy ◽  
Customer Relationship ◽  
Beverage Industry ◽  
Different Types

Any brand’s presence on social networks has a significant impact on emotional reactions of its users to different types of posts on social media (SM). If a company understands the preferred types of posts (photo or video) of its customers, based on their reactions, it could make use of these preferences in designing its future communication strategy. The study examines how the use of SM technology and customer-centric management systems could contribute to sustainable business development of companies by means of social customer relationship management (sCRM). The two companies included in the study provide a general consumer good in the beverage industry. As such, it may be said that users interacting with the posts these companies make on their official channels are in fact customers or potential customers. The study aims to analyze customer reaction to two types of posts (photos or videos) on six social networks: Facebook, Twitter, Instagram, Pinterest, Google+ and Youtube. It brings evidence on the differences and similarities between the SM customer behaviors of two highly competitive brands in the beverage industry. Drawing on current literature on SM, sCRM and marketing, the output of this study is the conceptualization and measurement of a brand’s SM ability to understand customer preferences for different types of posts by using various statistical tools and the sentiment analysis (SA) technique applied to big sets of data.

Sign in / Sign up

Export Citation Format

Share Document