scholarly journals Fake News Detection on Social Media

2020 ◽  
pp. 1132-1137
Author(s):  
Dipti Chaudhari ◽  
Krina Rana ◽  
Radhika Tannu ◽  
Snehal Yadav
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Smart Phone ◽  
Online News ◽  
Support Vector ◽  
Fake News ◽  
News Sources ◽  
Application Architecture ◽  
News Websites ◽  
Facebook Pages

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and the articles which are circulated among the social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs and social networking sites. It can be considered that social media has replaced the traditional media and become one of the main platforms for spreading news. News on social media trends to travel faster and easier than traditional news sources due to the internet accessibility and convenience. It is harmful for the society to believe on the rumors and pretend to be a news. The basic need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and methodology for fake news detection. With the help of Machine Learning, we tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. Even we have presented the mechanism to identify the significant Tweet's attribute and application architecture to systematically automate the classification of the online news.

scholarly journals Fake News Detection on Social Media Using A Natural Language Inference Approach

2020 ◽  
Author(s):  
Amir Bidgoly ◽  
Hossein Amirkhani ◽  
Fariba Sadeghi
Keyword(s):  
Social Media ◽  
Natural Language ◽  
News Item ◽  
Support Vector ◽  
Statistical Features ◽  
Fake News ◽  
K Nearest Neighbors ◽  
News Sources ◽  
Online Social Media ◽  
Embedding Methods

Abstract Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method based on Natural Language Inference (NLI) approach. Instead of using only statistical features of the content or context of the news, the proposed method exploits a human-like approach, which is based on inferring veracity using a set of reliable news. In this method, the related and similar news published in reputable news sources are used as auxiliary knowledge to infer the veracity of a given news item. We also collect and publish the first inference-based fake news detection dataset, called FNID, in two formats: the two-class version (FNID-FakeNewsNet) and the six-class version (FNID-LIAR). We use the NLI approach to boost several classical and deep machine learning models including Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, k-Nearest Neighbors, Support Vector Machine, BiGRU, and BiLSTM along with different word embedding methods including Word2vec, GloVe, fastText, and BERT. The experiments show that the proposed method achieves 85.58% and 41.31% accuracies in the FNID-FakeNewsNet and FNID-LIAR datasets, respectively, which are 10.44% and 13.19% respective absolute improvements.

Fake It “Till You Make It”: Debunking Fake News in a Post-Truth America

2019 ◽  
Vol 8 (1) ◽  
pp. 114-133
Keyword(s):  
Social Media ◽  
First Amendment ◽  
Presidential Election ◽  
Political Discourse ◽  
Cutting Edge ◽  
American People ◽  
Fake News ◽  
News Sources ◽  
The Media ◽  
The U.S

Since the 2016 U.S. presidential election, attacks on the media have been relentless. “Fake news” has become a household term, and repeated attempts to break the trust between reporters and the American people have threatened the validity of the First Amendment to the U.S. Constitution. In this article, the authors trace the development of fake news and its impact on contemporary political discourse. They also outline cutting-edge pedagogies designed to assist students in critically evaluating the veracity of various news sources and social media sites.

Avenues to News and Diverse News Exposure Online: Comparing Direct Navigation, Social Media, News Aggregators, Search Queries, and Article Hyperlinks

2021 ◽  
pp. 194016122110091
Author(s):  
Magdalena Wojcieszak ◽  
Ericka Menchen-Trevino ◽  
Joao F. F. Goncalves ◽  
Brian Weeks
Keyword(s):  
Social Media ◽  
Search Engines ◽  
Direct Access ◽  
Data Set ◽  
Individual Level ◽  
News Sources ◽  
Internet Users ◽  
News Websites ◽  
The Us ◽  
Social Media Platforms

The online environment dramatically expands the number of ways people can encounter news but there remain questions of whether these abundant opportunities facilitate news exposure diversity. This project examines key questions regarding how internet users arrive at news and what kinds of news they encounter. We account for a multiplicity of avenues to news online, some of which have never been analyzed: (1) direct access to news websites, (2) social networks, (3) news aggregators, (4) search engines, (5) webmail, and (6) hyperlinks in news. We examine the extent to which each avenue promotes news exposure and also exposes users to news sources that are left leaning, right leaning, and centrist. When combined with information on individual political leanings, we show the extent of dissimilar, centrist, or congenial exposure resulting from each avenue. We rely on web browsing history records from 636 social media users in the US paired with survey self-reports, a unique data set that allows us to examine both aggregate and individual-level exposure. Visits to news websites account for about 2 percent of the total number of visits to URLs and are unevenly distributed among users. The most widespread ways of accessing news are search engines and social media platforms (and hyperlinks within news sites once people arrive at news). The two former avenues also increase dissimilar news exposure, compared to accessing news directly, yet direct news access drives the highest proportion of centrist exposure.

scholarly journals Detection of FAKE NEWS on SOCIAL MEDIA using CLASSIFICATION Data Mining Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3132-3138
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Information Exchange ◽  
Learning Algorithm ◽  
Daily Life ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Fake News ◽  
Other Information

In today’s world social media is one of the most important tool for communication that helps people to interact with each other and share their thoughts, knowledge or any other information. Some of the most popular social media websites are Facebook, Twitter, Whatsapp and Wechat etc. Since, it has a large impact on people’s daily life it can be used a source for any fake or misinformation. So it is important that any information presented on social media should be evaluated for its genuineness and originality in terms of the probability of correctness and reliability to trust the information exchange. In this work we have identified the features that can be helpful in predicting whether a given Tweet is Rumor or Information. Two machine learning algorithm are executed using WEKA tool for the classification that is Decision Tree and Support Vector Machine.

scholarly journals Impact of Social Media Visuals on People’s Visual Communication during Social Movements in Bangladesh

2019 ◽  
pp. 9-18
Author(s):  
MD Saiful Alam Chowdhury ◽  
Monira Begum ◽  
Shaolin Shaon
Keyword(s):  
Social Media ◽  
Social Movements ◽  
Social Networking ◽  
Social Networking Sites ◽  
Visual Communication ◽  
Online News ◽  
Substantial Role ◽  
The Past ◽  
Popular View ◽  
The Impact

The past decade has seen an armorial growth of the influence of social media on many aspects of people’s lives. Social networking sites, especially Facebook, play a substantial role in framing popular view through its contents. This article explores the impact of visuals, especially photos and videos, published in social media during social movements. Importantly that some visuals received attention in social media during agitations which later got featured or become news in print, electronic and online news portal media as well. Some of the visuals later proved to be edited or fabricated contents which created confusion among participants in this research and beyond. The confusion has contributed to the acceleration or shrinkage of the movement in question in many cases. The center of this article is to examine how social media visuals influence people’s visual communication during social movements. Additionally, it digs out the user’s activity on social media during movements.

Fake News Detection Using Deep Learning

2021 ◽  
pp. 440-451
Author(s):  
Varalakshmi Konagala ◽  
Shahana Bano
Keyword(s):  
Deep Learning ◽  
Uncertain Data ◽  
Online News ◽  
Sufficient Information ◽  
Fake News ◽  
Web Based ◽  
News Sources ◽  
N Gram

The engendering of uncertain data in ordinary access news sources, for example, news sites, web-based life channels, and online papers, have made it trying to recognize capable news sources, along these lines expanding the requirement for computational instruments ready to give into the unwavering quality of online substance. For instance, counterfeit news outlets were observed to be bound to utilize language that is abstract and enthusiastic. At the point when specialists are chipping away at building up an AI-based apparatus for identifying counterfeit news, there wasn't sufficient information to prepare their calculations; they did the main balanced thing. In this chapter, two novel datasets for the undertaking of phony news locations, covering distinctive news areas, distinguishing proof of phony substance in online news has been considered. N-gram model will distinguish phony substance consequently with an emphasis on phony audits and phony news. This was pursued by a lot of learning analyses to fabricate precise phony news identifiers and showed correctness of up to 80%.

scholarly journals Trends in the diffusion of misinformation on social media

2019 ◽  
Vol 6 (2) ◽  
pp. 205316801984855 ◽  
Author(s):  
Hunt Allcott ◽  
Matthew Gentzkow ◽  
Chuan Yu
Keyword(s):  
Social Media ◽  
Relative Magnitude ◽  
Democratic Institutions ◽  
Fake News ◽  
User Interactions ◽  
News Stories ◽  
News Websites ◽  
Social Media Platforms

In recent years, there has been widespread concern that misinformation on social media is damaging societies and democratic institutions. In response, social media platforms have announced actions to limit the spread of false content. We measure trends in the diffusion of content from 569 fake news websites and 9540 fake news stories on Facebook and Twitter between January 2015 and July 2018. User interactions with false content rose steadily on both Facebook and Twitter through the end of 2016. Since then, however, interactions with false content have fallen sharply on Facebook while continuing to rise on Twitter, with the ratio of Facebook engagements to Twitter shares decreasing by 60%. In comparison, interactions with other news, business, or culture sites have followed similar trends on both platforms. Our results suggest that the relative magnitude of the misinformation problem on Facebook has declined since its peak.

Post popularity and reactions in retail brand pages on Facebook

2019 ◽  
Vol 47 (9) ◽  
pp. 957-973 ◽  
Author(s):  
Ioannis Antoniadis ◽  
Symeon Paltsoglou ◽  
Vasilis Patoulidis
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Marketing Communication ◽  
Interactive Marketing ◽  
Content Type ◽  
Message Length ◽  
Length Increase ◽  
Facebook Pages ◽  
Positive Effect ◽  
Practical Implications

Purpose Social networking sites and Facebook have grown to become an important channel of interactive marketing communication with consumers for retail. The purpose of this paper is to examine the ways posts characteristics and reactions affect post popularity and engagement in retail brands Facebook pages. Design/methodology/approach In total, 18 retail brand pages out of the 120 most popular brand pages on Facebook in Greece are examined for a three months’ period (April–June 2016). In all, 2,627 posts are analyzed with the use of OLS regressions in order to identify the characteristics of posts that increase consumers’ engagement, including the newly introduced reaction feature. Findings The results suggest that richness of content (images and videos) and message length increase the engagement levels and the popularity of posts. Reactions have a positive effect on engagement, and negative reactions stronger than positive reactions, except in sharing. On the other hand, posting time does not seem to have a statistically significant impact on the engagement and popularity of a post. Research limitations/implications The study was conducted during a period that reactions were only recently introduced by Facebook, therefore users and brands may not have been familiarized with their use. Practical implications The study contributes to the understanding of consumer engagement with retail brands’ pages on Facebook and social media, and the ways they use reactions and other ways of interactions with brand posts. The results can provide some insight to retailers on how to achieve higher levels of engagement for their brands through their Facebook pages, improving the effectiveness of social media marketing campaigns. Originality/value The findings contribute in understanding the ways users interact with brand posts in Facebook using reactions, using a number of popularity measures, providing useful insights about reactions, engagement and e-WoM, extending prior research.

scholarly journals Are people incidentally exposed to news on social media? A comparative analysis

2017 ◽  
Vol 20 (7) ◽  
pp. 2450-2468 ◽  
Author(s):  
Richard Fletcher ◽  
Rasmus Kleis Nielsen
Keyword(s):  
United States ◽  
Social Media ◽  
United Kingdom ◽  
Comparative Analysis ◽  
Survey Data ◽  
Online Survey ◽  
Online News ◽  
News Sources ◽  
Incidental Exposure ◽  
Media Environments

Scholars have questioned the potential for incidental exposure in high-choice media environments. We use online survey data to examine incidental exposure to news on social media (Facebook, YouTube, Twitter) in four countries (Italy, Australia, United Kingdom, United States). Leaving aside those who say they intentionally use social media for news, we compare the number of online news sources used by social media users who do not see it as a news platform, but may come across news while using it (the incidentally exposed), with people who do not use social media at all (non-users). We find that (a) the incidentally exposed users use significantly more online news sources than non-users, (b) the effect of incidental exposure is stronger for younger people and those with low interest in news and (c) stronger for users of YouTube and Twitter than for users of Facebook.

scholarly journals Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

10.2196/17650 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17650
Author(s):  
Genghao Li ◽  
Bing Li ◽  
Langlin Huang ◽  
Sibing Hou
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Label Propagation ◽  
World Health ◽  
Support Vector ◽  
Depression Detection ◽  
Text Information ◽  
The Rich ◽  
Health Organization ◽  
Future Work

Background According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage. Objective The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon. Methods We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance. Results The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection. Conclusions Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.

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