Fake News Types and Detection Models on Social Media A State-of-the-Art Survey

2020 ◽  
pp. 562-573 ◽  
Author(s):  
Botambu Collins ◽  
Dinh Tuyen Hoang ◽  
Ngoc Thanh Nguyen ◽  
Dosam Hwang
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
Fake News

scholarly journals Towards Combating Pandemic-Related Misinformation in Social Media

2021 ◽  
pp. 140-158
Author(s):  
Isa Inuwa-Dutse
Keyword(s):  
Social Media ◽  
Public Discourse ◽  
Preventive Measures ◽  
State Of The Art ◽  
Fake News ◽  
Social Distancing ◽  
The Public ◽  
Toxic Impact ◽  
Online Social Media ◽  
Social Media Platforms

Conventional preventive measures during pandemics include social distancing and lockdown. Such measures in the time of social media brought about a new set of challenges – vulnerability to the toxic impact of online misinformation is high. A case in point is COVID-19. As the virus propagates, so does the associated misinformation and fake news about it leading to an infodemic. Since the outbreak, there has been a surge of studies investigating various aspects of the pandemic. Of interest to this chapter are studies centering on datasets from online social media platforms where the bulk of the public discourse happens. The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets; (2) offering relevant areas to study using the datasets; and (3) demonstrating how relevant datasets, strategies, and state-of-the-art IT tools can be leveraged in managing the pandemic.

scholarly journals Assessing the combination of DistilBERT news representations and difusion topological features to classify fake news

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carlos Abel Córdova Sáenz ◽  
Marcelo Dias ◽  
Karin Becker
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
State Of The Art ◽  
Fake News ◽  
Topological Features ◽  
Compact Representations ◽  
Fact Checking ◽  
Textual Features ◽  
Propagation Network

Fake news (FN) have affected people’s lives in unimaginable ways. The automatic classification of FN is a vital tool to prevent their dissemination and support fact-checking. Related work has shown that FN spread faster, deeper, and more broadly than truthful news on social media. Deep learning has produced state-of-the-art solutions in this field, mainly based on textual attributes. In this paper, we propose to combine compact representations of the textual news properties generated using DistilBERT, with topological metrics extracted from their propagation network in social media. Using a dataset related to politics and distinct learning algorithms, we extensively assessed the components of the proposed solution. Regarding the textual attributes, we reached results comparable to stateof-the-art solutions using only the news title and contents, which is useful for FN early detection. We assessed the influential topological metrics, and the effect of their combination with the news textual features. We also explored the use of ensembles. Our results were very promising, revealing the potential of the features proposed and the adoption of ensembles.

scholarly journals Detecting COVID-19-Related Fake News Using Feature Extraction

2022 ◽  
Vol 9 ◽  
Author(s):  
Suleman Khan ◽  
Saqib Hakak ◽  
N. Deepa ◽  
B. Prabadevi ◽  
Kapal Dev ◽  
...  
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
Electronic Media ◽  
Random Forest Classifier ◽  
Machine Learning Algorithms ◽  
Digital Marketing ◽  
Fake News ◽  
Text Data ◽  
News Sources ◽  
Computational Overhead

Since its emergence in December 2019, there have been numerous posts and news regarding the COVID-19 pandemic in social media, traditional print, and electronic media. These sources have information from both trusted and non-trusted medical sources. Furthermore, the news from these media are spread rapidly. Spreading a piece of deceptive information may lead to anxiety, unwanted exposure to medical remedies, tricks for digital marketing, and may lead to deadly factors. Therefore, a model for detecting fake news from the news pool is essential. In this work, the dataset which is a fusion of news related to COVID-19 that has been sourced from data from several social media and news sources is used for classification. In the first step, preprocessing is performed on the dataset to remove unwanted text, then tokenization is carried out to extract the tokens from the raw text data collected from various sources. Later, feature selection is performed to avoid the computational overhead incurred in processing all the features in the dataset. The linguistic and sentiment features are extracted for further processing. Finally, several state-of-the-art machine learning algorithms are trained to classify the COVID-19-related dataset. These algorithms are then evaluated using various metrics. The results show that the random forest classifier outperforms the other classifiers with an accuracy of 88.50%.

Says Who? The Effects of Presentation Format and Source Rating on Fake News in Social Media

MIS Quarterly ◽  
2019 ◽  
Vol 43 (3) ◽  
pp. 1025-1039 ◽  
Author(s):  
Antino Kim ◽  
◽  
Alan R. Dennis ◽  
Keyword(s):  
Social Media ◽  
Presentation Format ◽  
Fake News

Identity concerns drive belief: The impact of partisan identity on the belief and dissemination of true and false news

2018 ◽  
Author(s):  
Andrea Pereira ◽  
Jay Joseph Van Bavel ◽  
Elizabeth Ann Harris
Keyword(s):  
Social Media ◽  
Political System ◽  
Political Identity ◽  
Social Goals ◽  
Fake News ◽  
Political Beliefs ◽  
System P ◽  
False News ◽  
Democrats And Republicans ◽  
The Impact

Political misinformation, often called “fake news”, represents a threat to our democracies because it impedes citizens from being appropriately informed. Evidence suggests that fake news spreads more rapidly than real news—especially when it contains political content. The present article tests three competing theoretical accounts that have been proposed to explain the rise and spread of political (fake) news: (1) the ideology hypothesis— people prefer news that bolsters their values and worldviews; (2) the confirmation bias hypothesis—people prefer news that fits their pre-existing stereotypical knowledge; and (3) the political identity hypothesis—people prefer news that allows their political in-group to fulfill certain social goals. We conducted three experiments in which American participants read news that concerned behaviors perpetrated by their political in-group or out-group and measured the extent to which they believed the news (Exp. 1, Exp. 2, Exp. 3), and were willing to share the news on social media (Exp. 2 and 3). Results revealed that Democrats and Republicans were both more likely to believe news about the value-upholding behavior of their in-group or the value-undermining behavior of their out-group, supporting a political identity hypothesis. However, although belief was positively correlated with willingness to share on social media in all conditions, we also found that Republicans were more likely to believe and want to share apolitical fake new. We discuss the implications for theoretical explanations of political beliefs and application of these concepts in in polarized political system.

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.

scholarly journals Credibility of Digital Political News in Spain: Comparison between Traditional Media and Social Media

2021 ◽  
Vol 10 (5) ◽  
pp. 170
Author(s):  
Reinald Besalú ◽  
Carles Pont-Sorribes
Keyword(s):  
Social Media ◽  
News Item ◽  
Digital Television ◽  
Fake News ◽  
Traditional Media ◽  
Political News ◽  
Digital News ◽  
Key Factor ◽  
The Mean ◽  
Digital Formats

In the context of the dissemination of fake news and the traditional media outlets’ loss of centrality, the credibility of digital news emerges as a key factor for today’s democracies. The main goal of this paper was to identify the levels of credibility that Spanish citizens assign to political news in the online environment. A national survey (n = 1669) was designed to assess how the news format affected credibility and likelihood of sharing. Four different news formats were assessed, two of them linked to traditional media (digital newspapers and digital television) and two to social media (Facebook and WhatsApp). Four experimental groups assigned a credibility score and a likelihood of sharing score to four different political news items presented in the aforementioned digital formats. The comparison between the mean credibility scores assigned to the same news item presented in different formats showed significant differences among groups, as did the likelihood of sharing the news. News items shown in a traditional media format, especially digital television, were assigned more credibility than news presented in a social media format, and participants were also more likely to share the former, revealing a more cautious attitude towards social media as a source of news.

scholarly journals Perceived truth of statements and simulated social media postings: an experimental investigation of source credibility, repeated exposure, and presentation format

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Lena Nadarevic ◽  
Rolf Reber ◽  
Anne Josephine Helmecke ◽  
Dilara Köse
Keyword(s):  
Social Media ◽  
Source Credibility ◽  
Repeated Exposure ◽  
Presentation Format ◽  
The Internet ◽  
Additive Effects ◽  
Fake News ◽  
Source Information ◽  
Internet Age

Abstract To better understand the spread of fake news in the Internet age, it is important to uncover the variables that influence the perceived truth of information. Although previous research identified several reliable predictors of truth judgments—such as source credibility, repeated information exposure, and presentation format—little is known about their simultaneous effects. In a series of four experiments, we investigated how the abovementioned factors jointly affect the perceived truth of statements (Experiments 1 and 2) and simulated social media postings (Experiments 3 and 4). Experiment 1 explored the role of source credibility (high vs. low vs. no source information) and presentation format (with vs. without a picture). In Experiments 2 and 3, we additionally manipulated repeated exposure (yes vs. no). Finally, Experiment 4 examined the role of source credibility (high vs. low) and type of repetition (congruent vs. incongruent vs. no repetition) in further detail. In sum, we found no effect of presentation format on truth judgments, but strong, additive effects of source credibility and repetition. Truth judgments were higher for information presented by credible sources than non-credible sources and information without sources. Moreover, congruent (i.e., verbatim) repetition increased perceived truth whereas semantically incongruent repetition decreased perceived truth, irrespectively of the source. Our findings show that people do not rely on a single judgment cue when evaluating a statement’s truth but take source credibility and their meta-cognitive feelings into account.

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