Modeling Real and Fake News Sharing in Social Networks

2021 ◽  
Author(s):  
Abishai Joy
Keyword(s):  
Social Media ◽  
Survival Analysis ◽  
Diffusion Of Innovation ◽  
Absolute Error ◽  
Support Vector ◽  
Fake News ◽  
High Dependency ◽  
Binary Classifiers ◽  
Characteristic Features ◽  
News Sharing

Online media is changing the traditional news industry and diminishing the role of journalists, newspapers, and even news channels. This in turn is enhancing the ability of fake news to influence public opinion on important topics. The threat of fake news is quite imminent, as it allows malicious users to share their agenda with a larger audience. Major social media platforms like Twitter, Facebook, etc., are making it easy to spread fake news due to the minimal moderation/ fact-checking on these platforms. This work aims at predicting fake and real news sharing in social media. Specifically, we employ a multi-level influence, drawn from the Diffusion of Innovation (DOI) theory on a real-world dataset and predict whether and when a given user will share information in social media. We hypothesize that fake and real news sharing is better predicted by considering user, news, and network-level feature attributes together. We are also predicting the time elapsed between the influencer and follower shares via survival analysis. Binary classifiers such as Support Vector Machine (SVM), Random Forest, etc. are used for the prediction of fake and real news sharing. This approach is demonstrated using a dataset comprising 1,572 users that are sampled from the FakeNewsNet repository. Our results show a 30% increase in the Area Under Receiver Operation Characteristics (AUROC) in comparison to the best baseline. Real and fake news sharing shows high dependency on user similarity, tie strength, and explicit features. Furthermore, the analysis shows that users with characteristic features like love, self-transcendence, ideals, conservation, and openness to change tend to share real news, whereas users with dominant features like self-enhancement, curiosity, closeness, structure, and harmony are more likely to share fake news. Finally, survival analysis is employed to predict the time elapsed between influencer and follower shares. The Concordance Index (C-Index) for real news sharing is slightly lower compared to the baseline, and the C-Index of Random Survival Forest (RSF) is comparable to the baseline for fake news sharing. Furthermore, in comparison to the regression baseline models, the Mean Absolute Error (MAE) is significantly less in RSF for both real and fake news sharing.

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 Do tabloids poison the well of social media? Explaining democratically dysfunctional news sharing

2018 ◽  
Vol 20 (11) ◽  
pp. 4255-4274 ◽  
Author(s):  
Andrew Chadwick ◽  
Cristian Vaccari ◽  
Ben O’Loughlin
Keyword(s):  
Social Media ◽  
Raw Material ◽  
Civic Culture ◽  
News Article ◽  
Fake News ◽  
Individual Level ◽  
The Status ◽  
Twitter Users ◽  
Use Of Social Media ◽  
News Sharing

The use of social media for sharing political information and the status of news as an essential raw material for good citizenship are both generating increasing public concern. We add to the debates about misinformation, disinformation, and “fake news” using a new theoretical framework and a unique research design integrating survey data and analysis of observed news sharing behaviors on social media. Using a media-as-resources perspective, we theorize that there are elective affinities between tabloid news and misinformation and disinformation behaviors on social media. Integrating four data sets we constructed during the 2017 UK election campaign—individual-level data on news sharing ( N = 1,525,748 tweets), website data ( N = 17,989 web domains), news article data ( N = 641 articles), and data from a custom survey of Twitter users ( N = 1313 respondents)—we find that sharing tabloid news on social media is a significant predictor of democratically dysfunctional misinformation and disinformation behaviors. We explain the consequences of this finding for the civic culture of social media and the direction of future scholarship on fake 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.

scholarly journals Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)

2021 ◽  
Vol 16 (6) ◽  
pp. 1-18
Author(s):  
T. V. Divya ◽  
Barnali Gupta Banik
Keyword(s):  
Social Media ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Word Embedding ◽  
Support Vector ◽  
Fake News ◽  
Short Term ◽  
Term Memory ◽  
Online Social Media ◽  
Long Short Term Memory

Fake news detection on job advertisements has grabbed the attention of many researchers over past decade. Various classifiers such as Support Vector Machine (SVM), XGBoost Classifier and Random Forest (RF) methods are greatly utilized for fake and real news detection pertaining to job advertisement posts in social media. Bi-Directional Long Short-Term Memory (Bi-LSTM) classifier is greatly utilized for learning word representations in lower-dimensional vector space and learning significant words word embedding or terms revealed through Word embedding algorithm. The fake news detection is greatly achieved along with real news on job post from online social media is achieved by Bi-LSTM classifier and thereby evaluating corresponding performance. The performance metrics such as Precision, Recall, F1-score, and Accuracy are assessed for effectiveness by fraudulency based on job posts. The outcome infers the effectiveness and prominence of features for detecting false news. .

scholarly journals Intentional or inadvertent fake news sharing? Fact-checking warnings and users’ interaction with social media content

2020 ◽  
Author(s):  
Alberto Ardèvol-Abreu ◽  
Patricia Delponti ◽  
Carmen Rodríguez-Wangüemert
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Media Content ◽  
Fake News ◽  
Negative Effects ◽  
News Stories ◽  
Social Media Platforms ◽  
Fact Checking ◽  
False News ◽  
News Sharing

The main social media platforms have been implementing strategies to minimize fake news dissemination. These include identifying, labeling, and penalizing –via news feed ranking algorithms– fake publications. Part of the rationale behind this approach is that the negative effects of fake content arise only when social media users are deceived. Once debunked, fake posts and news stories should therefore become harmless. Unfortunately, the literature shows that the effects of misinformation are more complex and tend to persist and even backfire after correction. Furthermore, we still do not know much about how social media users evaluate content that has been fact-checked and flagged as false. More worryingly, previous findings suggest that some people may intentionally share made up news on social media, although their motivations are not fully explained. To better understand users’ interaction with social media content identified or recognized as false, we analyze qualitative and quantitative data from five focus groups and a sub-national online survey (N = 350). Findings suggest that the label of ‘false news’ plays a role –although not necessarily central– in social media users’ evaluation of the content and their decision (not) to share it. Some participants showed distrust in fact-checkers and lack of knowledge about the fact-checking process. We also found that fake news sharing is a two-dimensional phenomenon that includes intentional and unintentional behaviors. We discuss some of the reasons why some of social media users may choose to distribute fake news content intentionally.

Service complaint identification in hotel social media: A two-step classification approach

2020 ◽  
pp. 002072092092846
Author(s):  
Jiahua Jin ◽  
Lu Lu
Keyword(s):  
Social Media ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Classification Algorithms ◽  
Construction Process ◽  
K Nearest Neighbor ◽  
Classification Approach ◽  
Consumer Complaint ◽  
Training Samples ◽  
Binary Classifiers

Hotel social media provides access to dissatisfied customers and their experiences with services. However, due to massive topics and posts in social media, and the sparse distribution of complaint-related posts and, manually identifying complaints is inefficient and time-consuming. In this study, we propose a supervised learning method including training samples enlargement and classifier construction. We first identified reliable complaint and noncomplaint samples from the unlabeled dataset by using small labeled samples as training samples. Combining the labeled samples and enlarged samples, classification algorithms support vector machine and k-nearest neighbor were then adopted to build binary classifiers during the classifier construction process. Experimental results indicate the proposed method can identify complaints from social media efficiently, especially when the amount of labeled training samples is small. This study provides an efficient approach for hotel companies to distinguish a certain kind of consumer complaint information from large number of unrelated information in hotel social media.

Arresting fake news sharing on social media: a theory of planned behavior approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vartika Pundir ◽  
Elangbam Binodini Devi ◽  
Vishnu Nath
Keyword(s):  
Social Media ◽  
Theory Of Planned Behavior ◽  
Conceptual Framework ◽  
Planned Behavior ◽  
Behavioral Control ◽  
Equation Modeling ◽  
Perceived Behavioral Control ◽  
Fake News ◽  
Content Type ◽  
News Sharing

Purpose This study aims to examine the collective impact of awareness and knowledge about fake news, attitudes toward news verification, perceived behavioral control, subjective norms, fear of missing out (FoMO) and sadism on social media users’ intention to verify news before sharing on social media. Design/methodology/approach The current study’s conceptual framework is developed by a comprehensive literature review on social networking and the theory of planned behavior. The data for samples were collected from 400 respondents in India to test the conceptual framework using the partial least square–structural equation modeling technique. Findings The results show that awareness and knowledge, perceived behavioral control, attitudes toward news verification and FoMO are significant predictors of intention to verify news before sharing. Research limitations/implications The present study concludes implications for managers of social media companies and policy actors that want to take steps toward arresting the spread of fake news via social media. Originality/value Academic investigation on fake news sharing on social media has recently gained traction. The current work is unique because it uses the theory of planned behavior as a basis for predicting social media user’s intention to verify news before sharing on social media.

Fake or real news? Understanding the gratifications and personality traits of individuals sharing fake news on social media platforms

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Brinda Sampat ◽  
Sahil Raj
Keyword(s):  
Social Media ◽  
Personality Traits ◽  
Information Sharing ◽  
Fake News ◽  
Content Type ◽  
Social Media Platform ◽  
The Social ◽  
Social Media Platforms ◽  
Media Platform ◽  
News Sharing

Purpose“Fake news” or misinformation sharing using social media sites into public discourse or politics has increased dramatically, over the last few years, especially in the current COVID-19 pandemic causing concern. However, this phenomenon is inadequately researched. This study examines fake news sharing with the lens of stimulus-organism-response (SOR) theory, uses and gratification theory (UGT) and big five personality traits (BFPT) theory to understand the motivations for sharing fake news and the personality traits that do so. The stimuli in the model comprise gratifications (pass time, entertainment, socialization, information sharing and information seeking) and personality traits (agreeableness, conscientiousness, extraversion, openness and neuroticism). The feeling of authenticating or instantly sharing news is the organism leading to sharing fake news, which forms the response in the study.Design/methodology/approachThe conceptual model was tested by the data collected from a sample of 221 social media users in India. The data were analyzed with partial least squares structural equation modeling to determine the effects of UGT and personality traits on fake news sharing. The moderating role of the platform WhatsApp or Facebook was studied.Findings The results suggest that pass time, information sharing and socialization gratifications lead to instant sharing news on social media platforms. Individuals who exhibit extraversion, neuroticism and openness share news on social media platforms instantly. In contrast, agreeableness and conscientiousness personality traits lead to authentication news before sharing on the social media platform.Originality/value This study contributes to social media literature by identifying the user gratifications and personality traits that lead to sharing fake news on social media platforms. Furthermore, the study also sheds light on the moderating influence of the choice of the social media platform for fake news sharing.

Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior

2019 ◽  
Vol 51 ◽  
pp. 72-82 ◽  
Author(s):  
Shalini Talwar ◽  
Amandeep Dhir ◽  
Puneet Kaur ◽  
Nida Zafar ◽  
Melfi Alrasheedy
Keyword(s):  
Social Media ◽  
Media Use ◽  
Dark Side ◽  
Fake News ◽  
Social Media Use ◽  
News Sharing ◽  
Sharing Behavior

scholarly journals FAKE NEWS PROLIFERATION IN NIGERIA: CONSEQUENCES, MOTIVATIONS, AND PREVENTION THROUGH AWARENESS STRATEGIES

2020 ◽  
Vol 8 (2) ◽  
pp. 318-327 ◽  
Author(s):  
Oberiri Destiny Apuke ◽  
Bahiyah Omar
Keyword(s):  
Social Media ◽  
Information Literacy ◽  
Preventive Measures ◽  
Youth Unemployment ◽  
Past Research ◽  
Fake News ◽  
Conflict Escalation ◽  
Media Control ◽  
News Sharing ◽  
Obligation To Inform

Purpose: This study aims to understand the effects of fake news spreading in Nigeria, the reasons for fake news sharing among social media users, and eventually propose preventive measures (i.e. awareness strategies) to combat the proliferation of fake news in Nigeria. Main results: Some grave implications of fake news sharing were identified such as death, conflict escalation, political hostility, and societal panic. Meanwhile, people were motivated to share news mainly because of their civil obligation to inform others and provide advice or warning. These motivations, together with other contextual reasons such as media control, interpersonal trust and youth unemployment, had led to fake news proliferation in Nigeria. Methodology: This study adopts a documentary research method to generate the information necessary to investigate fake news spread in Nigeria. A total of 265 articles were drawn from Google Scholar search and after a close examination, only 20 articles were included for analysis. Implications: There is a need to increase fake news awareness, media and information literacy among Nigerians. Social media users should be constantly informed through adequate advertisements, workshops, conferences, and other forms of sensitization, about the consequences of fake news sharing, how to spot and differentiate fake news with made-up news and why it is imperative to be self-aware before forwarding any message. Originality/novelty: This paper contributes to knowledge in two ways. First, it compiles past research on fake news in Nigeria and analysed contextual factors and consequences of fake news proliferation in this context. Second, it reinforces the need for fake news awareness as a means of reducing the spread of fake news among social media users in Nigeria.

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