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Optimal Weighted Extreme Learning Machine for Cybersecurity fake news Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake news.The recently developed machine learning(ML)models can be employed for the detection and classification of fake news.This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity fake news Detection and Classification.The goal of the CAS-WELM technique is to discriminate news into fake and real.The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding process.Then,N-gram based feature extraction technique is derived to gen-erate feature vectors.Lastly,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm.The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions.The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

Semiotics and political discourse in the post-truth era

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Language and Semiotic Studies 2024年第1期10卷 65-82页

作者： BetülÇanakpınar Murat Kalelioğlu V.Doğan Günay Istinye University IstanbulTürkiye School of Foreign Languages T C Mardin Artuklu UniversityMardinTürkiye Faculty of Literature Department of LinguisticsDokuz Eylül UniversityIzmirTürkiye

The concept of post-truth has been on the world’s agenda since 2016.Perhaps this concept,which is frequently encountered in political life,provides the speaker with a freer speaking platform.Politicians,instead of presenting the reality to their constituents as it is,want to reshape this reality according to their own ideology and present it to the recipient.Politicians have certain objectives in creating such a virtual reality.These aims can be listed as keeping the voters,increasing the voting potential,and attracting undecided voters to their favor.The common result of all these aims is to gain political power or to remain in the existing power.Post-truth reality is constructed relatively more easily in social media environments.The construction of reality can be through beliefs or emotions.In such ways,the recipient is tried to be persuaded,and thus behavioral change is achieved.One of the most necessary subjects for this artificial reality atmosphere is the anti-subject.The idea that the anti-subject does not know the truth that we(you and I)know and believe and is trying to destroy it is the main theme of post-truth discourse.Politicians try to persuade the voters to believe in their own truth by expressing this idea to them.There are many examples of this around the world.In this study,firstly,the concept of post-truth is examined and its various definitions are discussed.The distinctions between the concepts of real and truth are analyzed and then it is explained how and for what purposes the concept of post-truth is used in politics.Following the comparison of post-truth and fake news,the concept is discussed within the framework of sender-receiver interaction in terms of communication.The concept is tried to be elucidated with the modalities such as/convincing/,/believing/,/being/,/appearing/.Besides,using the semiotic square,the study also tries to reveal the different purposes and forms of use of the concept of post-truth.As a result,it has been observed that the concept of post-truth is effective in

关键词： semiotics discourse politics post-truth fake news

fake news Detection on Social Media: A Temporal-Based Approach

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Computers, Materials & Continua 2021年第12期69卷 3563-3579页

作者： Yonghun Jang Chang-Hyeon Park Dong-Gun Lee Yeong-Seok Seo Department of Computer Engineering Yeungnam UniversityGyeongsan38541Korea

Following the development of communication techniques and smart devices,the era of Artificial Intelligence(AI)and big data has arrived.The increased connectivity,referred to as hyper-connectivity,has led to the development of smart cities.People in these smart cities can access numerous online contents and are always connected.These developments,however,also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels.Information cannot often be verified,which can confuse the users.The increasing influence of social media has thus led to the emergence and increasing prevalence of fake news.In this study,we propose a methodology to classify and identify fake news emanating from social channels.We collected content from Twitter to detect fake news and statistically verified that the temporal propagation pattern of quote retweets is effective for the classification of fake news.To verify this,we trained the temporal propagation pattern to a two-phases deep learning model based on convolutional neural networks and long short-term memory.The fake news classifier demonstrates the ability for its early detection.Moreover,it was verified that the temporal propagation pattern was the most influential feature compared to other feature groups discussed in this paper.

关键词： Artificial intelligence deep learning fake news rumor smart city data analysis

The mass,fake news,and cognition security

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Frontiers of Computer Science 2021年第3期15卷 1-13页

作者： Bin GUO Yasan DING Yueheng SUN Shuai MA Ke LI Zhiwen YU School of Computer Science Northwestern Polytechnical UniversityXi'an 710072China School of Cyber Security Tianjin UniversityTianjin 300350China School of Computer Science and Engineering Beihang UniversityBeijing 100191China

The widespread fake news in social networks is posing threats to social stability,economic development,and political democracy,etc.Numerous studies have explored the effective detection approaches of online fake news,while few works study the intrinsic propagation and cognition mechanisms of fake news.Since the development of cognitive science paves a promising way for the prevention of fake news,we present a new research area called Cognition Security(CogSec),which studies the potential impacts of fake news on human cognition,ranging from misperception,untrusted knowledge acquisition,targeted opinion/attitude formation,to biased decision making,and investigates the effective ways for fake news debunking.CogSec is a multidisciplinary research field that leverages the knowledge from social science,psychology,cognition science,neuroscience,AI and computer science.We first propose related definitions to characterize CogSec and review the literature history.We further investigate the key research challenges and techniques of CogSec,including human-content cognition mechanism,social influence and opinion dif-fusion,fake news detection,and malicious bot detection.Finally,we summarize the open issues and future research directions,such as the cognition mechanism of fake news,influence maximization of fact-checking information,early detection of fake news,fast refutation of fake news,and so on.

关键词： cyberspace cognition security fake news crowd computing human-content interaction

Performance Evaluation of Multiple Classifiers for Predicting fake news

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Journal of Computer and Communications 2022年第9期10卷 1-21页

作者： Arzina Tasnim Md. Saiduzzaman Mohammad Arafat Rahman Jesmin Akhter Abu Sayed Md. Mostafizur Rahaman Department of Information Systems Security Bangladesh University of Professionals Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.

关键词： fake news Machine Learning TF-IDF Classifier Estimator F1 Score Recall Precision Voting Classifiers Stacking Classifier Soft Voting Hard Voting

Analysing the behavioural finance impact of ‘fake news’phenomena on financial markets:a representative agent model and empirical validation

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Financial Innovation 2021年第1期7卷 1169-1198页

作者： Bryan Fong Faculty of Economics Churchill CollegeUniversity of CambridgeStorey’s WayCambridge CB30DSUK

This paper proposes an original behavioural finance representative agent model,to explain how fake news’empirical price impacts can persist in finance despite contradicting the efficient-market hypothesis.The model reconciles empirically-observed price overreactions to fake news with empirically-observed price underreactions to real news,and predicts a novel secondary impact of fake news:that fake news in a security amplifies underreactions to subsequent real news for the security.Evaluating the model against a large-sample event study of the 2019 Chinese ADR Delisting Threat fake news and debunking event,this paper finds strong qualitative validation for its model’s dynamics and predictions.

关键词： Behavioural finance fake news Representative agent model Event study Bootstrapping

Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

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Computers, Materials & Continua 2023年第5期75卷 2921-2938页

作者： Muhammad Asfand-e-Yar Qadeer Hashir Syed Hassan Tanvir Wajeeha Khalil Department of Computer Science Center of Excellence in Artificial Intelligence(CoE-AI)Bahria UniversityIslamabad44000Pakistan Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawar25000Pakistan

The growth of the internet and technology has had a significant effect on social interactions.False information has become an important research topic due to the massive amount of misinformed content on social networks.It is very easy for any user to spread misinformation through the media.Therefore,misinformation is a problem for professionals,organizers,and societies.Hence,it is essential to observe the credibility and validity of the news articles being shared on social media.The core challenge is to distinguish the difference between accurate and false information.Recent studies focus on news article content,such as news titles and descriptions,which has limited their achievements.However,there are two ordinarily agreed-upon features of misinformation:first,the title and text of an article,and second,the user engagement.In the case of the news context,we extracted different user engagements with articles,for example,tweets,i.e.,read-only,user retweets,likes,and shares.We calculate user credibility and combine it with article content with the user’s context.After combining both features,we used three Natural language processing(NLP)feature extraction techniques,i.e.,Term Frequency-Inverse Document Frequency(TF-IDF),Count-Vectorizer(CV),and Hashing-Vectorizer(HV).Then,we applied different machine learning classifiers to classify misinformation as real or fake.Therefore,we used a Support Vector Machine(SVM),Naive Byes(NB),Random Forest(RF),Decision Tree(DT),Gradient Boosting(GB),and K-Nearest Neighbors(KNN).The proposed method has been tested on a real-world dataset,i.e.,“fakenewsnet”.We refine the fakenewsnet dataset repository according to our required features.The dataset contains 23000+articles with millions of user engagements.The highest accuracy score is 93.4%.The proposed model achieves its highest accuracy using count vector features and a random forest classifier.Our discoveries confirmed that the proposed classifier would effectively classify misinformation in social networks.

关键词： Misinformation user credibility fake news machine learning

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

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Journal of Social Computing 2022年第4期3卷 322-344页

作者： Jaber Valinejad Zhen Guo Jin-Hee Cho Ing-Ray Chen the Data and System Science Lab Harvard Medical SchoolHarvard UniversityCambridgeMA 01451USA. Department of Computer Science Virginia TechNational Capital Region CampusFalls ChurchVA 22043USA.

The COVID-19 pandemic has severely harmed every aspect of our daily lives,resulting in a slew of social problems.Therefore,it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery.To this end,various types of social sensing tools,such as tweeting and publicly released news,have been employed to understand individuals’and communities’thoughts,behaviors,and attitudes during the COVID-19 pandemic.However,some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19.This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience.We use fact-checking organizations to classify news as real,mixed,or fake,and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience(CR).Based on the news articles and tweets collected,we quantify CR based on two key factors,community wellbeing and resource distribution,where resource distribution is assessed by the level of economic resilience and community capital.Based on the estimates of these two factors,we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets.To improve the operationalization and sociological significance of this work,we use dimension reduction techniques to integrate the dimensions.

关键词： community resilience social computing data science fake news social media urban computing computational social science machine learning

Predicting abnormal trading behavior from internet rumor propagation:a machine learning approach

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Financial Innovation 2023年第1期9卷 56-78页

作者： Li‑Chen Cheng Wei‑Ting Lu Benjamin Yeo Department of Information and Finance Management National Taipei University of TechnologyTaipei 100Taiwan Albers School of Business and Economics Seattle University90112th AveSeattleWA 98122USA

In 2021,the abnormal short-term price fluctuations of GameStop,which were triggered by internet stock discussions,drew the attention of academics,financial analysts,and stock trading commissions alike,prompting calls to address such events and maintain market stability.However,the impact of stock discussions on volatile trading behavior has received comparatively less attention than traditional fundamentals.Furthermore,data mining methods are less often used to predict stock trading despite their higher accuracy.This study adopts an innovative approach using social media data to obtain stock rumors,and then trains three decision trees to demonstrate the impact of rumor propagation on stock trading behavior.Our findings show that rumor propagation outperforms traditional fundamentals in predicting abnormal trading behavior.The study serves as an impetus for further research using data mining as a method of inquiry.

关键词： fake news Rumors Data mining Social media Classification Machine learning GameStop Reddit