Model for Detecting Fake News on Twitter

PurposeThe creation and dissemination of fake news can have severe consequences for a company’s brand. Researchers, policymakers and practitioners are eagerly searching for solutions to get us out of the “fake news crisis”. Here, one approach is to use automated tools, such as artificial intelligence (AI) algorithms, to support managers in identifying fake news. The study in this paper demonstrates how AI with its ability to analyze vast amounts of unstructured data, can help us tell apart fake and real news content. Using an AI application, this study examines if and how the emotional appeal, i.e., sentiment valence and strength of specific emotions, in fake news content differs from that in real news content. This is important to understand, as messages with a strong emotional appeal can influence how content is consumed, processed and shared by consumers.Design/methodology/approachThe study analyzes a data set of 150 real and fake news articles using an AI application, to test for differences in the emotional appeal in the titles and the text body between fake news and real news content.FindingsThe results suggest that titles are a strong differentiator on emotions between fake and real news and that fake news titles are substantially more negative than real news titles. In addition, the results reveal that the text body of fake news is substantially higher in displaying specific negative emotions, such as disgust and anger, and lower in displaying positive emotions, such as joy.Originality/valueThis is the first empirical study that examines the emotional appeal of fake and real news content with respect to the prevalence and strength of specific emotion dimensions, thus adding to the literature on fake news identification and marketing communications. In addition, this paper provides marketing communications professionals with a practical approach to identify fake news using AI.

Nowadays, with the advancement of technology, the use of news sources has also undergone a great evolution. News sources have constantly evolved from past to present, ranging from magazines to radios, from newspapers to televisions. The fact that it has become so easy to access news has caused society to pay more attention to fake news. Fake news has the ability to influence society through news sources such as social media, which can reach wider audiences with the development of technology. The difficulties of users in accessing accurate and reliable sources in this information flow that shapes their daily lives increases the potential for the spread of fake news, and it becomes increasingly difficult to distinguish between real and fake news. In this study, classification models for fake news detection were designed using machine learning algorithms. The dataset, which includes fake and real news examples, contains 42,000 examples. Each class, including fake and real samples, contains 22,000 sample data. In order to increase data quality, accuracy and usability, preprocessing methods were applied to the data set. The removal of numbers, stop words, and html tags was done in the pre-processing step to remove unnecessary information from the text. Models were created for fake news detection with singular and ensemble classification algorithms. Performance evaluation of the models was performed using 5-fold cross-validation. In the performance comparisons of the models, values such as accuracy, sensitivity, specificity, tp rate and fp rate were calculated. The highest performance results were observed in the random forest classification algorithm with an accuracy rate of 76%.

People lie, actions Don't! Modeling infodemic proliferation predictors among social media users

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features; 2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.

Due to the widespread use of social media, people are exposed to fake news and misinformation. Spreading fake news has adverse effects on both the general public and governments. This issue motivated researchers to utilize advanced natural language processing concepts to detect such misinformation in social media. Despite the recent research studies that only focused on semantic features extracted by deep contextualized text representation models, we aim to show that content-based feature engineering can enhance the semantic models in a complex task like fake news detection. These features can provide valuable information from different aspects of input texts and assist our neural classifier in detecting fake and real news more accurately than using semantic features. To substantiate the effectiveness of feature engineering besides semantic features, we proposed a deep neural architecture in which three parallel convolutional neural network (CNN) layers extract semantic features from contextual representation vectors. Then, semantic and content-based features are fed to a fully connected layer. We evaluated our model on an English dataset about the COVID-19 pandemic and a domain-independent Persian fake news dataset (TAJ). Our experiments on the English COVID-19 dataset show 4.16% and 4.02% improvement in accuracy and f1-score, respectively, compared to the baseline model, which does not benefit from the content-based features. We also achieved 2.01% and 0.69% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art results reported by Shifath et al. (A transformer based approach for fighting covid-19 fake news, arXiv preprint arXiv:2101.12027, 2021). Our model outperformed the baseline on the TAJ dataset by improving accuracy and f1-score metrics by 1.89% and 1.74%, respectively. The model also shows 2.13% and 1.6% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art model proposed by Samadi et al. (ACM Trans Asian Low-Resour Lang Inf Process, https://doi.org/10.1145/3472620, 2021).

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

The availability and expansion of social media has made it difficult to distinguish between fake and real news. Information falsification has exponentially increased as a result of how simple it is to spread information through sharing. Social media dependability is also under jeopardy due to the extensive dissemination of false information. Therefore, it has become a research problem to automatically validate information, specifically source, content, and publisher, to identify it as true or false. Despite its limitations, machine learning (ML) has been crucial in the categorization of information. Previous studies suggested three-step methods for categorising false information on social media. In the first step of the process, the data set is subjected to a number of pre-processing processes in order to transform unstructured data sets into structured data sets. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by WOA (Whale Optimization Algorithm) for weight value to tune the classification part. Finally, a Hybrid Classification model that is hybrid with a fuzzy based Convolutional Neural Network and kernel based support vector machine is constructed in order to identify the data pertaining to bogus news. However using single classifier for fake news detection produces the insufficient accuracy. To overcome this issue in this work introduced an improved model for fake news classification. To turn unstructured data sets into structured data sets, a variety of pre-processing operations are used on the data set in the initial phase of the procedure. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by COA (Coati Optimization Algorithm) for weight value to tune the classification part. Finally, an ensemble of RNN (Recurrent Neural Networks), VGG-16 and ResNet50.A classification model was developed to recognise bogus news information. Evaluate each fake news analysis' performance in terms of accuracy, precision, recall, and F1 score. The suggested model, out of all the methodologies taken into consideration in this study, provides the highest outcomes, according to experimental findings

The spread propagation of fake news about COVID-19 can make it distressing to handle the pandemic situation. Identifying the fake and real news on social media needs to be done as quickly as possible to prevent chaos in the community and hampering the handling of COVID-19. In this study, we conducted some experiments to get a model that works well for classifying information into fake or real news using tweet data. We implemented two different ways to represent data to train machine learning classifier models, syntactic-based using Bag-of-Words and TF-IDF, and semantic-based using Word2Vec and FastText. We evaluated each model produced by the training process using two types of testing data. The results show that The Linear Support Vector Machine model using TF-IDF obtained the best F1-Score value in both testing data. The model obtained F1-Score 92.21% in Testing Data 1 and 93.33% in Testing Data 2.

Nowadays, current events related to diverse fields are published in newspapers, shared on social media and broadcasted on radio and television. The explosive growth in online news contents has made it very difficult to discriminate between real and fake. As a result, fake news has become prevalent and immensely challenging to analyze and verify. Indeed, it is a big challenge to the government and public to debate the situation depending on case to case. For this purpose, a mechanism has to be taken on fact-checking rumors and statements particularly those that get thousands of views and likes before being debunked and refuted by expert sources. Various machine learning techniques have been used to detect and classify fake news. However, these approaches are restricted in terms of accuracy. This study has applied a random forest (RF) classifier to predict fake or real news. For this purpose, twenty-three (23) textual features are extracted from ISOT Fake News Dataset. Four best feature selection techniques like chi2, univariate, information gain and feature importance are used to select fourteen best features out of twenty-three. The proposed model and other benchmark techniques are evaluated on benchmark dataset using best features. Experimental findings show that the proposed model outperformed state-of-the-art machine learning techniques such as GBM, XGBoost and Ada Boost Regression Model in terms of classification accuracy.

Analytic and intuitive reasoning processes have been implicated as important determinants of belief in (or skepticism of) fake news. However, the underlying cognitive mechanisms that encourage endorsement of fake news remain unclear. The present study investigated cognitive decoupling/response inhibition and the potential role of conflict processing in the initiation of analytic thought about fake news as factors that may facilitate skepticism. A base-rate task was used to test the hypotheses that conflict processing deficits and inefficient response inhibition would be related to stronger endorsement of fake news. In support of these hypotheses, increased belief in fake (but not real) news was associated with a smaller decrease in response confidence in the presence (vs. absence) of conflict and with inefficient (in terms of response latency) inhibition of prepotent responses. Through its support for these hypotheses, the present study advances efforts to determine who will fall for fake news, and why.

Classifier algorithms are a subfield of data mining and play an integral role in finding patterns and relationships within large datasets. In recent years, fake news detection has become a popular area of data mining for several important reasons, including its negative impact on decision-making and its virality within social networks. In the past, traditional fake news detection has relied primarily on information context, while modern approaches rely on auxiliary information to classify content. Modelling with machine learning and natural language processing can aid in distinguishing between fake and real news. In this research, we mine data from Reddit, the popular online discussion forum and social news aggregator, and measure machine learning classifiers in order to evaluate each algorithm’s accuracy in detecting fake news using only a minimal subset of data.

The widespread dissemination of fake news could have serious negative consequences for individuals and society. First, fake news could upset the balance of authenticity in the news ecosystem. For example, the most popular fake news was more prevalent on Facebook or Instagram media. Second, fake news intentionally persuaded consumers to accept biased or false beliefs. Third, fake news was changing the way people interpret and react to real news. For example, some fake news was created to mistrust and confuse people, so it was impossible, to tell the truth from what was not. To mitigate the negative impact of fake news, it was very important to develop methods to automatically detect fake news in social networks, namely problem-based learning, in order to differentiate between real and fake visual content. Qualitative and quantitative approaches were performed using experimental and control groups to determine whether problem learning could induce students to engage more actively with the topic and develop critical thinking skills to avoid the implicit bias of fake news. This study was conducted at Universitas Katolik Santo Thomas Medan, Indonesia. This research showed that problem-based learning could promote the development of learning communities where learners could freely exchange ideas and ask questions related to the material being studied. Therefore, problem-based learning was an effective way to improve the analytical ability to distinguish between real news and fake news based on the credibility of the news.

Along with the advancement of the world wide web, the rise and far reaching appropriation of the social site initiative have distorted the manner in which news is shaped and distributed. News has gotten quicker, less expensive and effectively available among web based life. This modify has joined a few hindrances also. Specifically, flabbergasting content, for example, fake news made by online networking clients, is getting progressively perilous. The fake news issue, in spite of being presented just because as of late, has become a significant examination theme because of the high substance of online networking. Writing fake remarks and news via web-based networking media is simple for clients. The primary test is to decide the distinction among genuine and fake news. We developed a method for the fake news classification on twitter. Web- based GUI is developed for the fake news classification system to categorize the tweets as fake or genuine. We develop a machine learning program to identify fake news by comparing tweets with genuine sources. Naive bayes and passive aggressive machine learning algorithms are estimated with TF-IDF feature extraction method.

Fake news analysis and detection is a method that requires using machine learning classifiers to recognize and detect news content as fake or real. No research work has been carried out on fake news analysis and detection using the machine learning algorithms using Truncated singular value decomposition (TSVD) on the ‘ISOT Fake News Dataset’ but there is a need for a better design that can provide better accuracy. This research aims to develop and use systematic progression in identifying fake news analysis using machine learning algorithms with TSVD. The machine learning algorithms which include the light Gradient Boosting Machine, Cat boosting classifier, and support vector machine with TSVD were used on ‘The ISOT Fake News Dataset’ to recognize the fake news from real news in the dataset, thereby improving the performance of these classifiers by cleaning the data to manage the data imbalances more efficiently. The recommended design yields an exactness of 97.33%. The postulated technique out classifies the current classifiers which possess an exactness of 95.05%. We recommend that Machine learning classifiers using TSVD should be applied in Fake News analysis and detection since it gives a better performance accuracy in fake news analysis and detection

Covid-19 fake news sentiment analysis