The explosion of social media allowed individuals to spread information without cost, with little investigation and fewer filters than before. This amplified the old problem of fake news, which became a major concern nowadays due to the negative impact it brings to the communities. In order to tackle the rise and spreading of fake news, automatic detection techniques have been researched building on artificial intelligence and machine learning. The recent achievements of deep learning techniques in complex natural language processing tasks, make them a promising solution for fake news detection too. This work proposes a novel hybrid deep learning model that combines convolutional and recurrent neural networks for fake news classification. The model was successfully validated on two fake news datasets (ISO and FA-KES), achieving detection results that are significantly better than other non-hybrid baseline methods. Further experiments on the generalization of the proposed model across different datasets, had promising results.

Fake news detection: A hybrid CNN-RNN based deep learning approach

Twitter is the third most popular worldwide Online Social Network (OSN) after Facebook and Instagram. Compared to other OSNs, it has a simple data model and a straightforward data access API. This makes it ideal for social network studies attempting to analyze the patterns of online behavior, the structure of the social graph, the sentiment towards various entities and the nature of malicious attacks in a vivid network with hundreds of millions of users. Indeed, Twitter has been established as a major research platform, utilized in more than ten thousands research articles over the last ten years. Although there are excellent review and comparison studies for most of the research that utilizes Twitter, there are limited efforts to map this research terrain as a whole. Here we present an effort to map the current research topics in Twitter focusing on three major areas: the structure and properties of the social graph, sentiment analysis and threats such as spam, bots, fake news and hate speech. We also present Twitter’s basic data model and best practices for sampling and data access. This survey also lays the ground of computational techniques used in these areas such as Graph Sampling, Natural Language Processing and Machine Learning. Along with existing reviews and comparison studies, we also discuss the key findings and the state of the art in these methods. Overall, we hope that this survey will help researchers create a clear conceptual model of Twitter and act as a guide to expand further the topics presented.

A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks

News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19’s communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This article presents a large-scale study based on data mined from Twitter . Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users’ profiles, meta-data and tweets’ context. The study noted various interesting conclusions including the critical impact in term of reach level of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.

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Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

Social media is used as a dominant source of news distribution among users. The world's preeminent decisions such as politics are acclaimed by social media to influence users for enclosing users' decisions in their favor. However, the adoption of social media is much needed for awareness but the authenticity of content is an unknown factor in the current scenario. Therefore, this research work finds it imperative to propose a solution to fake news detection and classification. In the case of fake news, content is the prime entity that captures the human mind towards trust for specific news. Therefore, a linguistic model is proposed to find out the properties of content that will generate language-driven features. This linguistic model extracts syntactic, grammatical, sentimental, and readability features of particular news. Language driven model requires an approach to handle time-consuming and handcrafted features problems in order to deal with the curse of dimensionality problem. Therefore, the neural-based sequential learning model is used to achieve superior results for fake news detection. The results are drawn to validate the importance of the linguistic model extracted features and finally combined linguistic feature-driven model is able to achieve the average accuracy of 86% for fake news detection and classification. The sequential neural model results are compared with machine learning based models and LSTM based word embedding based fake news detection model as well. Comparative results show that features based sequential model is able to achieve comparable evaluation performance in discernable less time.

Linguistic feature based learning model for fake news detection and classification

Rumors on social media have always been an important issue that seriously endangers social security. Researches on timely and effective detection of rumors have aroused lots of interest in both academia and industry. At present, most existing methods identify rumors based solely on the linguistic information without considering the temporal dynamics and propagation patterns. In this work, we aim to solve rumor detection task under the framework of representation learning. We first propose a novel way to construct the propagation graph by following the propagation structure (who replies to whom) of posts on Twitter. Then we propose a gated graph neural network based algorithm called PGNN, which can generate powerful representations for each node in the propagation graph. The proposed PGNN algorithm repeatedly updates node representations by exchanging information between the neighbor nodes via relation paths within a limited time steps. On this basis, we propose two models, namely GLO-PGNN (rumor detection model based on the global embedding with propagation graph neural network) and ENS-PGNN (rumor detection model based on the ensemble learning with propagation graph neural network). They respectively adopt different classification strategies for rumor detection task, and further improve the performance by including attention mechanism to dynamically adjust the weight of each node in the propagation graph. Experiments on a real-world Twitter dataset demonstrate that our proposed models achieve much better performance than state-of-the-art methods both on the rumor detection task and early detection task.

Rumor detection based on propagation graph neural network with attention mechanism.

Internet and social media have become a widespread, large scale and easy to use platform for real-time information dissemination. It has become an open stage for discussion, ideology expression, knowledge dissemination, emotions and sentiment sharing. This platform is gaining tremendous attraction and a huge user base from all sections and age groups of society. The matter of concern is that up to what extent the contents that are circulating among all these platforms every second changing the mindset, perceptions and lives of billions of people are verified, authenticated and up to the standards. This paper puts forward a holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm. Further, a taxonomy is provided for the classification of malicious information content at different stages and prevalent technologies to cope up with this issue form origin, propagation, detection and containment stages. We also put forward a research gap and possible future research directions so that the web information content could be more reliable and safer to use for decision making as well as for knowledge sharing.

Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities

HAN, image captioning, and forensics ensemble multimodal fake news detection

Internet-based information circulation has given rise to the proliferation of fake and misleading contents, which has extreme hostile effects on individuals and humanity. Supervised artificial intelligence techniques require a huge amount of annotated data which is a time-consuming, expensive and laborious task as the speed and volume of social media news generation is very high. To counter this situation, we propose an innovative Convolutional Neural Network semi-supervised framework built on the self-ensembling concept to take leverage of the linguistic and stylometric information of annotated news articles, at the same time explore the hidden patterns in unlabelled data as well. Self-ensembling provides consensus predictions of the labels of unannotated data using previous epochs outputs of network-in-training. These accumulated ensemble predictions are supposed to be a better predictor for the unknown labels than the output of most recent training epoch, thus suitable to be used as a proxy for the labels of unannotated data. The uniqueness of the framework is that it ensembles all the outputs of previous training epochs of the neural network to use them as an unsupervised target for comparing them with current output prediction of unlabelled articles. The framework is validated with extensive experiments on three datasets for different proportions of labelled and unlabelled data. It can achieve highest 97.45% fake news classification accuracy using 50% labelled articles on Fake News Data Kaggle dataset. Contemporary baseline methods are placed in juxtaposition with the proposed architecture which demonstrates the robustness of our work compared to the state-of-the-art.

A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles

An upsurge of false information revolves around the internet Social media and websites are flooded with unverified news posts These posts are comprised of text, images, audio, and videos There is a requirement for a system that detects fake content in multiple data modalities We have seen a considerable amount of research on classification techniques for textual fake news detection, while frameworks dedicated to visual fake news detection are very few We explored the state-of-the-art methods using deep networks such as CNNs and RNNs for multi-modal online information credibility analysis They show rapid improvement in classification tasks without requiring pre-processing To aid the ongoing research over fake news detection using CNN models, we build textual and visual modules to analyze their performances over multi-modal datasets We exploit latent features present inside text and images using layers of convolutions We see how well these convolutional neural networks perform classification when provided with only latent features and analyze what type of images are needed to be fed to perform efficient fake news detection We propose a multi-modal Coupled ConvNet architecture that fuses both the data modules and efficiently classifies online news depending on its textual and visual content We thence offer a comparative analysis of the results of all the models utilized over three datasets The proposed architecture outperforms various state-of-the-art methods for fake news detection with considerably high accuracies

ConvNet frameworks for multi-modal fake news detection

In March 2020, Coronavirus disease was officially announced as a pandemic all over the world by the World Health Organization (WHO). Since then, the whole pharmaceutical world is in a state of war with COVID-19 and has a responsibility to provide its vaccine for the entire world as soon as possible. People have come forward and spoken about their emotions on both the mainstream and the social media. This study aims to analyze COVID-19 Vaccine related tweets and generate a report on that analysis. The motivation behind this study is the overspread of the virus and the increasing number of cases daily. The method used for topic modeling is a latest statistical model in Natural Language Processing (NLP), and it belongs to the machine learning toolbox and artificial intelligence toolbox, Latent Dirichlet Allocation (LDA) to determine popular themes, and for sentiment analysis, VADER and TextBlob are used on the dataset of 980,557 tweets which were scraped from Twitter based on a particular set of 14 keywords namely, "Covaxin," "BNT162b2", "SputnikV," and "mRNA-1273" from November 1 to December 16 in 2020.

Priyanka Meel

Papers

Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities

HAN, image captioning, and forensics ensemble multimodal fake news detection

A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles

ConvNet frameworks for multi-modal fake news detection

Analysing Public Sentiments Regarding COVID-19 Vaccine on Twitter