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

FNDNet – A deep convolutional neural network for fake news detection

One of the challenges today is to face fake news (false information) in health due to its potential impact on people's lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media.

https://www.mdpi.com/1660-4601/17/7/2430/pdf

A New Application of Social Impact in Social Media for Overcoming Fake News in Health.

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.

Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection

Detection and veracity analysis of fake news via scrapping and authenticating the web search

In the present era, the social network is used as an important medium for sharing thoughts and opinions of an individual. The main reason behind this is, it provides a fast-spreading of information among the public easily, requiring a very low cost of access. This leads to having online social media as one of the stepping stones to encourage false content and influencing public opinion and its decision. Rumour is one of the prominent forms of misleading information on social media and should be detected as early as possible for avoiding their significant effects. Due to these reasons, the researchers have put their keen interest in developing an effective rumour detection framework in the last years. In this paper, we mainly focused on six main aspects. Firstly, we discuss rumours from a definition perspective that have been considered in the state-of-the-art and describe the generalized model of rumour detection. Secondly, we discuss how to get access to data from different social media platforms, and presents various state-of-the-art methods to gather these data, as well as publicly available datasets. Third, we describe a different set of features that have been considered in rumour detection approaches. Fourth, we provide deep insight into the various methods used to employ rumour detection and its veracity assessment on multimedia data (Text and Images) with some practical implications. Whereas in the fifth aspect, the constraints of the study have been discussed. Finally, we concluded with useful findings and suggested future directions.

A review on rumour prediction and veracity assessment in online social network

Clickbait is one of the form of false content, purposely designed to attract the user’s attention and make them curious to follow the link and read, view, or listen to the attached content. The teaser aim behind this is to exploit the curiosity gap by giving information within the short statement. Still, the given statement is not sufficient enough to satisfy the curiosity without clicking through the linked content and lure the user to get into the respective page via playing with human psychology and degrades the user experience. To counter this problem, we develop a Clickbait Video Detector (CVD) scheme. The scheme leverages to learn three sets of latent features based on User Profiling, Video-Content, and Human Consensus, these are further used to retrieve cognitive evidence for the detection of clickbait videos on YouTube. The first step is to extract audio from the videos, which is further transformed to textual data, and later on, it is utilized for the extraction of video content-based features. Secondly, the comments are analyzed, and features are extracted based on human responses/reactions over the posted content. Lastly, user profile based features are extracted. Finally, all these features are fed into the classifier. The proposed method is tested on the publicly available fake video corpus [FVC], [FVC-2018] dataset, and a self-generated misleading video dataset [MVD]. The achieved result is compared with other state-of-the-art methods and demonstrates superior performance.

/pdf/a-unified-approach-for-detection-of-clickbait-videos-on-4jq2351lpw.pdf

A unified approach for detection of Clickbait videos on YouTube using cognitive evidences

Nowadays social media is one of the important medium of sharing thoughts and opinions of the individual due to its easy access and also it provides an opportunity to the malicious user to post deliberately fabricated false content to influence people for creating controversies, playing with public emotions, etc. The spread of contaminated information such as Rumours, Hoax, Accidental misinformation, etc. over the web is becoming an emergency situation that can have a very harmful impact on society and individuals. In this paper, we have developed an automated system “Hoax-News Inspector” for the detection of fake news that propagates through the web and social media in the form of text. To distinguish fake and real reports on an early basis, we identified prominent features by exploring two sets of attributes that lead to information spread: Article/post-content-based features, Sentiment based features and the mixture of both called as Hybrid features. The proposed algorithm is trained and tested on the self-generated dataset as well as one of the popular existing datasets Liar. It has been found that the proposed algorithm gives the best results using the Random Forest classifier with an accuracy of 95% by considering all sets of features. Detecting and verifying news have many practical applications for business markets, news consumers, and time-sensitive services, which generally help to minimize the spread of false information. Our proposed system Hoax News-Inspector can automatically collect fabricated news data and classify it into binary classes Fake or Real, which later benefits further research for predicting and understanding Fake news.

Hoax news-inspector: a real-time prediction of fake news using content resemblance over web search results for authenticating the credibility of news articles

In the current scenario social media platforms are one the efficient way to share opinions and thoughts of an individual. User can freely share their thoughts on an event/ situation. This can be a curse for the society if social media platform is utilized with some bad intention to spread false information and create chaos/ confusion among public which greatly degrades user experience. In the current pandemic many people have their eye on any news article related to corona cure. Malicious users take this as an opportunity to spread fake news in order to create confusion among public or some monetary benefits, the detection of which is of paramount importance. The proposed technique is leverages to learn crucial evidences based on Context Knowledge, Distance Metric and Word Resemblance with respect to news article headline and its content concerning top 10 google search results related to the claim, where considering COVID-19 as one of the special case studies from the application perspective. This paper proposed a novel scheme for the prediction of false information and generated a covidfakenews dataset that further be utilized for the analysis and evaluation of our model. The results reveals that the proposed intelligent strategy gives promising experimental results and quite effective in predicting False information.

Deepika Varshney

Papers

Detection and veracity analysis of fake news via scrapping and authenticating the web search

A review on rumour prediction and veracity assessment in online social network

A unified approach for detection of Clickbait videos on YouTube using cognitive evidences

Hoax news-inspector: a real-time prediction of fake news using content resemblance over web search results for authenticating the credibility of news articles

Analysing and Identifying Crucial Evidences for the prediction of False Information proliferated during COVID-19 Outbreak: A Case Study