Social media network is one of the important parts of human life based on the recent technologies and developments in terms of computer science area. This environment has become a famous platform for sharing information and news on any topics and daily reports, which is the main era for collecting data and data transmission. There are various advantages of this environment, but in another point of view there are lots of fake news and information that mislead the reader and user for the information needed. Lack of trust-able information and real news of social media information is one of the huge problems of this system. To overcome this problem, we have proposed an integrated system for various aspects of blockchain and natural language processing (NLP) to apply machine learning techniques to detect fake news and better predict fake user accounts and posts. The Reinforcement Learning technique is applied for this process. To improve this platform in terms of security, the decentralized blockchain framework applied, which provides the outline of digital contents authority proof. More specifically, the concept of this system is developing a secure platform to predict and identify fake news in social media networks.

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Fake Media Detection Based on Natural Language Processing and Blockchain Approaches

Coal industry is a typical high-risk industry with frequent accidents. In an effort to ensure workers’ safety and health, and reduce the probability of productivity decrease, it is essential to identify the contributing factors of coal mine safety production risks through certain technical means. Accident cases, as a concentrated display of accident hazard source, are of great value in extracting key risk factors that may induce coal mine disasters. Therefore, this study creatively proposed an effective method combining text mining, association rule mining and Bayesian network to deeply mine and use the massive coal mine safety accident case text data, so as to achieve effective identification of coal mine safety risk factors and explore the mechanism of interaction between risk factors and their importance. The research main included three steps. First, due to the high uncertainty and difference in the way of expression of the coal mine accident report texts, the conventional text mining process cannot effectively identify the risk factors, resulting in the incompleteness and deviation of the risk factors list. This study improved the text mining process, through Chinese word segmentation, keyword extraction, related word mining, semantic analysis, etc. to mine the collected 726 reports, and identify 78 safety risk factors. Then, the Apriori algorithm was used to obtain the extremely frequent itemset of risk factors and 362 strong association rules, and constructed the Bayesian network model on this basis. Finally, six main risk factors of coal mine safety production and their associated-factors were clarified through sensitivity and critical path analysis. The study shows that compared with the risks caused by the environment and equipment, the lack of management, education, and supervision are the root cause of coal mine accidents. This research provides a new way of thinking for effectively extracting using information from unstructured and non-standardized texts, as well as a new perspective for data-driven safety risk factors identification and complex interaction mechanisms research, having a great significance for coal mine safety risk pre-control management. 

Identifying coal mine safety production risk factors by employing text mining and Bayesian network techniques

This paper is inspired by the PQ penny flip game. It employs group-theoretic concepts to study the original game and its possible extensions. In this paper, it is shown that the PQ penny flip game can be associated, in a precise way, with the dihedral group D8 and that within D8 there exist precisely two classes of equivalent winning strategies for Q. This is achieved by proving that there are exactly two different sequences of states that can guarantee Q’s win with probability 1.0. It is demonstrated that the game can be played in every dihedral group D8n, where n≥1, without any significant change. A formal examination of what happens when Q can draw their moves from the entire U(2), leads to the conclusion that, again, there are exactly two classes of winning strategies for Q, each class containing an infinite number of equivalent strategies, but all of them sending the coin through the same sequence of states as before. Finally, when general extensions of the game, with the quantum player having U(2) at their disposal, are considered, a necessary and sufficient condition for Q to surely win against Picard is established: Q must make both the first and the last move in the game.

https://www.mdpi.com/2227-7390/9/10/1115/pdf

The Connection between the PQ Penny Flip Game and the Dihedral Groups

Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.

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OntoED: Low-resource Event Detection with Ontology Embedding

In the past decade, the rapid spread of large volumes of online information among an increasing number of social network users is observed. It is a phenomenon that has often been exploited by malicious users and entities, which forge, distribute, and reproduce fake news and propaganda. In this paper, we present a novel approach to the automatic detection of fake news on Twitter that involves (a) pairwise text input, (b) a novel deep neural network learning architecture that allows for flexible input fusion at various network layers, and (c) various input modes, like word embeddings and both linguistic and network account features. Furthermore, tweets are innovatively separated into news headers and news text, and an extensive experimental setup performs classification tests using both. Our main results show high overall accuracy performance in fake news detection. The proposed deep learning architecture outperforms the state-of-the-art classifiers, while using fewer features and embeddings from the tweet text.

Deep Learning for Fake News Detection in a Pairwise Textual Input Schema

Prisoners’ Dilemma is a well-known game in game theory with many variations and applications in a number of fields. The addition of quantum strategies in this game opens up new possibilities and changes the equilibria of the game significantly.

A Brief Survey of the Prisoners' Dilemma Game and Its Potential Use in Biology.

The rapid development of network services has led to the exponential growth of online information and the increasing number of social media users. These services are exploited by malicious accounts that spread fake news and propaganda in vast user networks. Consequently, an automated solution for fake news and deception detection is required. This paper introduces a new data set consisting of 2,366 tweets written in English, regarding the Hong Kong events (August, 2019), and a well-defined method for fake news detection that uses both linguistic and network features. Our approach is tested with experiments using 2 machine learning models, achieving high performance compared to previous research.

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Fake News Detection Regarding the Hong Kong Events from Tweets

Mining social web text has been at the heart of the Natural Language Processing and Data Mining research community in the last 15 years. Though most of the reported work is on widely spoken languages, such as English, the significance of approaches that deal with less commonly spoken languages, such as Greek, is evident for reasons of preserving and documenting minority languages, cultural and ethnic diversity, and identifying intercultural similarities and differences. The present work aims at identifying, documenting and comparing social text data sets, as well as mining techniques and applications on social web text that target Modern Greek, focusing on the arising challenges and the potential for future research in the specific less widely spoken language.

https://www.mdpi.com/2306-5729/6/5/52/pdf

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Web-based learning platforms are now offering a considerable amount of training options, exhibiting great variability, similar to the one encountered by a potential customer in electronic shops. To mitigate this, efficient and effective recommendations engines are needed, capable of satisfying the specific needs of each user, while achieving the best possible promotion of available online training. This paper discusses the design of a potential generic architecture for online education recommender systems, specifically targeted for promoting online courses and web-based learning material. From an algorithmic perspective, the system relies on item-based and user-based collaborative filtering approaches. It extends this approach with a genetic algorithm, thus increasing its potential impact. Overall, the paper paves the ground for the specification of generic principles governing the design of personalized online education platforms as well as identifying metrics for evaluating their performance.

Maria Nefeli Nikiforos

Papers

A Brief Survey of the Prisoners' Dilemma Game and Its Potential Use in Biology.

Deep Learning for Fake News Detection in a Pairwise Textual Input Schema

Fake News Detection Regarding the Hong Kong Events from Tweets

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

Enhancing Collaborative Filtering Recommendations for Web-based Learning Platforms with Genetic Algorithms