Memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks

The rapid advancements of social media networks have created the problem of overloaded information. As a result, the service providers push multiple redundant contents and advertisements to the users without adequate analysis of the user interests. The content recommendation without user interests reduces the probability of users reading them and the wastage rate of network load increases. This problem can be alleviated by providing accurate content recommendations with consideration of users' precise interests and content similarity. Content centric networking has been developed as the trending framework to satisfy these requirements and improve access to relevant information and reception by the desired user. The uses of message entity by giving a proper name, the users' real-time interests are identified and then the accurate and popular contents with high contextual similarity are recommended. An efficient content recommendation scheme is presented in this paper using Memory Augmented Distributed Monte Carlo Tree Search (MAD-MCTS) algorithm for ensuring minimum energy consumption in the CCN. The big data context of the users' social media data is considered in this study so that the complexity can be visualized and controlled to minimize the network complexities. Experiments are conducted on a benchmark as well as an offline collected Twitter dataset on Covid-19 and the results implied that the accuracy and convergence of the proposed MAD-MCTS outperform the other content recommendation algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Tracking News Stories in Short Messages in the Era of Infodemic

Tracking news stories in documents is a way to deal with the large amount of information that surrounds us everyday, to reduce the noise and to detect emergent topics in news. Since the Covid-19 outbreak, the world has known a new problem: infodemic. News article titles are massively shared on social networks and the analysis of trends and growing topics is complex. Grouping documents in news stories lowers the number of topics to analyse and the information to ingest and/or evaluate. Our study proposes to analyse news tracking with little information provided by titles on social networks. In this paper, we take advantage of datasets of public news article titles to experiment news tracking algorithms on short messages. We evaluate the clustering performance with little amount of data per document. We deal with the document representation (sparse with TF-IDF and dense using Transformers [26]), its impact on the results and why it is key to this type of work. We used a supervised algorithm proposed by Miranda et al. [22] and K-Means to provide evaluations for different use cases. We found that TF-IDF vectors are not always the best ones to group documents, and that algorithms are sensitive to the type of representation. Knowing this, we recommend taking both aspects into account while tracking news stories in short messages. With this paper, we share all the source code and resources we handled. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Sentiment Analysis on Covid-19 Vaccinations in Ireland using Support Vector Machine

Monitoring and analyzing social data is currently a norm to gauge public sentiments for efficiently marketing prod-ucts and services. With the recent outbreak of the Coronavirus disease 2019 (Covid-19) and subsequent vaccination programs, it became essential to spread awareness and understand the public sentiments on Covid-19 vaccines. This paper describes the life-cycle of conducting a Sentiment Analysis (SA) on the Covid-19 vaccination program in Ireland. Global and Irish Tweets were collected via Twitter API from January 2020 to August 2021. A lexicon and rule-based VADER tool labelled the global dataset as negative, positive, and neutral. After that, Irish tweets were classified into different sentiments using Support Vector Machine (SVM). Results show positive sentiment toward vaccines at the beginning of the vaccination drive, however, this sentiment gradually changed to negative in early 2021. © 2022 IEEE.

COVID-19 Sentiment Analysis applying BERT

The growth of social data on the internet has accelerated during the last two decades. As a result, researchers can access data and information for various academic and commercial purposes. The novel coronavirus disease (COVID-19) is a current pandemic that has sparked widespread concern worldwide. Spreading misleading information on social media platforms like Twitter, on the other hand, is exacerbating the disease's concern. This research aims to examine tweets and develop a model that can detect public sentiment from social media posts;consequently, necessary precautions can be taken to preserve adequate validity of information for the general public. We believe that various social media platforms have a significant impact on creating public awareness about the disease's importance and encouraging preventive measures among community members. For this study, we applied the Bidirectional Encoder Representations from Transformers (BERT) model, a new deep-learning technique for text analysis and performance with exceptional multi-class accuracy. We also compared it with six shallow machine learning models. © 2022 IEEE.

COS2: Detecting Large-Scale COVID-19 Misinformation in Social Networks

The ongoing COVID-19 pandemic is bringing an “infodemic” on social media. Simultaneously, the huge volume of misinformation (such as rumors, fake news, spam posts, etc.) is scattered in every corner of people’s social life. Traditional misinformation detection methods typically focus on centralized offline processing, that is, they process pandemic-related social data by deploying the model in a single local server. However, such processing incurs extremely long latency when detecting social misinformation related to COVID-19, and cannot handle large-scale social misinformation. In this paper, we propose COS2, a distributed and scalable system that supports large-scale COVID-19-related social misinformation detection. COS2 is able to automatically deploy many groups to distribute deep learning models in scalable cloud servers, process large-scale COVID-19-related social data in various groups, and efficiently detect COVID-19-related tweets with low latency. © 2022, Springer Nature Switzerland AG.