We analyze the betweenness centrality (BC) of nodes in large complex networks. In general, the BC is increasing with connectivity as a power law with an exponent $\eta$. We find that for trees or networks with a small loop density $\eta=2$ while a larger density of loops leads to $\eta<2$. For scale-free networks characterized by an exponent $\gamma$ which describes the connectivity distribution decay, the BC is also distributed according to a power law with a non universal exponent $\delta$. We show that this exponent $\delta$ must satisfy the exact bound $\delta\geq (\gamma+1)/2$. If the scale free network is a tree, then we have the equality $\delta=(\gamma+1)/2$.

Betweenness Centrality in Large Complex Networks

The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.

/pdf/a-performance-comparison-of-supervised-machine-learning-1rt40b4tdw.pdf

A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.

In the past two decades, the growth of social data on the web has rapidly increased. This leads to researchers to access the data and information for many academic research and commercial uses. Social data on the web contains many real life events that occurred in daily life, today the global COVID-19 disease is spread worldwide. Many individuals including media organizations and government agencies are presenting the latest news and opinions regarding the coronavirus. In this study, the twitter data has been pulled out from Twitter social media, through python programming language, using Tweepy library, then by using TextBlob library in python, the sentiment analysis operation has been done. After the measuring sentiment analysis, the graphical representation has been provided on the data. The data we have collected on twitter are based on two specified hashtag keywords, which are (“COVID-19, coronavirus”). The date of searching data is seven days from 09-04-2020 to 15-04-2020. In the end a visualized presentation regarding the results and further explanation are provided.

/pdf/twitter-sentiment-analysis-on-worldwide-covid-19-outbreaks-4aa1cvju9q.pdf

Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks

Nowadays, the whole world is confronting an infectious disease called the coronavirus. No country remained untouched during this pandemic situation. Due to no exact treatment available, the disease has become a matter of seriousness for both the government and the public. As social distance is considered the most effective way to stay away from this disease. Therefore, to address the people eagerness about the Corona pandemic and to express their views, the trend of people has moved very fast towards social media. Twitter has emerged as one of the most popular platforms among those social media platforms. By studying the same eagerness and opinions of people to understand their mental state, we have done sentiment analysis using the BERT model on tweets. In this paper, we perform a sentiment analysis on two data sets; one data set is collected by tweets made by people from all over the world, and the other data set contains the tweets made by people of India. We have validated the accuracy of the emotion classification from the GitHub repository. The experimental results show that the validation accuracy is $$\approx$$
 94%.

/pdf/sentiment-analysis-on-the-impact-of-coronavirus-in-social-1du83vzxje.pdf

Sentiment analysis on the impact of coronavirus in social life using the BERT model

Recently, the population density in cities has increased at a higher pace. According to the United Nations Population Fund, cities accommodated 3.3 billion people (54%) of the global population in 2014. By 2050, around 5 billion people (68%) will be residing in cities. In order to make lifestyles in cities more comfortable and cost-effective, the city must be smart and intelligent. It is mainly accomplished through an intelligent decision-making process using computational intelligence-based technologies. This paper explored how artificial intelligence (AI) is being used in the smart city concept. From 2014 to 2021, we examined 133 articles (97% of Scopus and 73% of WoS) in healthcare, education, environment and waste management, agriculture, mobility and smart transportation, risk management, and security. Moreover, we observed that the healthcare (23% impact), mobility (19% impact), privacy and security (11% impact), and energy sectors (10% impact) have a more significant influence on AI adoption in smart cities. Since the epidemic hit cities in 2019, the healthcare industry has intensified its AI-based advances by 60%. According to the analysis, AI algorithms such as ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVM have a higher impact on the various smart city domains. 

Adoption of artificial intelligence in smart cities: A comprehensive review

The Coronavirus pandemic has taken the world by storm as also the social media. As the awareness about the ailment increased, so did messages, videos and posts acknowledging its presence. The social networking site, Twitter, demonstrated similar effect with the number of posts related to coronavirus showing an unprecedented growth in a very short span of time. This paper presents a statistical analysis of the twitter messages related to this disease posted since January 2020. Two types of empirical studies have been performed. The first is on word frequency and the second on sentiments of the individual tweet messages. Inspection of the word frequency is useful in characterizing the patterns or trends in the words used on the site. This would also reflect on the psychology of the twitter users at this critical juncture. Unigram, bigram and trigram frequencies have been modeled by power law distribution. The results have been validated by Sum of Square Error (SSE), R2 and Root Mean Square Error (RMSE). High values of R2 and low values of SSE and RMSE lay the grounds for the goodness of fit of this model. Sentiment analysis has been conducted to understand the general attitudes of the twitter users at this time. Both tweets by general public and WHO were part of the corpus. The results showed that the majority of the tweets had a positive polarity and only about 15% were negative.

Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic

An edge AI-enabled IoT healthcare monitoring system for smart cities

Blockchain and Governance: Theory, Applications and Challenges

Indian Railway Network has been analyzed on the basis of number of trains directly linking two railway zones. The network has been displayed as a weighted graph where the weights denote the number of trains between the zones. It may be pointed out that each zone is a complex network in itself and may depict different characteristic features. The zonal network therefore can be considered as a network of complex networks. In this paper, self links, in-degree and out-degree of each zone have been computed which provides information about the inter and intra zonal connectivity. Degree passenger correlation which gives an idea about number of trains and passengers originating from a particular zone which might play a role in policy making decisions has also been studied. Some other complex network parameters like betweenness, clustering coefficient and cliques have been obtained to get more insight about the complex Indian zonal network.

Bhavya Ahuja Grover

Papers

Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic

An edge AI-enabled IoT healthcare monitoring system for smart cities

Blockchain and Governance: Theory, Applications and Challenges

Complex Network Analysis of Indian Railway Zones

A multi-label movie genre classification scheme based on the movie’s subtitles