Extracting Feelings of People Regarding COVID-19 by Social Network Mining

Vahdat-Nejad, Hamed; Salmani, Fatemeh; Hajiabadi, Mahdi; Azizi, Faezeh; Abbasi, Sajedeh; Jamalian, Mohadese; Mosafer, Reyhaneh; Bagherzadeh, Parsa; Hajiabadi, Hamideh

doi:10.1142/s0219649222400081

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…It will induce Russia to pay compensation to Ukraine depending on the consequences of the war. In [39] author presented a lexicon-based approach with the topographical catalogue to indicate each tweet with its position and analyze the tweet's sentiment analysis which shows the frequency and sentiments for each country that correlate with the authorized facts of COVID-19. The highest emphasis on the nations with Coronavirus shows the frequency of the tweets with a significant correlation and extracts knowledge that was inherent in the tweets.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

Suman,

Varshney,

Chouhan

et al. 2024

IJCDS

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X (formerly Twitter) has become a vital source of information on various variety of social, political, and economic concerns, as a consequence of its growth and popularity which has resulted in an enormous number of people sharing their opinions on a wide range of areas. To determine people's emotions about the Russia-Ukraine war (RUW), this study examines trends in English-language tweets. In this work, we have engaged 34 countries to tweet opinions that produce a strong perception of the people about the war and message to the world what people famine from the countries and that affects their lives. To analyze positive and negative emotions in tweets, which are represented by hope and fear, the LSTM-CNN model is based on deep learning. A time series is calculated that correlates with the rate of recurrence of negative and positive tweets in different nations. Additionally, an approach based on the average of the neighborhood has been used for modelling and grouping the time series of various countries. The clustering method gives results as significant information, how people feel about this dispute and share their opinions about RUW is approached. When compare on different models on overall data that the 96% accuracy is Achieved by the LSTM-CNN model. 97.09% accuracy, is achieved when the comparing the tweets from the cluster 1 countries. When comparing the tweets from the cluster 2 countries the 99% accuracy, is achieved. 97% accuracy is achieved by comparing the tweets from cluster 3 countries. 97% accuracy, is achieved when the comparing the tweets from cluster 4 countries. 96% accuracy is achieved by the LSTM-CNN model when the comparing the tweets from cluster 5 countries by the different models. This research study helps the uninfluenced press members to have an impartial source of information for their reports and articles.

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Section: Literature Surveymentioning

confidence: 99%

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

Suman,

Varshney,

Chouhan

et al. 2024

IJCDS

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“…Vahdat-Nejad [25] argued that regarding the COVID-19, the public which is the main audience can be segmented into different age, job and sex groups as well as to those with underlying disease which make them more conscious to COVID-19. They also argued that communication goal should be adherence to healthy behaviour and instructions related to COVID-19.…”

Section: Introductionmentioning

confidence: 99%

The Use of Internet and Social Networks During covid-19 in Greece

Tsourgiannis

Valsamidis

Karagianni³

et al. 2023

KSS

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Human civilization has been transformed dramatically due to the increase of information and communication technologies (ICTs). The new digital era, and more particularly the digitization of information has contributed to many changes in all areas of life. More specifically the social relations among people developed new social and cultural structures. Furthermore, the COVID-19 pandemic forced humanity to adopt digital technologies in most aspects of economic and social life. Internet, telework, remote work, and distance learning are now part of everyday life in society, and users of online social networks have increased dramatically in comparison with those of recent decades around the world. On the other hand, the situation that human experience due to the pandemic produced by COVID-19 disease may have increased the negative effects of excessive use of social networks. This study aims to explore the attitudes of Greek citizens toward the use of the Internet and social media before and during the pandemic. A primary survey on random 525 Greek citizens was conducted from September–November 2021. Principal component analysis (PCA) was conducted to identify the main attitudes of Greek citizens toward the use of social media during COVID-19 era. Therefore, two main attitudes were derived from PCA: (a) use of social media to be informed, and (b) use of social media for entertainment. Cluster analysis was performed to classify those citizens into groups according to their attitudes toward the use of social networks during COVID-19 period. It identified three groups of citizens: (a) those who are indifferent to the use of social networks (b) those who use social networks mainly to be informed, and (c) those who use social networks only for entertainment. Following that, a Friedman nonparametric test was performed to determine the primary reasons why Greek citizens use the Internet and social networks prior to and during the COVID-19 pandemic. Nonparametric tests, including the Chi-square and Friedman nonparametric tests were performed to develop the profile of each of the identified groups of citizens toward the main reasons they use the Internet and social networks, for what purposes, and their demographic characteristics. Keywords: social networks, Internet, COVID-19

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