Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic

Amanatidis, Dimitrios; Mylona, Ifigeneia; Kamenidou, Irene; Mamalis, Spyridon; Stavrianea, Aikaterini

doi:10.3390/app11094281

Cited by 22 publications

(14 citation statements)

References 43 publications

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“…This way, they were able to take the sentiment analysis a step further and explain some of the changes in the data. The three leading companies who were involved in research on vaccines were identified as Pfizer, AstraZeneca, and Johnson and Johnson [ 11 ]; researchers extracted their Instagram posts from the start of vaccination and received data from users using their own hashtags. The companies' qualitative variations in manuscripts and visual characteristics (i.e., image categorization by transfer learning) are initially presented in this research.…”

Section: Introductionmentioning

confidence: 99%

“…In this way they were able to take the sentimental analysis a step further and explain some of the changes in the data. The three leading companies are identified: Pfizer, AstraZeneca and Johnson and Johnson, who are involved in research on vaccines and researchers [11] extract their Instagram posts from the start of vaccination and who receive data from users using their own hashtags. The company qualitative variations on manuscripts and visual characteristics, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Alam

Khan

Dhruba

et al. 2021

Computational and Mathematical Methods in Medicine

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The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Alam

Khan

Dhruba

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…AstraZeneca had an Instagram account longer than the other two; had the most posts and the highest rate of posting. However, it has the lowest number of followers (Amanatidis et al, 2021).…”

Section: Subtheme: Qualitative Researchmentioning

confidence: 99%

Do Not Come Near Me, Mrs Rona is here: A Systematic Review on the Use of Instagram during COVID-19

Abdul¹,

Muhammad²,

Harun³

et al. 2022

IJARAFMS

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COVID-19 pandemic infected the world in 2019 and resulted in quarantines and isolation measures that have proven to contain the virus. The isolation exercise had caused drastic changes to normal daily activities, primarily affecting interpersonal relations among humans, where most interactions have now taken place digitally. As a result of these changes, daily online usage has increased with social network applications, including Instagram, dominating the world's most visited websites. This internet platform's usage is mainly for finding information, connecting with loved ones, and keeping up-to-date with news and happenings worldwide. This emerging phenomenon has progressively shifted the communication landscape of both the personal and the business world. These entities of traditional sources are now being integrated with improved online mediators. Recent studies have also highlighted how the interactions of global societies on digital media like Instagram impacted society's education, social life, leisure, and health. Therefore, this systematic review paper is timely to analyze the existing literature on the use of Instagram during the COVID-19 pandemic and provides further insights in highlighting the extended usage and functions of Instagram during an emergency state like the COVID-19 pandemic. Based on PRISMA Statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a systematic review of four (4) main processes was conducted on Scopus 65 related studies: identification, screening, eligibility and data analysis. Of the 65 articles that have been reviewed, only 42 articles were finally selected for further analysis. Findings of the analysis revealed four main themes -the use of Instagram for research (two subthemes), to market products, as a basis for information tools (two subthemes) and for social activity during the pandemic. The ultimate presentation of this paper provides several recommendations with in-depth deliberation. Based on the review, it was found that Instagram was used for information search related to the COVID-19 pandemic and for research (particularly research related to the pandemic and online questionnaire distribution due to movement control order restriction to conduct fieldwork manually). Furthermore, Instagram was used to market

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“…The other huge social platform of our time is undoubtedly Instagram. In [21], the authors examined the Instagram entries of three major vaccine manufacturers. In the comments under the posts of these companies, the users' intention to comment was mainly to make general statements, communicate facts, and share experiences, which in this context, meant their post-vaccination experience.…”

Section: Sentiment Analysis In Covid-19mentioning

confidence: 99%

Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

Nemes

Kiss

2021

Applied Sciences

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Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the ’Bidirectional encoder representations from transformers’ (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseline and compare the results with the RNN, NLTK and TextBlob sentiment analyses. The RNN results are significantly closer to the benchmark results given by BERT, both models are able to categorize all tweets without a single tweet fall into the neutral category. Then, via a deeper analysis of these results, we can get an even more concise picture of people’s emotional state in the given period of time. The data from these analyses further support the emotional categories, and provide a deeper understanding that can provide a solid starting point for other disciplines as well, such as linguistics or psychology. Thus, the sentiment analysis, supplemented with information extraction and named entity recognition analyses, can provide a supported and deeply explored picture of specific sentiment categories and user attitudes.

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Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic

Cited by 22 publications

References 43 publications

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Do Not Come Near Me, Mrs Rona is here: A Systematic Review on the Use of Instagram during COVID-19

Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

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