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Summary of Big Data Analysis on Canadian News
about the Covid-19 Toward Educational Impact
ABSTRACT
A Covid-19 has changed many things in our daily life for over 23 months of period since it has spread
over Canada. One of the major changes in the world as well as Canada is the educational impact due to
public health problem. People are isolated and fear has spread over educational systems with more
concentration on public health issues. The purpose of this summary is to investigate text mining analysis
of current headlines for Canadian News of Covid-19 on educational impact. Methods used for the text
mining include wordcloud, association rule and LDA topic modeling based on the Gibbs sampling.
Keywords: wordcloud, LDA topic modelling, Covid-19, Canada, text mining
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INTRODUCTION
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The COVID-19 has posted one of the most challenging situations for the academics as well
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as the students all around the world. Students being the biggest stake holders of the education
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process, their challenges during this pandemic become even more pivotal. These challenges vary in
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intensity and type depending on multiple factors. In this paper, we analyze and document the
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challenges particular to the issues and keyword of Covid-19 on Canadian educational systems as a
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public health issue to document with. An analysis is performed on news headlines and keywords by
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tokenizing, to analyze various factors affecting the statistical analysis during COVID-19. The results
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of the graph analysis showed that a relationship between covid-19 and government as well as
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educational lock down during 2021.
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The World Health Organization (WHO) in their media briefing on the 11th March, 2020 declared the spread
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of the contagious coronavirus disease, also referred to as COVID-19 as a pandemic. COVID-19 stands for
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coronavirus disease and even referred to as the 2019 novel coronavirus or ‘2019-nCoV’ (Bender, 2020).
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The COVID-19 virus is linked to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV) that
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similarly can be as fatal (Meng, Hua & Bian, 2020). This new virus can be transmitted just in minutes
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through droplets or even touching surface metals or other materials which have been infected from a
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person who has respiratory problems. Even though the elderly and the very young children are easily
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affected, nobody is immune to this new infectious disease once it hits the body, so all people are
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susceptible to its devastating effects (Meng, Hua & Bian, 2020)
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Since social distancing was a key recommendation to check the spread of COVID-19, the
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educational institutions across the world were suddenly faced by an unprecedented challenge of
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continuing to provide some form of educational assistance to students despite the pandemics (Jeanne,
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Leonie & Parlo, 2020). Furthermore, the response of the institutions globally should not only address the
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challenges related to educational needs, but to also think about economic and sustainability factors. The
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emerging responses varied depending on level of education, demographics, infrastructure facilities and
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location wise impact of the pandemic (Mulenga, Marbán, 2020; Dhawan, 2020).
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However, most of the educational systems decided to rely on some form of technology as a
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substitute modality. In 2020, OECD Rapid Response report by Reimers and Schleicher surveyed 330
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respondents across 98 countries about their educational responses to COVID-19. It was interesting to see
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many countries like Finland, Argentina, Russia (Radio and Television); Austria, Belgium, Costa Rica, Croatia,
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Czech Republic, Georgia, Indonesia, Israel, Latvia, Malaysia, Romania, and Saudi Arabia (Television) using
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radio and television media to supplement and support online learning or learning at home.
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Reports conflict with how quickly the higher education sector responded. While some reported
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rapid transition by 2 February (Leung & Sharma, 2020); others reported the transition did not occur until
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mid to late February (McKenzie, 2020). As observed in their report Global survey report by International
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Association of Universities (IAU, 2020), at almost all higher education institutes, a COVID-19 affected
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teaching and learning, with two-thirds of them reporting that the classroom teaching has been replaced
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by distance teaching and learning. Distance teaching is broadly categorized as Online learning, blended
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learning and Remote learning. Given the nature of the pandemic, the distance teaching categories
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adapted during this period have been mainly online learning and remote learning. Although, quite often
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the terms online learning and distance learning are used interchangeably, as believed by majority of
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educationalists, we define online learning as the one involving the delivery of the course/program
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material through some form of digital device utilizing the internet. On the other hand, we identify the
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creation of online virtual synchronous classrooms as remote learning.
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In 2020, OECD Rapid Response report by Reimers and Schleicher pointed out that “The domains
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for which most people considered that an education response involved the most challenges were the
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availability of technological infrastructure, addressing student emotional health, addressing the right
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balance between digital and screen free activities and managing the technological infrastructure.”
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The other challenge faced by the institutions was the readiness and the preparedness of the public
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health issues and the students towards the entire distance teaching process. As Bao (2020) pointed out
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that it was a massive, disruptive shift to move all the existing courses to an online environment in a matter
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of days. In this paper, we try to dwell deeper into these issues, particularly for higher education. This was
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achieved by conducting an analysis on news headlines that represent concurrent issues during the COVID-
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19 pandemic. The result of the keywords highlighted the important factors vital for public health issue
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during pandemics. The documentation of such results can prove helpful insight, as we end with online
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higher education. These results can also be beneficial in the event of any future sudden disruption of
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higher education due to unprecedented circumstances.
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METHODS
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PART 1. WEB SCRAWLING
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1. Steps for crawling Canada news of covid-19 about relevant topics of an education. First, in the Yahoo
website Canada, search for the relevant topics, ‘Covid education’.
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2. Second, using html source code, analyze the relevant code for headlines which represents the
interest of content for the impact of the Covid-19 toward the education.
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3. Third, using program
, web crawl the data of 50 pages in total of 260 news headlines.
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The frequency analysis of new headlines, wordcloud analysis (macroscopic outline )
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Tokenized words of the headlines are made into a frequency table. The R output shows the table of 50
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most often used words for headlines of 507 news (combination of first and second crawling), in search
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for ‘covid education’ of Canadian news.
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PART 2. WORDCLOUD VISUALIZATION
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Wordcloud (also known as tag clouds or word art) are used to visualize and summarize all sorts of data,
from feedback to academic papers, and everything in between.
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There are many word cloud generators to choose from, each one with its own unique design and
customization capabilities.
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Those adverbs (‘to’, ‘in’, ‘for’, ‘of’, ‘and’, ‘on’, and ‘as’) of covid-19 virus subject is eliminated for
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unnecessary details and only used the commonly used words for further investigation in the section of
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topic modeling.
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PART 3. WORD CONNECTION (VISUALIZATION)
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SNA measures and maps the flow of relationships and relationship changes between knowledge-
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possessing entities. Simple and complex entities include websites, computers, animals, humans, groups,
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Social network analysis (SNA) is a process of quantitative and qualitative analysis of a social network.
organizations and nations.
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A SNA (social network analysis) for intersected words (microscopic outline).
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Based on the bags of words extracted, an analysis of words are made for the intersected usage of words.
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PART 4. TOPIC MODELING (LDA: LATENT DIRICHLET ALLOCATION ANALYSIS)
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important events in history by analyzing text based on year. Web based libraries can use LDA
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to recommend books based on your past readings. News providers can use topic modelling to understand
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articles quickly or cluster similar articles.
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What are some of the real world uses topic modelling has? Historians can use LDA to identify
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A tool and technique for topic modeling, Latent Dirichlet Allocation (LDA) classifies or
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categorizes the text into a document and the words per topic, these are modeled based on the Dirichlet
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distributions and processes.
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The LDA makes two key assumptions:
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1. Documents are a mixture of topics, and
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2. Topics are a mixture of tokens (or words)
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And, these topics using the probability distribution generate the words. In statistical language, the
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documents are known as the probability density (or distribution) of topics and the topics are the
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probability density (or distribution) of words.
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The end goal of LDA is to find the most optimal representation of the Document-Topic matrix and the
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Topic-Word matrix to find the most optimized Document-Topic distribution and Topic-Word
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distribution.
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As LDA assumes that documents are a mixture of topics and topics are a mixture of words so LDA
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backtracks from the document level to identify which topics would have generated these documents
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and which words would have generated those topics.
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RESULTS
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some statistically proven significant factors discussed below which need to be either maintained or
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improved in any future public health issues and spread of respiratory disease.
The overall results showed general news keywords analysis. This words connection and ideas comes from
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WORDCLOUD VISUALIZATION 1
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adverbs. It is easy to find the significant words related to ‘covid’, and ‘education’ efficiently with the size
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of words in the graph. Now, some inferences are found from the result of the wordcloud.
The R wordcloud shows statistical significant throughout the frequencies of words, with deletion of
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Some words, including ‘pandemic’, ‘season’, ‘schools’, ‘online’, ‘lockdown’, ‘closed’ and ‘students’
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shows the educational perspective of covid-19 impact in Canada. Then, the words including ‘Ontario’
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and ‘B.C’ shows the locational importance of interest.
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Also, it shows current terror of the ‘shooting’ in ‘Nova’, ’Soctia’, ‘funeral’, and ‘victims’ in which shows
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the related people’s interest towards the covid-19 pandemic situation.
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The interest is given towards words that people are attracted, including ‘mass’, ‘measures’, ‘ramping’,
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‘says’, ‘news’, and ‘season’.
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It is also inferences that ‘cases’ and ‘week’ ‘during’ and ‘amid’ shows the periodic intervals of covid-19
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impact, which comes from cognitive understandings of people.
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In these words, news reflects the trends and ideas of people ‘how’ influential covid-19 pandemic is, as
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the mass media does for people in terms of understanding current situation.
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WORD CONNECTION VISUALIZATION
SNA (social network analysis) for intersected words (microscopic outline)
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Based on the bags of words extracted, an analysis of words are made for the intersected usage of words.
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• The top 10 words are found from within connections by the sentences of headlines, and this is made
into matrix.
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• This is cross checked with the lift and the support of cross table (matrix) formed by the ‘apriori’
package.
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The
simply the ratio of these values: target response divided by average response.
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Hence, if the lift is high, then it means the co-occurrence of A and B means higher chance of A and B
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occurring together.
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Also, the
. The purpose to find how connect words are in writing headlines can be
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confirmed with frequencies of co-occurrence and the lift and support.
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• Then, this is formed into a graph of network which shows the connections of words as a
representative of most used headlines.
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The analysis is based on common usage of words in headlines for two words. The words commonly
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shows up include ‘Ontario’, ‘new’, ’lockdown’, ‘B.C’, ’cases’, ‘schools’, ‘may’ and ‘students’, ‘week’.
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From the heatmap, it is shown that the covid has high correlation with adverbs.
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Hierarchal clustering analysis based on the lift of the matrix. This let us understand the similar group of
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words and connect with different group of clustered words in a stepwise graph, which provides a
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structural understanding of the clustered words.
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More evidently, even though there should be statistical justification for how number of clusters are
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determined, the graph clearly shows the clusters of covid-19 with adverbs and related words of an
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education. The R network graph below shows the ultimate ranking relationships of words in the
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headlines. The words are based on the correlation frequencies and relation being close enough is
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determined by the hierarchical clustering and the probability of the support.
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First, people are interested in the news because of the social distanced as ‘news’ of populated areas
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‘Ontario’, ‘B.C’ where the ‘measures’ is related.
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Also, the decision of the decision of the ‘minister’ and action of government is relative to students
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current situation of covid-19.
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There are some unique search analysis of relationship that covid-19 is strongly related to ‘cases’ of
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‘new’ individuals, and the object which is ‘students’ is relevant to ‘online’ learning and ‘lockdown’ of
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‘schools’ also mattered.
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TOPIC MODELING (LDA): 5 TOPICS, WITH 20 KEYWORDS
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of topics, under the common topic of ‘covid-19’, are deduced from the two results by the Gibbs
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sampling. It could be summarized into 3 different topics. First, student receiving online teaching and are
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sensitive news is noticed as coherent to previous analysis.
Although it would be added to show most likely 5 topics are adequate for the choosing topics, the table
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Second, the schools related to education of during May lockdown is popular topic as the reality comes
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true.
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Third, terror is still related to covid-19 of pandemic situation as people are feared of shooting and death
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which is new as it is a reflection that people are afraid of.
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From the LDA topic modeling, it is confirmed that the topics are very easily noticed with relative words
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that are summarized. It confirms the relation of previous analysis and combines with data sampling
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method to fit appropriate words together.
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DISCUSSION
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- Some of headlines are duplicate and has same headlines to other headlines.
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- Limited data for query access for the headlines is solved through second crawling of the data.
Problem and solutions:
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CONCLUSION
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education related to each provinces in Canada for better understanding for the future. What was achieved
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was the distinction of the significant factors in terms of public health matters from the higher education
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institutions. However, apart from this, it is well known for the importance of public health and issues
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during pandemic period. During the pandemics, students and many people often have problems such as
The main aim of the study was to study and document the keywords and topics of this pandemic in higher
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lack of interaction, communication to be relied for public news, or concurrent issues when they are self-
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isolated at home. Ensuring successful online education requires techniques to be implemented by public
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health communities to minimize negative public heath impact of students and encourage isolation and
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reliability in news of public media. The following 3 key points summarize the overall statistical analysis of
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methods used.
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Finally, with 3 methods of analysis, education impact of covid-19 pandemic could be understood as
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follows:
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Online learning, where schools are lockdown during May:
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From this fact, the keyword of during and up to shows people curiosity of minister’s decisions and what
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news says about it.
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New cases and death of terror in Nova Scotia:
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Although these keywords are related to more general understandings of covid-19 pandemic situation,
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mass media and people are feared of terror as duration of social-distancing becomes longer.
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Students, locational importance on Ontario, B.C news
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As for the populated areas, university students and kids are sensitive to news of local areas, especially
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educational impact on students. There are lots of schools and colleges located in B.C and Ontario and
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the keywords of topics reflect this fact of popular interest.
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FIGURES AND TABLES
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Figure 1: Steps for crawling Canada news of covid-19 about relevant topics of an education. First, in the
Yahoo website Canada, search for the relevant topics, ‘Covid education’.
Figure 2. Using html source code, analyze the relevant code for headlines which represents the interest
of content for the impact of the Covid-19 toward the education.
Figure 3. using program
, web crawl the data of 50 pages in total of 260 news headlines.
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Figure 4. The R wordcloud shows statistical significant throughout the frequencies of words, with
deletion of adverbs.
Figure 5. From the heatmap, it is shown that the covid has high correlation with adverbs.
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Figure 6. Hierarchal clustering analysis based on the lift of the matrix. This let us understand the similar
group of words and connect with different group of clustered words in a stepwise graph, which provides
a structural understanding of the clustered words.
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Figure 7. The R graph below shows the ultimate ranking relationships of words in the headlines. The
words are based on the correlation frequencies and relation being close enough is determined by the
hierarchical clustering and the probability of the support.
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Table 1: Most likely 5 topics are adequate for the choosing topics, the table of topics, under the
common topic of ‘covid-19’, are deduced from the two results by the Gibbs sampling.
student
schools
shooting
online
during
death
cases
lockdown
new
news
pandemic
Nova Scotia
education
May
funerals
B.C
up to
says