Comparing Public Sentiment towards COVID-19 Vaccines across Canadian Cities: Analysis of Comments on Reddit (Preprint)

2021 ◽  
Author(s):  
Cathy Yan ◽  
Melanie Law ◽  
Stephanie Nguyen ◽  
Janelle Cheung ◽  
Jude Kong
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Latent Dirichlet Allocation ◽  
Local Level ◽  
The Other ◽  
Vaccine Uptake ◽  
Local Culture ◽  
Random Forest Regression ◽  
Country Level ◽  
Public Sentiment

BACKGROUND Social media enables the rapid consumption of news related to COVID-19, and serves as a platform for discussions. Its richness in text-based data in the form of posts and comments allows researchers to identify popular topics and assess public sentiment. Yet, the vast majority of this type of work is done on the platform or country level, and does not account for local culture and policies. OBJECTIVE The aim of this study is to use location-based subreddits on Reddit to study city-level variations in sentiments towards vaccine-related topics. METHODS Comments made on posts providing regular updates on COVID-19 statistics in the Vancouver, Toronto, and Calgary subreddits (r/vancouver, r/toronto, r/calgary) between July 13th, 2020 and June 14th, 2021 were extracted (N = 49,291, 20,764, and 21,277, respectively). Latent Dirichlet allocation was used to identify frequently discussed topics. Sentiment (joy, sadness, fear, and anger) scores were assigned to comments using random forest regression. RESULTS Beginning by focusing on the Vancouver subreddit, the number of comments made on each post positively correlated with the number of new cases (P < 0.001). From the comments, thirteen topics were identified. Two were related to vaccines, one regarding vaccine uptake and the other about vaccine supply. The levels of discussion for both topics were linked to the total number of vaccinations given (Granger test for causality: P < 0.001). Comments pertaining to either topic displayed higher scores for joy compared to comments about other topics (P < 0.001). Calgary and Toronto also discussed vaccine uptake. Sentiment scores for this topic differed across the three cities (P < 0.001). CONCLUSIONS Overall, our work demonstrates that comments from Reddit can be used to better understand concerns and sentiments surrounding the pandemic at the local level, which enables more targeted and publicly-acceptable policies.

scholarly journals Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 153
Author(s):  
Eva Melišová ◽  
Adam Vizina ◽  
Martin Hanel ◽  
Petr Pavlík ◽  
Petra Šuhájková
Keyword(s):  
Czech Republic ◽  
Random Forest ◽  
Linear Regression ◽  
Statistical Methods ◽  
Stepwise Regression ◽  
The Other ◽  
Water Reservoirs ◽  
Random Forest Regression ◽  
Local Conditions ◽  
The Difference

Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic.

scholarly journals Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China

2019 ◽  
Vol 8 (4) ◽  
pp. 185 ◽  
Author(s):  
Xuehua Han ◽  
Juanle Wang
Keyword(s):  
Social Media ◽  
Spatial Distribution ◽  
Latent Dirichlet Allocation ◽  
Developmental Stages ◽  
Classification Model ◽  
Economic Losses ◽  
Public Servants ◽  
Public Sentiment ◽  
South Central ◽  
The Government

Social media has been applied to all natural disaster risk-reduction phases, including pre-warning, response, and recovery. However, using it to accurately acquire and reveal public sentiment during a disaster still presents a significant challenge. To explore public sentiment in depth during a disaster, this study analyzed Sina-Weibo (Weibo) texts in terms of space, time, and content related to the 2018 Shouguang flood, which caused casualties and economic losses, arousing widespread public concern in China. The temporal changes within six-hour intervals and spatial distribution on sub-district and city levels of flood-related Weibo were analyzed. Based on the Latent Dirichlet Allocation (LDA) model and the Random Forest (RF) algorithm, a topic extraction and classification model was built to hierarchically identify six flood-relevant topics and nine types of public sentiment responses in Weibo texts. The majority of Weibo texts about the Shouguang flood were related to “public sentiment”, among which “questioning the government and media” was the most commonly expressed. The Weibo text numbers varied over time for different topics and sentiments that corresponded to the different developmental stages of the flood. On a sub-district level, the spatial distribution of flood-relevant Weibo was mainly concentrated in high population areas in the south-central and eastern parts of Shouguang, near the river and the downtown area. At the city level, the Weibo texts were mainly distributed in Beijing and cities in the Shandong Province, centering in Weifang City. The results indicated that the classification model developed in this study was accurate and viable for analyzing social media texts during a disaster. The findings can be used to help researchers, public servants, and officials to better understand public sentiments towards disaster events, to accelerate disaster responses, and to support post-disaster management.

scholarly journals A Novel Framework for Mining Social Media Data Based on Text Mining, Topic Modeling, Random Forest, and DANP Methods

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2041
Author(s):  
Chi-Yo Huang ◽  
Chia-Lee Yang ◽  
Yi-Hao Hsiao
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Random Forest ◽  
Social Theory ◽  
Latent Dirichlet Allocation ◽  
Theoretical Framework ◽  
Causal Relationships ◽  
Social Media Data ◽  
Feature Importance ◽  
Media Data

The huge volume of user-generated data on social media is the result of the aggregation of users’ personal backgrounds, past experiences, and daily activities. This huge size of the generated data, the so-called “big data,” has been studied and investigated intensively during the past few years. In spite of the impression one may get from the media, a great deal of data processing has not been uncovered by existing techniques of data engineering and processing. However, very few scholars have tried to do so, especially from the perspective of multiple-criteria decision-making (MCDM). These MCDM methods can derive influence relationships and weights associated with aspects and criteria, which can hardly be achieved by traditional data analytics and statistical approaches. Therefore, in this paper, we aim to propose an analytic framework to mine social networks, feed the meaningful information via MCDM methods based on a theoretical framework, derive causal relationships among the aspects of the theoretical framework, and finally compare the causal relationships with a social theory. Latent Dirichlet allocation (LDA) will be adopted to derive topic models based on the data retrieved from social media. By clustering the topics into aspects of the social theory, the probability associated with each aspect will be normalized and then transformed to a Likert-type 5-point scale. Afterwards, for every topic, the feature importance of all other topics will be derived using the random forest (RF) algorithm. The feature importance matrix will be transformed to the initial influence matrix of the decision-making trial and evaluation laboratory (DEMATEL). The influence relationships among the aspects and criteria and influence weights can then be derived by using the DEMATEL-based analytic network process (DANP). The influence weight versus each criterion can be derived by using DANP. To verify the feasibility of the proposed framework, Taiwanese users’ attitudes toward air pollution will be analyzed based on the value–belief–norm (VBN) theory by using social media data retrieved from Dcard (dcard.tw). Based on the analytic results, the causal relationships are fully consistent with the VBN framework. Further, the mutual influences derived in this work that were seldom discussed by earlier works, i.e., the mutual influences between altruistic concerns and egoistic concerns, as well as those between altruistic concerns and biosphere concerns, are worth further investigation in future.

scholarly journals Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea

2021 ◽  
Vol 18 (12) ◽  
pp. 6549
Author(s):  
Jae-Geum Shim ◽  
Kyoung-Ho Ryu ◽  
Sung Hyun Lee ◽  
Eun-Ah Cho ◽  
Yoon Ju Lee ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Time Course ◽  
Latent Dirichlet Allocation ◽  
Allocation Model ◽  
Public Response ◽  
Latent Dirichlet Allocation Model ◽  
Public Sentiment ◽  
Before And After

The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people’s lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for “coronavirus” and “vaccines” as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public’s anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.

scholarly journals Social Media Sharing of Articles About Measles in a European Context: Text Analysis Study (Preprint)

2021 ◽  
Author(s):  
Dominik Wawrzuta ◽  
Mariusz Jaworski ◽  
Joanna Gotlib ◽  
Mariusz Panczyk
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Scientific Information ◽  
Vaccine Uptake ◽  
Vaccine Hesitancy ◽  
Effective Vaccine ◽  
Data Set ◽  
Vaccination Rates ◽  
Nature Of Information ◽  
Social Media Platforms

BACKGROUND Despite the existence of an effective vaccine, measles still threatens the health and lives of many Europeans. Notably, during the COVID-19 pandemic, measles vaccine uptake declined; as a result, after the pandemic, European countries will have to increase vaccination rates to restore the extent of vaccination coverage among the population. Because information obtained from social media are one of the main causes of vaccine hesitancy, knowledge of the nature of information pertaining to measles that is shared on social media may help create educational campaigns. OBJECTIVE In this study, we aim to define the characteristics of European news about measles shared on social media platforms (ie, Facebook, Twitter, and Pinterest) from 2017 to 2019. METHODS We downloaded and translated (into English) 10,305 articles on measles published in European Union countries. Using latent Dirichlet allocation, we identified main topics and estimated the sentiments expressed in these articles. Furthermore, we used linear regression to determine factors related to the number of times a given article was shared on social media. RESULTS We found that, in most European social media posts, measles is only discussed in the context of local European events. Articles containing educational information and describing world outbreaks appeared less frequently. The most common emotions identified from the study’s news data set were fear and trust. Yet, it was found that readers were more likely to share information on educational topics and the situation in Germany, Ukraine, Italy, and Samoa. A high amount of anger, joy, and sadness expressed within the text was also associated with a higher number of shares. CONCLUSIONS We identified which features of news articles were related to increased social media shares. We found that social media users prefer sharing educational news to sharing informational news. Appropriate emotional content can also increase the willingness of social media users to share an article. Effective media content that promotes measles vaccinations should contain educational or scientific information, as well as specific emotions (such as anger, joy, or sadness). Articles with this type of content may offer the best chance of disseminating vital messages to a broad social media audience.

Pengaruh Unsur Budaya Lokal dalam Ungkapan Berbahasa Perancis

Metahumaniora ◽  
2017 ◽  
Vol 7 (3) ◽  
pp. 378
Author(s):  
Vincentia Tri Handayani
Keyword(s):  
Mental Representation ◽  
Cultural Context ◽  
Oral Tradition ◽  
Minimal Element ◽  
The Other ◽  
Local Culture ◽  
Community Groups ◽  
Cultural Community ◽  
The Difference ◽  
Complex Words

AbstrakFolklor yang menghasilkan tradisi lisan merupakan perwujudan budaya yang lahirdari pengalaman kelompok masyarakat. Salah satu bentuk tradisi lisan adalah ungkapan yangmengandung unsur budaya lokal dalam konstruksinya yang tidak dimiliki budaya lainnya.Ungkapan idiomatis memberikan warna pada bahasa melalui penggambaran mental. Dalambahasa Perancis, ungkapan dapat berupa locution dan expression. Perbedaan motif acuansuatu ungkapan dapat terlihat dari pengaruh budaya masyarakat pengguna bahasa. Sebuahleksem tidak selalu didefinisikan melalui unsur minimal, tidak juga melalui kata-kata,baik kata dasar atau kata kompleks, namun dapat melalui kata-kata beku yang maknanyatetap. Hubungan analogis dari makna tambahan yang ada pada suatu leksem muncul dariidentifikasi semem yang sama. Semem tersebut mengarah pada term yang diasosiasikan danyang diperkaya melalui konteks (dalam ungkapan berhubungan dengan konteks budaya).Kata kunci: folklor, ungkapan, struktur, makna idiomatis, kebudayaanAbstractFolklore which produces the oral tradition is a cultural manifestation born out theexperience of community groups. One form of the oral tradition is a phrase that containsthe elements of local culture in its construction that is not owned the other culture. Theidiomatic phrase gives the color to the language through the mental representation. InFrench, the expression can consist of locution and expression. The difference motivesreference of an expression can be seen from the influence of the cultural community thelanguage users. A lexeme is not always defined through a minimal element, nor throughwords, either basic or complex words, but can be through the frost words whose meaningsare fixed. The analogical connection of the additional meanings is on a lexeme arises fromthe identification of the same meaning. The meaning ‘semem’ leads to the associated termsand which are enriched through the context (in idiom related to the cultural context).Keywords : folklore, idioms, structure, idiom meaning, cultureI PENDAHULUAN

Personal Discernment and Dialogue. Learning from ‘the Other’

2020 ◽  
Vol 12 (4) ◽  
pp. 27
Author(s):  
Michael Barnes SJ
Keyword(s):  
The Other ◽  
Local Culture ◽  
Society Of Jesus ◽  
Spiritual Exercises ◽  
The South ◽  
Ignatian Spirituality ◽  
Michel De Certeau ◽  
Personal Interactions ◽  
Ignatius Of Loyola

This article considers the theme of discernment in the tradition of Ignatian spirituality emanating from the Spiritual Exercises of St Ignatius of Loyola (1491-1556), the founder of the Society of Jesus (Jesuits). After a brief introduction which addresses the central problematic of bad influences that manifest themselves as good, the article turns to the life and work of two Jesuits, the 16th C English missionary to India, Thomas Stephens and the 20th C French historian and cultural critic, Michel de Certeau. Both kept up a constant dialogue with local culture in which they sought authenticity in their response to ‘events’, whether a hideous massacre which shaped the pastoral commitment and writing of Stephens in the south of the Portuguese enclave of Goa or the 1968 student-led protests in Paris that so much affected the thinking of de Certeau. Very different in terms of personal background and contemporary experience, they both share in a tradition of discernment as a virtuous response to what both would understand as the ‘wisdom of the Spirit’ revealed in their personal interactions with ‘the other’.

Covid-19 – rumours and facts in media

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 171-174
Author(s):  
Tarare Toshida ◽  
Chaple Jagruti
Keyword(s):  
Social Media ◽  
Preventive Measures ◽  
Vital Role ◽  
The Other ◽  
Sources Of Information ◽  
Other Hand ◽  
Corona Virus ◽  
The People ◽  
The World

The covid-19 resulted in broad range of spread throughout the world in which India has also became a prey of it and in this situation the means of media is extensively inϑluencing the mentality of the people. Media always played a role of loop between society and sources of information. In this epidemic also media is playing a vital role in shaping the reaction in ϑirst place for both good and ill by providing important facts regarding symptoms of Corona virus, preventive measures against the virus and also how to deal with any suspect of disease to overcome covid-19. On the other hand, there are endless people who spread endless rumours overs social media and are adversely affecting life of people but we always count on media because they provide us with valuable answers to our questions, facts and everything in need. Media always remains on top of the line when it comes to stop the out spread of rumours which are surely dangerous kind of information for society. So on our side we should react fairly and maturely to handle the situation to keep it in the favour of humanity and help government not only to ϑight this pandemic but also the info emic.

Industrial Technology Graduates’ Acquired Skills and Competencies from the University

2019 ◽  
Vol 5 (3) ◽  
pp. 189
Author(s):  
Amado C Gequinto ◽  
Do Mads
Keyword(s):  
Social Media ◽  
Text Messaging ◽  
Global Market ◽  
The Other ◽  
Global Markets ◽  
Industrial Technology ◽  
The University ◽  
Descriptive Method

Skills and competencies are highly regarded in todays global market. Different agencies specifically those seeking for  technologists, technicians, and engineers, have stressed out that skills and competencies as major components  for individual workers.  This aimed to determine  the relevance and appropriateness of acquired skills and competencies by industrial technology graduates, and determine the extent of use of skills and competencies in the current employment. Review of related literatures and studies have been considered in the realization, understanding, analysis, and interpretation of this research exploration. A descriptive method of research was used with 78 graduates from 2015-2016 and 117 graduates from 2016-2017, who participated in the study survey process. The BatStateU Standardized Questionnaire was used to gather data. A brief interview and talk during the visit of alumni in the university was also considered, as well as the other means of social media like email, facebook, messenger, and text messaging.   Results show that skills and competecnices acquired by industrial technology graduates are all relevant and appropriate.  The study also found that there is some to great extent use of acquired skills and competencies to their current employment. The study implies that the acquired skills and competencies from the university significantly provided the graduates the opportunities ins the national and global markets and industries.

Nglyc: A Random Forest Method for Prediction of N-Glycosylation Sites in Eukaryotic Protein Sequence

2020 ◽  
Vol 27 (3) ◽  
pp. 178-186 ◽  
Author(s):  
Ganesan Pugalenthi ◽  
Varadharaju Nithya ◽  
Kuo-Chen Chou ◽  
Govindaraju Archunan
Keyword(s):  
Random Forest ◽  
Protein Sequence ◽  
Glycosylation Site ◽  
Computational Method ◽  
The Other ◽  
Eukaryotic Protein ◽  
Random Forest Method ◽  
Glycosylation Sites ◽  
Human And Mouse ◽  
Better Than

Background: N-Glycosylation is one of the most important post-translational mechanisms in eukaryotes. N-glycosylation predominantly occurs in N-X-[S/T] sequon where X is any amino acid other than proline. However, not all N-X-[S/T] sequons in proteins are glycosylated. Therefore, accurate prediction of N-glycosylation sites is essential to understand Nglycosylation mechanism. Objective: In this article, our motivation is to develop a computational method to predict Nglycosylation sites in eukaryotic protein sequences. Methods: In this article, we report a random forest method, Nglyc, to predict N-glycosylation site from protein sequence, using 315 sequence features. The method was trained using a dataset of 600 N-glycosylation sites and 600 non-glycosylation sites and tested on the dataset containing 295 Nglycosylation sites and 253 non-glycosylation sites. Nglyc prediction was compared with NetNGlyc, EnsembleGly and GPP methods. Further, the performance of Nglyc was evaluated using human and mouse N-glycosylation sites. Results: Nglyc method achieved an overall training accuracy of 0.8033 with all 315 features. Performance comparison with NetNGlyc, EnsembleGly and GPP methods shows that Nglyc performs better than the other methods with high sensitivity and specificity rate. Conclusion: Our method achieved an overall accuracy of 0.8248 with 0.8305 sensitivity and 0.8182 specificity. Comparison study shows that our method performs better than the other methods. Applicability and success of our method was further evaluated using human and mouse N-glycosylation sites. Nglyc method is freely available at https://github.com/bioinformaticsML/ Ngly.

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