scholarly journals Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach

10.2196/24585 ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e24585
Author(s):  
Tiago de Melo ◽  
Carlos M S Figueiredo
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
News Media ◽  
Topic Modeling ◽  
Entity Recognition ◽  
Political Polarization ◽  
Important Approach ◽  
Topic Sentiment ◽  
The Impact ◽  
Over Time

Background The COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet. Objective The purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil. Methods This work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts. Results In this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic. Conclusions This study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities.

scholarly journals Comparing News and Tweets about COVID-19 in Brazil (Preprint)

2020 ◽  
Author(s):  
Tiago de Melo ◽  
Carlos M. S. Figueiredo
Keyword(s):  
Sentiment Analysis ◽  
News Media ◽  
Political Polarization ◽  
Internet Media ◽  
High Incidence ◽  
The World ◽  
Important Approach ◽  
Topic Sentiment ◽  
Negative Sentiment ◽  
Over Time

BACKGROUND COVID-19 pandemic is severely affecting people all over the world. Nowadays, an important approach to understand such a phenomenon and its impacts on the lives of people consists of monitoring social networks and news on Internet. OBJECTIVE COVID-19 pandemic is severely affecting people all over the world. Nowadays, an important approach to understand such a phenomenon and its impacts on the lives of people consists of monitoring social networks and news on Internet. METHODS This work proposes a methodology based on topic modeling, named entity recognition and sentiment analysis of the text to compare Twitter posts and news, followed by envision of COVID evolution and impacts. We have focused on an analysis in Brazil, one important epicenter of the pandemic in the world, so we have faced the challenge to deal with Brazilian Portuguese texts. RESULTS This work collected and analysed 18,413 articles from news media, and 1,597,934 tweets posted by 1,299,084 users in Brazil. Results show that the proposed methodology improved the topic-sentiment analysis over time, so a better monitoring of Internet media is allowed. Besides, with this tool, we extracted some interesting insights about COVID evolution in Brazil. For instance, we found out that Twitter presents similar topic coverage from news media, the main entities are similar, but they differ in theme distribution and entity diversity. Besides, some aspects represent a negative sentiment of political theme from both media, and a high incidence of mentions to a specific drug denotes a high political polarization of the pandemic. CONCLUSIONS This work collected and analysed 18,413 articles from news media, and 1,597,934 tweets posted by 1,299,084 users in Brazil. Results show that the proposed methodology improved the topic-sentiment analysis over time, so a better monitoring of Internet media is allowed. Besides, with this tool, we extracted some interesting insights about COVID evolution in Brazil. For instance, we found out that Twitter presents similar topic coverage from news media, the main entities are similar, but they differ in theme distribution and entity diversity. Besides, some aspects represent a negative sentiment of political theme from both media, and a high incidence of mentions to a specific drug denotes a high political polarization of the pandemic.

scholarly journals Using Social Media Data to Understand the Impact of Promotional Information on Laypeople�s Discussions: A Case Study of Lynch Syndrome (Preprint)

2017 ◽  
Author(s):  
Jiang Bian ◽  
Yunpeng Zhao ◽  
Ramzi G Salloum ◽  
Yi Guo ◽  
Mo Wang ◽  
...  
Keyword(s):  
Social Media ◽  
Colon Cancer ◽  
Genetic Testing ◽  
Lynch Syndrome ◽  
Sentiment Analysis ◽  
Health Information ◽  
Topic Modeling ◽  
Cancer Awareness ◽  
Related Information ◽  
The Impact

BACKGROUND Social media is being used by various stakeholders among pharmaceutical companies, government agencies, health care organizations, professionals, and news media as a way of engaging audiences to raise disease awareness and ultimately to improve public health. Nevertheless, it is unclear what effects this health information has on laypeople. OBJECTIVE This study aimed to provide a detailed examination of how promotional health information related to Lynch syndrome impacts laypeople’s discussions on a social media platform (Twitter) in terms of topic awareness and attitudes. METHODS We used topic modeling and sentiment analysis techniques on Lynch syndrome–related tweets to answer the following research questions (RQs): (1) what are the most discussed topics in Lynch syndrome–related tweets?; (2) how promotional Lynch syndrome–related information on Twitter affects laypeople’s discussions?; and (3) what impact do the Lynch syndrome awareness activities in the Colon Cancer Awareness Month and Lynch Syndrome Awareness Day have on laypeople’s discussions and their attitudes? In particular, we used a set of keywords to collect Lynch syndrome–related tweets from October 26, 2016 to August 11, 2017 (289 days) through the Twitter public search application programming interface (API). We experimented with two different classification methods to categorize tweets into the following three classes: (1) irrelevant, (2) promotional health information, and (3) laypeople’s discussions. We applied a topic modeling method to discover the themes in these Lynch syndrome–related tweets and conducted sentiment analysis on each layperson’s tweet to gauge the writer’s attitude (ie, positive, negative, and neutral) toward Lynch syndrome. The topic modeling and sentiment analysis results were elaborated to answer the three RQs. RESULTS Of all tweets (N=16,667), 87.38% (14,564/16,667) were related to Lynch syndrome. Of the Lynch syndrome–related tweets, 81.43% (11,860/14,564) were classified as promotional and 18.57% (2704/14,564) were classified as laypeople’s discussions. The most discussed themes were treatment (n=4080) and genetic testing (n=3073). We found that the topic distributions in laypeople’s discussions were similar to the distributions in promotional Lynch syndrome–related information. Furthermore, most people had a positive attitude when discussing Lynch syndrome. The proportion of negative tweets was 3.51%. Within each topic, treatment (16.67%) and genetic testing (5.60%) had more negative tweets compared with other topics. When comparing monthly trends, laypeople’s discussions had a strong correlation with promotional Lynch syndrome–related information on awareness (r=.98, P<.001), while there were moderate correlations on screening (r=.602, P=.05), genetic testing (r=.624, P=.04), treatment (r=.69, P=.02), and risk (r=.66, P=.03). We also discovered that the Colon Cancer Awareness Month (March 2017) and the Lynch Syndrome Awareness Day (March 22, 2017) had significant positive impacts on laypeople’s discussions and their attitudes. CONCLUSIONS There is evidence that participative social media platforms, namely Twitter, offer unique opportunities to inform cancer communication surveillance and to explore the mechanisms by which these new communication media affect individual health behavior and population health.

Gamifying the Media Classroom

2017 ◽  
Vol 7 (4) ◽  
pp. 22-49
Author(s):  
Katie Seaborn ◽  
Deborah I. Fels ◽  
Rob Bajko ◽  
Jaigris Hodson
Keyword(s):  
Social Media ◽  
Student Engagement ◽  
Conceptual Framework ◽  
Student Perspective ◽  
Educational Settings ◽  
The Media ◽  
Game Elements ◽  
The Impact ◽  
Over Time

Gamification, or the use of game elements in non-game contexts, has become a popular and increasingly accepted method of engaging learners in educational settings. However, there have been few comparisons of different kinds of courses and students, particularly in terms of discipline and content. Additionally, little work has reported on course instructor/designer perspectives. Finally, few studies on gamification have used a conceptual framework to assess the impact on student engagement. This paper reports on findings from evaluating two gamified multimedia and social media undergraduate courses over the course of one semester. Findings from applying a multidimensional framework suggest that the gamification approach taken was moderately effective for students overall, with some elements being more engaging than others in general and for each course over time." Post-term questionnaires posed to the instructors/course designers revealed congruence with the student perspective and several challenges pre- and post-implementation, despite the use of established rules for gamifying curricula.

scholarly journals Polarization in the contemporary political and media landscape

2020 ◽  
Author(s):  
Anne E Wilson ◽  
Victoria Parker ◽  
Matthew Feinberg
Keyword(s):  
Social Media ◽  
Political Polarization ◽  
Political Elites ◽  
Ideological Polarization ◽  
Societal Level ◽  
Polarization Phenomena ◽  
Partisan Media ◽  
Over Time

Political polarization is on the rise in America. Although social psychologists frequently study the intergroup underpinnings of polarization, they have traditionally had less to say about macro societal processes that contribute to its rise and fall. Recent cross-disciplinary work on the contemporary political and media landscape provides these complementary insights. In this paper, we consider the evidence for and implications of political polarization, distinguishing between ideological, affective, and false polarization. We review three key societal-level factors contributing to these polarization phenomena: the role of political elites, partisan media, and social media dynamics. We argue that institutional polarization processes (elites, media and social media) contribute to people’s misperceptions of division among the electorate, which in turn can contribute to a self-perpetuating cycle fueling animosity (affective polarization) and actual ideological polarization over time.

scholarly journals Análise de discursos em notícias sobre homofobia, racismo e sexismo em comentários de portais brasileiros de notícias

2021 ◽  
Author(s):  
Lucas Rodrigues ◽  
Antonio Jacob Junior ◽  
Fábio Lobato
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Data Visualization ◽  
Language Processing ◽  
Topic Modeling ◽  
Hate Speech ◽  
Psychological Impact ◽  
Internet Service ◽  
General Law

Posts with defamatory content or hate speech are constantly foundon social media. The results for readers are numerous, not restrictedonly to the psychological impact, but also to the growth of thissocial phenomenon. With the General Law on the Protection ofPersonal Data and the Marco Civil da Internet, service providersbecame responsible for the content in their platforms. Consideringthe importance of this issue, this paper aims to analyze the contentpublished (news and comments) on the G1 News Portal with techniquesbased on data visualization and Natural Language Processing,such as sentiment analysis and topic modeling. The results showthat even with most of the comments being neutral or negative andclassified or not as hate speech, the majority of them were acceptedby the users.

Impact of Social Media and Technology Companies on Digital Journalism

2019 ◽  
pp. 147-170
Author(s):  
Şükrü Oktay Kılıç ◽  
Zeynep Genel
Keyword(s):  
Social Media ◽  
News Media ◽  
Business Models ◽  
The Other ◽  
Media Companies ◽  
Digital Journalism ◽  
The World ◽  
Social Media Platforms ◽  
Media And Technology ◽  
The Impact

A handful of social media companies, with their shifting strategies to become hosts of all information available online, have significantly changed the news media landscape in recent years. Many news media companies across the world have gone through reorganizations in a bid to keep up with new storytelling techniques, technologies, and tools introduced by social media companies. With their non-transparent algorithms favoring particular content formats and lack of interest in developing solid business models for publishers, social media platforms, on the other hand, have attracted widespread criticism by many academics and media practitioners. This chapter aims at discussing the impact of social media on journalism with the help of digital research that provides an insight on what storytelling types with which three most-followed news outlets in Turkey gain the most engagement on Facebook.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

scholarly journals A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

2020 ◽  
Vol 4 (4) ◽  
pp. 33
Author(s):  
Toni Pano ◽  
Rasha Kashef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Significant Role ◽  
Prediction Models ◽  
Financial Sector ◽  
Research Gap ◽  
Text Preprocessing ◽  
The Impact

During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

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