scholarly journals Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Yi Zhao ◽  
Haixu Xi ◽  
Chengzhi Zhang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Experimental Results ◽  
Social Implications ◽  
Income Levels ◽  
Related Information ◽  
The Social ◽  
Twitter Users

AbstractCoronavirus disease 2019 (COVID-19) pandemic-related information are flooded on social media, and analyzing this information from an occupational perspective can help us to understand the social implications of this unprecedented disruption. In this study, using a COVID-19-related dataset collected with the Twitter IDs, we conduct topic and sentiment analysis from the perspective of occupation, by leveraging Latent Dirichlet Allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model, respectively. The experimental results indicate that there are significant topic preference differences between Twitter users with different occupations. However, occupation-linked affective differences are only partly demonstrated in our study; Twitter users with different income levels have nothing to do with sentiment expression on covid-19-related topics.

scholarly journals Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data (Preprint)

2020 ◽  
Author(s):  
Yankun Gao ◽  
Zidian Xie ◽  
Dongmei Li
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Viral Infections ◽  
Application Programming Interface ◽  
Electronic Cigarette ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Twitter Users ◽  
Public Health Practitioners

BACKGROUND Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic. OBJECTIVE In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data. METHODS The study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups. RESULTS The US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic. CONCLUSIONS Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

Social Media Activism and Convergence in Tweet Topics After the Initial #MeToo Movement for Two Distinct Groups of Twitter Users

2021 ◽  
pp. 088626052110014
Author(s):  
Jason M. Baik ◽  
Thet H. Nyein ◽  
Sepideh Modrek
Keyword(s):  
Social Media ◽  
Sexual Assault ◽  
Frequency Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Bigram Frequency ◽  
Media Activism ◽  
Online Social Media ◽  
Before And After ◽  
Twitter Users

Online social media movements are now common and support cultural discussions on difficult health and social topics. The #MeToo movement, focusing on the pervasiveness of sexual assault and harassment, has been one of the largest and most influential online movements. Our study examines topics of conversation on Twitter by supporters of the #MeToo movement and by Twitter users who were uninvolved in the movement to explore the extent to which tweet topics for these two groups converge over time. We identify and collect one year’s worth of tweets for supporters of the #MeToo movement ( N = 168 users; N = 105,538 tweets) and users not involved in the movement ( N = 147 users; N = 112,301 tweets referred to as the Neutral Sample). We conduct topic frequency analysis and implement an unsupervised machine learning topic modeling algorithm, latent Dirichlet allocation, to explore topics of discussion on Twitter for these two groups of users before and after the initial #MeToo movement. Our results suggest that supporters of #MeToo discussed different topics compared to the Neutral Sample of Twitter users before #MeToo with some overlap on politics. The supporters were already discussing sexual assault and harassment issues six months before #MeToo, and discussion on this topic increased 13.7-fold in the six months after. For the Neutral Sample, sexual assault and harassment was not a key topic of discussion on Twitter before #MeToo, but there was some limited increase afterward. Results of bigram frequency analysis and topic modeling showed a clear increase in topic related to gender for the supporters of #MeToo but gave mixed results for the Neutral Sample comparison group. Our results suggest limited shifts in the conversation on Twitter for the Neutral Sample. Our methods and results have implications for measuring the extent to which online social media movements, like #MeToo, reach a broad audience.

scholarly journals Implementasi Algoritma Klasifikasi Terhadap Tweet Pornografi Kaum Homoseksual Pada Twitter

2020 ◽  
Vol 6 (2) ◽  
pp. 204-212
Author(s):  
Taopik Hidayat ◽  
Rangga Pebrianto ◽  
Risca Lusiana Pratiwi ◽  
Windu Gata ◽  
Daniati Uki Eka Saputri
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Personal Relationships ◽  
Same Sex ◽  
The Social ◽  
Twitter Users ◽  
Negative Impacts

Abstract: Twitter is one of the social media with the number of users who reach millions of users. The number of Twitter users in 2019 increased by 17 percent in 2018 to 145 million users with a variety of good both positive and bad. The negative impacts that occur such as the spread of status, images, and videos that affect pornography especially among freedom groups. Homosexuals are sexually oriented people who like the same sex that occurs in men, the rejection often experienced by men makes one of the reasons intellectuals use Twitter social media to show their personal relationships, open to each other, socializing with same sex, looking for conversation, to become a place to find a partner. The purpose of this study is to determine the positive and negative sentiments to determine the level of accuracy of intellectual pornography tweets in Indonesia from data taken from Twitter tweets by using the TF-IDF and k-NN methods. The results of this study get an accuracy value of 88.25% containing pornography and the remaining 11.75% not containing pornography will contain news, news, and other information.Keywords: homosexual, sentiment analysis, twitterAbstrak: Twitter merupakan salah satu media sosial dengan jumlah pengguna mencapai jutaan pengguna. Jumlah pengguna Twit-ter pada tahun 2019 dicatat meningkat 17 persendari tahun 2018 menjadi 145 juta pengguna dengan berbagai dampak baik dampak positif maupun dampak negatif. Dampak negatif yang ditimbulkannya seperti penyebaran status, gambar, dan video yang bersifat pornografi khsusunya di kalangan kaum homoseksual. Homoseksual merupakan orang yang berorientasi seksual sebagai penyuka sesama jenis yang terjadi pada kaum pria, Penolakan yang sering dialami kaum homoseksual men-jadikan salah satu alasan kaum homoseksual menggunakan media sosial Twitter untuk menunjukkan identitas diri mereka, saling terbuka, bersosialisasi dengan sesama jenis, mencari penghasilan, hingga menjadi ajang pencarian pasangan. Tujuan dari penelitian ini adalah untuk mengetahui sentimen positif dan negatif untuk mengetahui tingkat akurasi terhadap tweet pornografi kaum homoseksual di Indonesia dari data yang diambil dari tweet Twitter dengan menggunakan metode TF-IDF dan k-NN. Hasil penelitian ini mendapatkan nilai accuracy sebesar 88,25% mengandung unsur pornografi dan sisanya sebesar 11,75 tidak mengandung unsur pornografi akan tetapi berisi iklan, berita, dan informasi lainnya.Kata kunci: homoseksual, sentimen analisis, twitter

scholarly journals Understanding users Display-Name Consistency across Social Networks

2019 ◽  
Vol 8 (5S3) ◽  
pp. 471-476
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Networking ◽  
Experimental Results ◽  
Personal Attributes ◽  
Media Content ◽  
Social Networking Services ◽  
Twitter User ◽  
The Social ◽  
Twitter Users

Online users create their profiles on numerous social platforms to get benefits of various types of social media content. During online profile creation, the user selects a username and feeds his/her personal details like name, location, email, etc. As different social networking services acquire common personal attributes of the same user and present them in a variety of formats. To understand the availability and similarity of personal attributes across various social networking services, we propose a method that uses the different distance measuring algorithms to determine the display-name similarity across social networks. From the experimental results, it is found that at least twenty percent GooglePlus-Facebook and Facebook-Twitter users select the same display name, while forty five percent Google and Twitter user select identical name across both the social networks.

scholarly journals Sentiment Analysis of West Java International Airport (Bijb) Kertajati on Twitter

2020 ◽  
Vol 4 (2) ◽  
pp. 176-182
Author(s):  
Oka Intan ◽  
Sri Widiyanesti
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Rapid Development ◽  
West Java ◽  
Word Cloud ◽  
Twitter User ◽  
The Social ◽  
Twitter Users ◽  
Negative Sentiment

The rapid development of technology allows everything to accessed by the internet that causes many users of social media and one of the social media is Twitter. An interesting topic to discuss on Twitter is about new and fresh things that attract many users to get involved. One of the things that attract Twitter users is the construction of a new airport, namely Kertajati Airport, which has some problems with airport activities, such as the small number of visitors, lonely conditions of the airport, and decreased number of routes. This study aims to find out Twitter user sentiments towards Kertajati Airport in West Java to know the quality of Kertajati Airport. The method used in this study is sentiment analysis by looking at the calculation of how many positive and negative sentiment have been obtained with the most result so it can reflect the quality of Kertajati Airport and then there is a word cloud to see the spread of word related to sentiment. The results of this study indicate that the quality of the Kertajati Airport cannot be said to be good because the results of the sentiment analysis found that negative sentiments have more percentages than positive sentiments

scholarly journals Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

2020 ◽  
Vol 4 (3) ◽  
pp. 650
Author(s):  
Rian Tineges ◽  
Agung Triayudi ◽  
Ira Diana Sholihati
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Internet Service ◽  
Active User ◽  
The Social ◽  
Twitter Users ◽  
Use Of The Internet

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

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.

scholarly journals Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study (Preprint)

2020 ◽  
Author(s):  
Sakun Boon-Itt ◽  
Yukolpat Skunkan
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
Public Perception ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Public Awareness ◽  
Good Communication ◽  
Three Stages ◽  
Twitter Users

BACKGROUND COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. OBJECTIVE The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. METHODS Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. RESULTS The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. CONCLUSIONS Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.

scholarly journals Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

10.2196/21978 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e21978
Author(s):  
Sakun Boon-Itt ◽  
Yukolpat Skunkan
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Topic Modeling ◽  
Public Perception ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Public Awareness ◽  
Good Communication ◽  
Three Stages ◽  
Twitter Users

Background COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. Objective The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. Methods Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. Results The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. Conclusions Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

2020 ◽  
Vol 5 (1) ◽  
pp. 86-99
Author(s):  
Runbin Xie ◽  
Samuel Kai Wah Chu ◽  
Dickson Kak Wah Chiu ◽  
Yangshu Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Disease Outbreaks ◽  
Semantic Networks ◽  
Web Crawler ◽  
Public Response ◽  
The Public

AbstractIt is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and “self-media,” together contribute to the information spread of positive sentiment.

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