scholarly journals Capturing the Trajectory of Psychological Status and Analyzing Online Public Reactions During the Coronavirus Disease 2019 Pandemic Through Weibo Posts in China

2021 ◽  
Vol 12 ◽  
Author(s):  
Yi-Chen Chiang ◽  
Meijie Chu ◽  
Shengnan Lin ◽  
Xinlan Cai ◽  
Qing Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Language Processing ◽  
High Frequency ◽  
Large Scale ◽  
Early Period ◽  
Psychological Status ◽  
Internet Users ◽  
External Stimuli ◽  
High Frequency Words

When a major, sudden infectious disease occurs, people tend to react emotionally and display reactions such as tension, anxiety, fear, depression, and somatization symptoms. Social media played a substantial awareness role in developing countries during the outbreak of coronavirus disease 2019 (COVID-19). This study aimed to analyze public opinion regarding COVID-19 and to explore the trajectory of psychological status and online public reactions to the COVID-19 pandemic by examining online content from Weibo in China. This study consisted of three steps: first, Weibo posts created during the pandemic were collected and preprocessed on a large scale; second, public sentiment orientation was classified as “optimistic/pessimistic/neutral” orientation via natural language processing and manual determination procedures; and third, qualitative and quantitative analyses were conducted to reveal the trajectory of public psychological status and online public reactions during the COVID-19 pandemic. Public psychological status differed in different periods of the pandemic (from December 2019 to May 2020). The newly confirmed cases had an almost 1-month lagged effect on public psychological status. Among the 15 events with high impact indexes or related to government decisions, there were 10 optimism orientation > pessimism orientation (OP) events (2/3) and 5 pessimism orientation > optimism orientation (PO) events (1/3). Among the top two OP events, the high-frequency words were “race against time” and “support,” while in the top two PO events, the high-frequency words were “irrationally purchase” and “pass away.” We proposed a hypothesis that people developed negative self-perception when they received PO events, but their cognition was developed by how these external stimuli were processed and evaluated. These results offer implications for public health policymakers on understanding public psychological status from social media. This study demonstrates the benefits of promoting psychological healthcare and hygiene activity in the early period and improving risk perception for the public based on public opinion and the coping abilities of people. Health managers should focus on disseminating socially oriented strategies to improve the policy literacy of Internet users, thereby facilitating the disease prevention work for the COVID-19 pandemic and other major public events.

scholarly journals Unfolding determinants of COVID-19 vaccine acceptance in China (Preprint)

2020 ◽  
Author(s):  
Fulian Yin ◽  
Zhaoliang Wu ◽  
Xinyu Xia ◽  
Meiqi Ji ◽  
Yanyan Wang ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Side Effects ◽  
Language Processing ◽  
Large Scale ◽  
Chinese Government ◽  
Vaccine Acceptance ◽  
Reproduction Ratio ◽  
Inactivated Vaccines ◽  
Chinese Social Media

BACKGROUND China is at the forefront of global efforts to develop COVID-19 vaccines and has five fast-tracked candidates in the final-stage, large scale human clinical trials tests. Layered on top of public engagement, making an informed and judicious choice is a catch-22 for the Chinese government in the context of COVID-19 vaccination promotion. OBJECTIVE In this study, public opinions in China are analyzed via public dialogues on Chinese social media, based on which the views on COVID-19 vaccines and vaccination of Chinese netizens are investigated. We recommend strategies for promoting vaccination programs in the most populous country based on in-depth understanding of the challenges in risk communication and social mobilizations. METHODS We proposed a novel emotional dynamics model SRS/I to analyze the opinion transmission paradigms on Chinese social media. Coupled with meta-analysis and natural language processing (NLP) techniques, the emotion polarity of individual opinion is examined in contexts. RESULTS We collected more than 1.75 million Weibo messages about COVID-19 vaccines from January to October in 2020. According to the public opinion reproduction ratio (R_0), the dynamic propagation of those messages can be classified into three-stage: the Ferment period (R_0,1.1360), the Evolution period (R_0, 2.8278) and the Transmission period (R_0, 3.0729). Significantly, the topics on COVID-19 vaccine acceptance in China are emerging from the landscape of public opinion transmission, such as Price, side effects, and the like. From September to October, 18.3% people held the idea that the vaccine price is high and gets 38.1% “likes,” while 35.9% people regarded it as inexpensive with 25.0% “likes.” The netizen’s emotional polarity on side effects is also the aspect of our research. We got 47.7% positive and 31.9% negative comments. We also captured that the inactivated vaccines aroused much more heated discussion than any other type of vaccine. It accounts for 53% of Discussions of all types’ vaccines, 42% of Forwards, 56% of Comments, and 49% of Likes. CONCLUSIONS Most Chinese hold that the vaccine is cheaper than previously thought, while some claim they could not afford it for their entire family. The Chinese are inclined to be positive to side effects over time and proud of China’s development regarding vaccines. Nevertheless, they have a collective misunderstanding about inactivated vaccines, insisting that inactivated vaccines are safer than other vaccines. Reflecting on those collective responses, the unfolding determinants of COVID-19 vaccine acceptance provide illuminating benchmarks for vaccine-promoting policy-makings.

scholarly journals Alternative media for a populist audience? Exploring political and media use predictors of exposure to Breitbart, Sputnik, and Co.

2021 ◽  
Author(s):  
Philipp Müller ◽  
Anne Schulz
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Relative Deprivation ◽  
Media Use ◽  
Alternative Media ◽  
Selective Exposure ◽  
Internet Users ◽  
Social Media Platforms ◽  
New Type ◽  
The Right

Alongside the recent rise of political populism, a new type of alternative media has established in past years that allegedly contribute to the distribution of the populist narrative. Using a large-scale quota survey of German Internet users (n = 1346) we investigate political and media use predictors of exposure to alternative media with an affinity to populism (AMP). Results reveal substantial differences between occasional and frequent AMP users. While both groups heavily use Twitter and Facebook for political information, occasional AMP users exhibit hardly any specific political convictions (except that they feel less personally deprived than non-users). Contrary to that, frequent AMP exposure is related to higher personal relative deprivation, stronger populist attitudes and a higher likelihood to vote for the right-wing populist party AfD. Against this background, frequent AMP use can be interpreted as partisan selective exposure whereas occasional AMP exposure might result from incidental contact via social media platforms. These findings are discussed regarding the role of alternative and social media in the recent populism wave.

scholarly journals Proactively Discouraging Cyberbullying Activities

2021 ◽  
Vol 9 (10) ◽  
pp. 1601-1607
Author(s):  
Puneetha KR
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Leisure Activities ◽  
Cyber Bullying ◽  
Future Research ◽  
Internet Users ◽  
The Social ◽  
Social Media Platforms ◽  
Root Word ◽  
Cyberbullying Detection

Abstract: Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. Cyber bullying is one of the most common problems faced by the internet users making internet a vulnerable space hence there has to be some detection that is needed on the social media platforms. Detecting the bullies online at the earliest makes sure that these platforms are safer for the user and internet indeed becomes a platform to share information and use it for other leisure activities. Even though there has been some research going on implementing detection and prevention of cyber bullying, it is not completely feasible due to certain limitations imposed. In this paper lexicon-based approach of the NLTK sentiwordnetis used to differentiate the positive and negative words and produce results. These words are given negative and positive values greater than or less than zero for positive and negative words respectively. Lexicon based systems utilize word lists and use the presence of words within the lists to detect cyberbullying. Lemmatization is used to find the root word. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in thisfield. Keywords: Abuse and crime involving computers, natural language processing, sentiment analysis, social networking

scholarly journals MO’ CHARACTERS MO’ PROBLEMS: ONLINE SOCIAL MEDIA PLATFORM CONSTRAINTS AND MODES OF COMMUNICATION

2020 ◽  
Author(s):  
Lewis Mitchell ◽  
Joshua Dent ◽  
Joshua Ross
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
English Language ◽  
Length Change ◽  
Reading Level ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Media Platform ◽  
Processing Techniques

It is widely accepted that different online social media platforms produce different modes of communication, however the ways in which these modalities are shaped by the constraints of a particular platform remain difficult to quantify. On 7 November 2017 Twitter doubled the character limit for users to 280 characters, presenting a unique opportunity to study the response of this population to an exogenous change to the communication medium. Here we analyse a large dataset comprising 387 million English-language tweets (10% of all public tweets) collected over the September 2017--January 2018 period to quantify and explain large-scale changes in individual behaviour and communication patterns precipitated by the character-length change. Using statistical and natural language processing techniques we find that linguistic complexity increased after the change, with individuals writing at a significantly higher reading level. However, we find that some textual properties such as statistical language distribution remain invariant across the change, and are no different to writings in different online media. By fitting a generative mathematical model to the data we find a surprisingly slow response of the Twitter population to this exogenous change, with a substantial number of users taking a number of weeks to adjust to the new medium. In the talk we describe the model and Bayesian parameter estimation techniques used to make these inferences. Furthermore, we argue for mathematical models as an alternative exploratory methodology for "Big" social media datasets, empowering the researcher to make inferences about the human behavioural processes which underlie large-scale patterns and trends.

scholarly journals Combating the Infodemic: A Chinese Infodemic Dataset for Misinformation Identification

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1094
Author(s):  
Jia Luo ◽  
Rui Xue ◽  
Jinglu Hu ◽  
Didier El Baz
Keyword(s):  
Social Media ◽  
High Frequency ◽  
Numerical Experiments ◽  
Healthcare Workers ◽  
Strong Relationship ◽  
Imbalanced Dataset ◽  
Intercoder Reliability ◽  
Social Media Platforms ◽  
True False ◽  
High Frequency Words

Misinformation posted on social media during COVID-19 is one main example of infodemic data. This phenomenon was prominent in China when COVID-19 happened at the beginning. While a lot of data can be collected from various social media platforms, publicly available infodemic detection data remains rare and is not easy to construct manually. Therefore, instead of developing techniques for infodemic detection, this paper aims at constructing a Chinese infodemic dataset, “infodemic 2019”, by collecting widely spread Chinese infodemic during the COVID-19 outbreak. Each record is labeled as true, false or questionable. After a four-time adjustment, the original imbalanced dataset is converted into a balanced dataset by exploring the properties of the collected records. The final labels achieve high intercoder reliability with healthcare workers’ annotations and the high-frequency words show a strong relationship between the proposed dataset and pandemic diseases. Finally, numerical experiments are carried out with RNN, CNN and fastText. All of them achieve reasonable performance and present baselines for future works.

scholarly journals Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the United States

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245319
Author(s):  
Jackson Bennett ◽  
Benjamin Rachunok ◽  
Roger Flage ◽  
Roshanak Nateghi
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
Resource Constraints ◽  
The United States ◽  
Population Based ◽  
Media Discourse ◽  
Policy Framing ◽  
Critical Issues

Surveys are commonly used to quantify public opinions of climate change and to inform sustainability policies. However, conducting large-scale population-based surveys is often a difficult task due to time and resource constraints. This paper outlines a machine learning framework—grounded in statistical learning theory and natural language processing—to augment climate change opinion surveys with social media data. The proposed framework maps social media discourse to climate opinion surveys, allowing for discerning the regionally distinct topics and themes that contribute to climate opinions. The analysis reveals significant regional variation in the emergent social media topics associated with climate opinions. Furthermore, significant correlation is identified between social media discourse and climate attitude. However, the dependencies between topic discussion and climate opinion are not always intuitive and often require augmenting the analysis with a topic’s most frequent n-grams and most representative tweets to effectively interpret the relationship. Finally, the paper concludes with a discussion of how these results can be used in the policy framing process to quickly and effectively understand constituents’ opinions on critical issues.

Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis

2021 ◽  
Vol 17 (2) ◽  
pp. 59-78
Author(s):  
Shailendra Kumar Singh ◽  
Manoj Kumar Sachan
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Large Scale ◽  
English Language ◽  
Product Reviews ◽  
Internet Users ◽  
Social Media Text ◽  
Analysis System

The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilingual phonetic text. Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain datasets. The system performance is tested on four different datasets and achieved better accuracy of 3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56% on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for verbs of the English language is proposed, which improves the SA system's performance.

Online Public Opinion about COVID-19 Vaccines: A Content Analysis of Vaccine Discussions on Twitter (Preprint)

2021 ◽  
Author(s):  
Huang Huang ◽  
Yuanbo Qiu
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
English Language ◽  
Early Period ◽  
Negative Attitude ◽  
Vaccine Safety ◽  
Vaccine Hesitancy ◽  
Analysis Tool ◽  
Health Providers ◽  
Vaccine Acceptance

BACKGROUND To combat the COVID-19 pandemic, various vaccines have been developed and their rollout is under way. However, the uptake rate is hindered by vaccine hesitancy influenced by the conversations on social media. It is necessary to trace public opinion toward COVID-vaccines on social media. OBJECTIVE The objective of this study is to examine the sentiments and topics of English-language twitter discussion regarding COVID-19 vaccination. Further this study also aims to explore the temporal trend of sentiments and topics over one month in the early period of vaccine roll-out. METHODS Following existing studies of vaccine acceptance and social media, we collected Tweet posts from Twitter data base using Twitter API from December 2020 to January 2021, which reflected actual public discussions toward COVID vaccination after the beginning of the rollout. After data cleansing and selection, 656,102 vaccine-related tweets were identified from 329,441 unique users. We leveraged VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis tool to explore sentiment scores and Natural Language Toolkit (NLTK) to confirm relevant topics. We also depicted daily changes of sentiments and topics in COVID-vaccine-related tweets across one month period. RESULTS Forty-two percentage of tweets expressed pro-vaccine sentiments while 21% held negative attitudes. The trend of sentiment kept positive and consistent overtime, but a sudden surge of negative tweets occurred around the New Year, which was caused by some unexpected adverse incidents. The Six main topics associated with vaccines were identified: Advocation of vaccination (42,459, 6.47%), Official information releases (29,847, 4.55%), Vaccine distribution (12,946, 1.97%), Vaccine safety concerns (11,236, 1.71%), Personal vaccination experience (5,594, 0.85%) and Conspiracy theory (2,962, 0.45%). Among popular tweets that have been reposted frequently, adverse incidents reported by reliable source have triggered intense discussions about vaccine safety issues, usually in a negative attitude. CONCLUSIONS : Most tweets expressed non-negative sentiments toward vaccination. However, vaccination-related adverse incidents have triggered intense discussions in a negative attitude. Our findings can help policymakers and health providers view the whole picture of the influence of social media and develop better communicative strategies for improving vaccine acceptance.

scholarly journals Impact of rumors or misinformation on coronavirus disease (COVID-19) in social media

2020 ◽  
Author(s):  
Samia Tasnim ◽  
Md Mahbub Hossain ◽  
Hoimonty Mazumder
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Large Scale ◽  
Health Care Organization ◽  
Healthcare Providers ◽  
Scientific Basis ◽  
Accurate Information ◽  
Care Organization ◽  
Strategic Partnerships ◽  
Physical And Mental Health

The COVID-19 pandemic has not only caused significant challenges for health system all over the globe but also fueled the surge of numerous rumors, hoaxes and misinformation, regarding etiology, outcomes, prevention, and cure of the disease. This misinformation are masking healthy behaviors and promoting erroneous practices that increase the spread of the virus and ultimately result in poor physical and mental health outcomes among individuals. Myriad incidents of mishaps caused by these rumors was reported across the world. To address this issue the frontline healthcare providers should be equipped with the most recent research findings and accurate information. The mass media, health care organization, community-based organizations, and other important stakeholders should build strategic partnerships and launch common platforms in disseminating authentic public health messages. Advanced technologies like natural language processing or data mining approaches should be applied in detection and removal online content with no scientific basis from all social media platforms. Those involved with the spread of such rumors should be brought to justice. Telemedicine based care should be established at a large scale to prevent depletion of limited resources.

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