Rapid Perception of Public Opinion in Emergency Events through Social Media

2022 ◽  
Vol 23 (2) ◽  
Yudi Chen ◽  
Yun Li ◽  
Zifu Wang ◽  
Alma Joanna Quintero ◽  
Chaowei Yang ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

Communicology ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 167-179
E.S. Nadezhkina

The term “digital public diplomacy” that appeared in the 21st century owes much to the emergence and development of the concept of Web 2.0 (interactive communication on the Internet). The principle of network interaction, in which the system becomes better with an increase in the number of users and the creation of user-generated content, made it possible to create social media platforms where news and entertainment content is created and moderated by the user. Such platforms have become an expression of the opinions of various groups of people in many countries of the world, including China. The Chinese segment of the Internet is “closed”, and many popular Western services are blocked in it. Studying the structure of Chinese social media platforms and microblogging, as well as analyzing targeted content is necessary to understand China’s public opinion, choose the right message channels and receive feedback for promoting the country’s public diplomacy. This paper reveals the main Chinese social media platforms and microblogging and provides the assessment of their popularity, as well as possibility of analyzing China’s public opinion based on “listening” to social media platforms and microblogging.

2021 ◽  
Vol 24 (2) ◽  
pp. 270-275 ◽  
Karen M. Douglas

Conspiracy theories started to appear on social media immediately after the first news about COVID-19. Is the virus a hoax? Is it a bioweapon designed in a Chinese laboratory? These conspiracy theories typically have an intergroup flavour, blaming one group for having some involvement in either manufacturing the virus or controlling public opinion about it. In this article, I will discuss why people are attracted to conspiracy theories in general, and why conspiracy theories seem to have flourished during the pandemic. I will discuss what the consequences of these conspiracy theories are for individuals, groups, and societies. I will then discuss some potential strategies for addressing the negative consequences of conspiracy theories. Finally, I will consider some open questions for research regarding COVID-19 conspiracy theories, in particular focusing on the potential impact of these conspiracy theories for group processes and intergroup relations.

2021 ◽  
Vol 32 (2) ◽  
pp. 36-49
Lu An ◽  
Junyang Hu ◽  
Manting Xu ◽  
Gang Li ◽  
Chuanming Yu

The highly influential users on social media platforms may lead the public opinion about public events and have positive or negative effects on the later evolution of events. Identifying highly influential users on social media is of great significance for the management of public opinion in the context of public events. In this study, the highly influential users of social media are divided into three types (i.e., topic initiator, opinion leader, and opinion reverser). A method of profiling highly influential users is proposed based on topic consistency and emotional support. The event of “Jiankui He Editing the Infants' Genes” was investigated. The three types of users were identified, and their opinion differences and dynamic evolution were revealed. The comprehensive profiles of highly influential users were constructed. The findings can help emergency management departments master the focus of attention and emotional attitudes of the key users and provide the method and data support for opinion management and decision-making of public events.

Fan Zuo ◽  
Abdullah Kurkcu ◽  
Kaan Ozbay ◽  
Jingqin Gao

Emergency events affect human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks, such as tweets, containing information about their status, help requests, incident reports, and other useful information. In this research project, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident-related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during two recent events in New York City, the Chelsea explosion and Hurricane Sandy, are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the model’s hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as support vector machine and recurrent neural network.

2021 ◽  
Vol 54 (3-4) ◽  
pp. 181-196
Piotr Kwiatek ◽  
Radoslav Baltezarević ◽  
Stavros Papakonstantinidis

Companies are becoming increasingly aware of the importance and opportunities provided by social media in order to communicate faster and more efficiently with their consumers. In order to convey the message about the value of their brands to their target market, organizations hire influential people who are considered to be the creators of public opinion in a virtual environment. Nowadays, social media are crowded with micro and macro influencers, or at least those who present themselves as such. Their main job is to represent and recommend brands to other users (their followers) and thus influence consumer attitudes about brands and possibly strengthen their purchasing decisions. Very often, influencers on social media are hired by companies to promote their brands for a fee. In order to have more control over their communication activities, companies provide them, in advance, with the content they want to be conveyed to consumers, but also the time frame when the message should be launched. In this way, organizations try to reduce the risk of turning electronic word-of-mouth (EWOM) communication into a negative one. In order for consumers to trust the recommendations of influencers on social media, these people need to have significant expertise in a certain area, charisma and respect from other users, so that their credibility affects the value of content and recommendations they place in the online environment. The aim of this paper is to present the attitudes of respondents who use social media websites about the impact of the credibility of influencers' recommendations on social media, and their opinion on whether and in what way their credibility influences consumer attitudes towards brands.

2020 ◽  
Vol 19 (4) ◽  
pp. 85-94
T.M. Bormotova ◽  
Yu.N. Mazaev ◽  
O.V. Yakovlev ◽  

based on a comprehensive analysis of content, posted text and video arrays of publications in Internet sources and social media of the Samara region, the main factors affecting the formation of public opinion about the police are considered. Quantitative analysis of the publication arrays revealed the tone of meaningful interpretation of numerical patterns of information about the activities of the regional police. Recommendations aimed at improving the attitude of the population to the work of the regional police are formulated.

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