Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine

2019 ◽  
Vol 15 (3) ◽  
pp. 275-283 ◽  
Author(s):  
Iana Sabatovych
Keyword(s):  
Social Media ◽  
Social Movements ◽  
Public Opinion ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Social Engagement ◽  
Naive Bayes ◽  
Public Attitudes ◽  
Social Media Platforms ◽  
Online Prediction

A wide variety of social media platforms have become integral to contemporary forms of social engagement, including mass protests. Twitter is considered specifically indicative of public attitudes in this regard. This study attempts to examine the feasibility of using Twitter sentiment analysis to predict the 2014 revolution in Ukraine. Tweets representing public opinion are clustered by means of the ‘StreamKM++’ algorithm into three classes (likely, neutral and unlikely). The resulting prediction model for the three classes (using Naïve Bayes) was 96.75 per cent. As such, this study offers a promising way to perform an online prediction of social movements.

scholarly journals Sentiment Analysis of Perpustakaan Nasional Republik Indonesia Through Social Media Twitter

MATICS ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 90
Author(s):  
Fakhris Khusnu Reza Mahfud
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
User Needs ◽  
Twitter Data ◽  
Knn Classification ◽  
National Libraries

The library is a gate of science and a heart of civilization. Indonesia already has a Perpustakaan Nasional consisted of 27 floors and is equipped with facilities that are adequate for user needs. Apart from that, we need to see opinions from the community as users. Public opinion about the library is critical for library managers to evaluate services and facilities from the library. One way to find out the views of the community is by using social media twitter. Twitter social media is often used in channelling opinions or expressing opinions about specific topics; besides social media, twitter is commonly used for digital campaign movements. Submission of views and even digital campaigns on Twitter social media greatly influence the opinions and even behaviour of society in various ways. This study analyzes tweets about national libraries by classifying, positive opinions, negative opinions and neutral opinions. In this study, twitter data will go through the preprocessing, weighting, and classification stages. TF-IDF and TF binary are used in weighting in this study. The classification used in this study is Naive Bayes and KNN. Accuracy, precision, and recall values were also used in this study to evaluate classification performance. The highest classification performance using KNN classification with TF-IDF weighting resulted in the value of accuracy, precision, and recall of 83.33%, 79.2%, and 83.3% respectively.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

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%.

Chinese Social Media Platforms and Microblogging in the Context of Public Diplomacy

Communicology ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 167-179
Author(s):  
E.S. Nadezhkina
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Public Diplomacy ◽  
User Generated Content ◽  
The Internet ◽  
Interactive Communication ◽  
Network Interaction ◽  
Social Media Platforms ◽  
The Right ◽  
Chinese Social Media

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.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

Profiling the Users of High Influence on Social Media in the Context of Public Events

2021 ◽  
Vol 32 (2) ◽  
pp. 36-49
Author(s):  
Lu An ◽  
Junyang Hu ◽  
Manting Xu ◽  
Gang Li ◽  
Chuanming Yu
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Emergency Management ◽  
Emotional Support ◽  
Opinion Leader ◽  
Negative Effects ◽  
The Public ◽  
Public Events ◽  
Social Media Platforms ◽  
Influential Users

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.

Outro

Renegades ◽  
2021 ◽  
pp. 123-128
Author(s):  
Trevor Boffone
Keyword(s):  
Social Media ◽  
Social Movements ◽  
Social Justice ◽  
Significant Role ◽  
Black Girls ◽  
New Music ◽  
Black Lives Matter ◽  
Social Media Platforms ◽  
The Revolution

The Outro explores how the Renegades throughout this book used their social media platforms and clout to further social justice messages during the height of the renewed Black Lives Matter movement following the murder of George Floyd in summer 2020. Renegade Zoomers played a significant role in celebrating Blackness and made many of these “moves” on social media. Whether it was through attending marches, creating viral dance challenges, or producing new music, Renegades positioned their creativity, joy, and labor as central to the movement for Black lives. Their work forced onlookers, moreover, to recognize the labor of Black girls in our social movements. Renegades reveal, ultimately, that the revolution will be digital.

scholarly journals Demystifying Social Bots: On the Intelligence of Automated Social Media Actors

2020 ◽  
Vol 6 (3) ◽  
pp. 205630512093926 ◽  
Author(s):  
Dennis Assenmacher ◽  
Lena Clever ◽  
Lena Frischlich ◽  
Thorsten Quandt ◽  
Heike Trautmann ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Public Opinion ◽  
Research Effort ◽  
Online Media ◽  
Social Media Platforms ◽  
New Perspective ◽  
High Degree ◽  
Supportive Nature ◽  
Shed Light

Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots.

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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