Number Of Twitter Users Research Articles

Implementasi Model Support Vector Machine Dalam Analisa Sentimen Masyarakat Mengenai Kebijakan Penerapan Aplikasi Mypertamina

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.

The Effect of Social Media on Elections: Evidence from The United States

Abstract We study how social media affects election outcomes in the United States. We use variation in the number of Twitter users across counties induced by early adopters at the 2007 South by Southwest festival, a key event in Twitter’s rise to popularity. We show that this variation is unrelated to observable county characteristics and electoral outcomes before the launch of Twitter. Our results indicate that Twitter lowered the Republican vote share in the 2016 and 2020 presidential elections, but had limited effects on Congressional elections and previous presidential elections. Evidence from survey data, primary elections, and text analysis of millions of tweets suggests that Twitter’s relatively liberal content may have persuaded voters with moderate views to vote against Donald Trump.

Sentiment Analysis in Environmental Sustainability Field by Machine Learning

Environmental sustainability is one of the influential topics of the last decade. Most people have become more environmentally aware and educated environmentally conscious. Sustainability concerns the sustainability of natural resources and environmental protection. The four pillars of sustainability include human, social, economic and environmental. Twitter is a popular social media platform that keeps us updated on the latest news, events and trends from around the world. In 2022, the number of Twitter users in Thailand reached around 11.45 million, accounting for 16.4 % of all Thai people. Also, the fastest growing conversation in Twitter is related to the environment and sustainability. Nowadays, customers pay attention to the environmental and social impact of products they buy. Sentiment Analysis is the process of analyzing emotions or feelings by using machine learning techniques. The main objective of this exploratory study is to conduct social media opinion mining in case of the environmental sustainability field of Thai people. The paper presents the linguistic analysis of the collected data and explains discovered phenomena, including data preprocessing steps, feature extraction, and model construction to determine positive, negative and neutral sentiments. The result reveals that sentiment analysis takes place around the sustainability context mostly in positive terms to make a better understanding of the dynamics and changes in environmental sustainability society.

Analisis Sentimen Masyarakat Indonesia Dalam Konflik Rusia-Ukraina Di Twitter

Russia is a big superpower that has power and plays an important role in international politics, while Ukraine, a former Soviet Union country, became independent on December 1, 1991. In 2014 there was also a conflict between Russia and Ukraine which was a meeting of superpowers. In February 2022, Russia resumed armed conflict with Ukraine. The Russia-Ukraine conflict has garnered many responses in the form of tweets from various circles of society, resulting in many traces of tweets containing public opinion on the Russia- Ukraine conflict on Twitter social media. This study aims to determine the results of the positive or negative impact of the conflict between Russia and Ukraine on the economy in Indonesia and to determine the results of accuracy, precision, recall resulting from the use of the Naïve Bayes method and feature selection Particle Swarm Optimization in RapidMiner Studio software. Particle Swarm Optimization is an optimization method inspired by the behavior of fish and poultry flocks in search of food sources. The preprocessing stage in this research includes cleansing, removing duplicates, data selection, normalization, case folding, tokenizing, filtering, stopwords, stemming, and labeling. The classification results obtained by 55.11% of twitter users commented negatively and 44.89% of twitter users commented positively about the conflict between Russia and Ukraine. By looking at the results of the sentiment analysis data above, where the number of Twitter users who commented negatively is higher, it can be concluded that the Indonesian people are worried about the surge in prices of basic daily necessities, as indicated by one of the tweets commenting on rising oil and gas prices BBM

Pengaruh Ulasan Konsumen pada Media Sosial Twitter Terhadap Keputusan Pembelian di Shopee

The development of the digital era in Indonesia has caused the internet to become a necessity for the Indonesian people. This makes Indonesia one of the largest online markets in Southeast Asia, with e-commerce as the main industry driver. Behind this high development, consumer behavior can be an important component in understanding business trends or business opportunities for business owners on e-commerce platforms. In addition, the dissemination of information on microblog services can be quickly spread, and this information can be easily searched by typing keywords or keywords from the information to be searched. In addition, the number of Twitter users in Indonesia has been at the top of the microblogging social media platform category in recent years. The purpose of this study is to explain how the influence of consumer reviews on Twitter social media on purchasing decisions at Shopee partially and simultaneously. The findings in this study were obtained using a quantitative method with a deductive type of research by distributing questionnaires to 385 respondents. Data processing using IBM SPSS Statistics software version 25, ranging from validity and reliability tests to data analysis techniques using multiple linear regression analysis techniques. The results of this study provide a quantitative description and show that behavioral perceptions, subjective norms, peer communication, emotional support, and parasocial interactions each have a positive and significant effect on purchasing decisions at Shopee Indonesia

Constructing twitter corpus of Iraqi Arabic Dialect (CIAD) for sentiment analysis

The number of Twitter users in Iraq has increased significantly in recent years. Major events, the political situation in the country, had a significant impact on the content of Twitter and affected the tweets of Iraqi users. Creating an Iraqi Arabic Dialect corpus is crucial for sentiment analysis to study such behaviors. Since no such corpus existed, this paper introduces the Corpus of Iraqi Arabic Dialect (CIAD). The corpus has been collected, annotated and made publicly accessible to other researchers for further investigation. Furthermore, the created corpus has been validated using eight different combinations of four feature-selections approaches and two versions of Support Vector Machine (SVM) algorithm. Various performance measures were calculated. The obtained accuracy, 78 %, indicates a promising potential application.

Topic Extraction in Social Networks

The number of Twitter users is increasing and the quantity of produced data is growing. Using this big data to analyze user behavior has become a very active field. The two key challenges of this paper are extracting data from Twitter and extracting topics from user tweets. The proposed approach uses data crawling to collect data from Twitter and a bunch of natural language processing techniques to extract information from the so collected data and build a dataset. Thereafter, we use K-means clustering and Latent Dirichlet Allocation to extract the prevalent topics from this dataset, as they are the most common in the literature. Our proposal is generic, it can be reused by scientists to annotate any text collection.

Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis

BackgroundPopulation mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases.ObjectiveThe aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina.MethodsThis longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting.ResultsPopulation mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%.ConclusionsUsing Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

Politicians on Social Media. The online database of members of national parliaments on Twitter

In this article, we present the new online database Politicians on Social Media (https://www.politiciansonsocialmedia.com), which is intended for both academics and the public. This database was created with the aim of being continuously updated and providing relevant information on politicians’ activity on social media. We first used data from this database to analyze Twitter. This social networking site is increasingly at the forefront of scientific interest due to its growing number of users. It is often examined with respect to how politicians use it. For politicians, Twitter is an opportunity not only to inform people about their activities but also to communicate directly with voters. Attention is focused on members of parliaments (MPs). We examine not only how many MPs have adopted Twitter but also how active they are on it. We also examine the determinants of Twitter adoption by MPs. We analyzed all countries of the European Union, the European Free Trade Association, and the United Kingdom. Therefore, the research includes 32 European countries. Our analysis revealed that most MPs have adopted Twitter in Western European countries such as France, Ireland, the Netherlands, Spain, or the United Kingdom, while MPs in Bulgaria, Hungary, Romania, and Slovakia use Twitter the least. Twitter is very actively used by Spanish and British MPs. By contrast, the least active MPs on Twitter are in post-communist countries such as Bulgaria and Romania. Our correlation analysis showed a strong association between the number of Twitter users in countries and the adoption of Twitter by MPs. Specifically, more Twitter users in a country correlates with its being adopted by more MPs.

Public Perceptions about Nepalese National Parks: A Global Twitter Discourse Analysis

Public participation in managing natural resources is important but obtaining diverse perspectives remains challenging. To assess how Twitter, a globally popular and influential public forum, could provide insights into diverse views about natural resource management in developing countries, we examined tweets about Nepalese National Parks. The content and sentiments of >9,000 tweets posted by >4,500 Twitter users from >90 countries were evaluated. Most tweets were sent by accounts in Nepal, India, USA, UK, Australia and Canada, with tourism, charismatic fauna, landscapes, culture, ethnic people, and issues relating to managing parks common topics. Twitter users’ origin/home location influenced which park and topics they discuss and with what emotions. The number of Twitter users was not correlated with park visitation but rather tweets were often sent in response to specific events and news. Twitter can provide insights into perceptions about parks including in low income countries, but there are important limitations.

The Effect of Social Media on Elections: Evidence from the United States

We study how social media affects election outcomes in the United States. We use variation in the number of Twitter users across counties induced by early adopters at the 2007 South by Southwest (SXSW) festival, a key event in Twitter's rise to popularity. We show that this variation is unrelated to observable county characteristics and electoral outcomes before the launch of Twitter. Our results indicate that Twitter lowered the Republican vote share in the 2016 and 2020 presidential elections, but had limited effects on Congress elections and previous presidential elections. Evidence from survey data, primary elections, and a text analysis of millions of tweets suggests that Twitter's relatively liberal content may have persuaded voters with moderate views to vote against Donald Trump.

The Effect of Social Media on Elections: Evidence from the United States

We study how social media affects election outcomes in the United States. We use variation in the number of Twitter users across counties induced by early adopters at the 2007 South by Southwest (SXSW) festival, a key event in Twitter's rise to popularity. We show that this variation is unrelated to observable county characteristics and electoral outcomes before the launch of Twitter. Our results indicate that Twitter lowered the Republican vote share in the 2016 and 2020 presidential elections, but had limited effects on Congress elections and previous presidential elections. Evidence from survey data, primary elections, and a text analysis of millions of tweets suggests that Twitter's relatively liberal content may have persuaded voters with moderate views to vote against Donald Trump.

An Intelligent Model for Text Forensics from Arabic Twitter Posts

The number of twitter users in Arab countries at a rapid pace of growth. As for the social networking platforms, they are a suitable environment for luring innocent users and commit different forms of crimes against them by cyber predators through claiming something they are not such as fake ID, age or gender targeting certain segments of the society. This have resulted in a huge number of cybercrimes including Phishing, harassment, cyber terrorism and many other forms. In this context, a number of different research efforts in the literature are available for English but very rare for Arabic texts. This article proposes an intelligent technique for enhancing text preprocessing that impacting the accuracy and performance of the classification task. The proposed model was evaluated against recently collected dataset from Arabic twitter users using different classifiers including naïve bays and neural networks to predict age attribute.

Detection and Prevention of Twitter Users with Suicidal Self-Harm Behavior

Recently, with the development of communication means such as 4G and the rapid growth of the use of mobile devices (smartphones and tablets) the number of twitter users has increased exponentially. By the end of 2018 Twitter had 321 million active users with over 600 million tweets every day. However, all this information will have no use if we cannot access the meaning it carries. The authors' idea is to identify Twitter users with suicidal or self-harm behaviors by analyzing their tweets using an algorithm inspired from the social life of Asian elephants. The objective is to prevent the situations of depressions, threats of suicide or any other form of self-destructive behavior that exists on Twitter.

Improving Nighttime Light Imagery With Location-Based Social Media Data

Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors.

Internet zavisnost kod studenata Zdravstvene nege

INTRODUCTION: Internet addiction is a topic that is widely debated in scientific circles and the media. Students spend a lot of time in content-related activities online, neglecting common activities such as socializing, business obligations, learning, home affairs, etc., and have difficulty interrupting internet activities, even when they themselves recognize it is time for it. AIM: The aim of this research was to investigate Internet addiction among students of the Medical Care, Faculty of Medicine in Foča. MATERIAL AND METHODS: The research was conducted at the Medical Faculty in Foča in the period from 07.11. until 09.11.2016. year with health care students. The selected study design is a cross section study. The study covered 60 students, the first, second and third year of studies that were present on the days of the study and volunteered to participate in the research. RESULTS: The survey involved 60 respondents, of whom 21 are men (35%) and 39 women (65%), ages 18 to 24 years old. All of our respondents are Internet users. The largest percentage of respondents (66.7%) use the internet for 2 to 4 hours. The largest number of respondents first accessed the Internet at the age of 13 (21.7%). Most respondents use Facebook as a social network 56 (93.3%), while the least number of Twitter users. CONCLUSION: All of our respondents are Internet users, and the largest percentage of respondents (66.7%) use the internet for 2 to 4 hours. The largest number of respondents first accessed the Internet at the age of 13 (21.7%).

Rare Cancers and Social Media: Analysis of Twitter Metrics in the First 2Years of a Rare-Disease Community for Myeloproliferative Neoplasms on Social Media-#MPNSM.

The use of social media has now become a standard means of communication for many individuals worldwide. The use of one specific form of social media, Twitter, has increased among healthcare providers, both as a means of information gathering and as a conduit for original content creation. Recently, major efforts by users have been put forward to help streamline the unprecedented amount of information that can be found on Twitter. These efforts have led to the creation of diseasespecific hashtag (#) medical communities and have greatly enhanced the ability to understand and better categorize the available data on Twitter. Specifically, for those involved in rare cancer fields, adhering to organically designed and consistently used hashtags has led to the rapid, reliable dissemination of information and the ability to efficiently discuss and debate topics of interest in the field. For the field of myeloproliferative neoplasms (MPNs), the creation of #MPNSM (myeloproliferative neoplasms on social media) in 2015 has facilitated interactions among healthcare stakeholders from all over the world in the MPN field. In order to better understand the trends and topics of interest to Twitter users of this novel medical community, we conducted the present analysis which focuses on Twitter analytics from the first two years of #MPNSM. In this analysis, we observed a sustained increase in the number of Twitter users, number of tweets, number of impressions, and number of retweets over time, demonstrating the feasibility of creating and maintaining a disease-specific hashtag for a rare cancer over time.

"Right Time, Right Place" Health Communication on Twitter: Value and Accuracy of Location Information

BackgroundTwitter provides various types of location data, including exact Global Positioning System (GPS) coordinates, which could be used for infoveillance and infodemiology (ie, the study and monitoring of online health information), health communication, and interventions. Despite its potential, Twitter location information is not well understood or well documented, limiting its public health utility.ObjectiveThe objective of this study was to document and describe the various types of location information available in Twitter. The different types of location data that can be ascertained from Twitter users are described. This information is key to informing future research on the availability, usability, and limitations of such location data.MethodsLocation data was gathered directly from Twitter using its application programming interface (API). The maximum tweets allowed by Twitter were gathered (1% of the total tweets) over 2 separate weeks in October and November 2011. The final dataset consisted of 23.8 million tweets from 9.5 million unique users. Frequencies for each of the location options were calculated to determine the prevalence of the various location data options by region of the world, time zone, and state within the United States. Data from the US Census Bureau were also compiled to determine population proportions in each state, and Pearson correlation coefficients were used to compare each state’s population with the number of Twitter users who enable the GPS location option.ResultsThe GPS location data could be ascertained for 2.02% of tweets and 2.70% of unique users. Using a simple text-matching approach, 17.13% of user profiles in the 4 continental US time zones were able to be used to determine the user’s city and state. Agreement between GPS data and data from the text-matching approach was high (87.69%). Furthermore, there was a significant correlation between the number of Twitter users per state and the 2010 US Census state populations (r ≥ 0.97, P < .001).ConclusionsHealth researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location. Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter. Given the strong correlation between both data gathering methods, future research may consider using more qualitative approaches with higher yields, such as text mining, to acquire information about Twitter users’ geographical location.