Sample Of Tweets Research Articles

Deep learning based Election Results Prediction using Twitter Data

Abstract: This project presents an intelligent system political sentiment analysis with information from Twitter. . The system utilizes deep learning techniques, specifically a BiLSTM model featuring an attention mechanism, to predict the political affiliation (BJP or Congress) of tweets. The data preprocessing involves cleaning text, data augmentation, tokenization, and indexing. A dataset is used to train the model. consisting of tweets from both parties, getting very accurate results in predicting the sentiment of tweets. Additionally, the software offers functionality for data visualization, including displaying sample tweets, sentiment analysis results, and the winning party. Additionally, an admin page is available for further customization and management. Overall, this project the possibilities of Using deep learning to analyze sentiment of political content on social media data.

Digital Movement of Opinion #BLACKLIVESMATTER in Creating Public Opinion About Black Lives Matter

#BlackLivesMatter is still used by netizens to express opinions about the Black Lives Matter movement which is still developing today. In April 2021, the court ruled for Derek Chauvin, the police officer who shot George Floyd. In addition, there are still many shootings of black citizens in the United States and various other countries. This study aims to determine the spread of #BlackLivesMastter and public opinion on Twitter during 20 – 27 April 2021 regarding Black Lives Matter. This research uses a combination of quantitative and qualitative methods. The quantitative method is used to see the distribution of 2,500 sample tweets using Netlytic.org and Gephi. The qualitative method is conducted to analyze public opinion about Black Lives Matter. The theory used is Computer-Mediated Communication, Public Space, and Critical Discourse Analysis. The results showed that opinions were dominated by positive opinions with 71.76%, negative opinions with 13.08% and irrelevant opinions with 15.16%. The positive opinions are dominant because they have succeeded in creating empathy, anger, and criticism, have political interests, and are voiced by political elite accounts, media, NGOs, and celebrities. Negative opinions are voiced by two main political elites and Twitter users who are not well known so they are considered personal opinions. Irrelevant opinions are voiced by Twitter users in more private spaces and are used to attract public attention.

MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media

The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialects.

Sweet tweets! Evaluating a new approach for probability-based sampling of Twitter

As survey costs continue to rise and response rates decline, researchers are seeking more cost-effective ways to collect, analyze and process social and public opinion data. These issues have created an opportunity and interest in expanding the fit-for-purpose paradigm to include alternate sources such as passively collected sensor data and social media data. However, methods for accessing, sourcing and sampling social media data are just now being developed. In fact, there has been a small but growing body of literature focusing on comparing different Twitter data access methods through either the elaborate firehose or the free Twitter search or streaming APIs. Missing from the literature is a good understanding of how to randomly sample Tweets to produce datasets that are representative of the daily discourse, especially within geographical regions of interest, without requiring a census of all Tweets. This understanding is necessary for producing quality estimates of public opinion from social media sources such as Twitter. To address this gap, we propose and test the Velocity-Based Estimation for Sampling Tweets (VBEST) algorithm for selecting a probability based sample of tweets. We compare the performance of VBEST sample estimates to other methods of accessing Twitter through the Search API on the distribution of total Tweets as well as COVID-19 keyword incidence and frequency and find that the VBEST samples produce consistent and relatively low levels of overall bias compared to common methods of access through the Search API across many experimental conditions.

Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification.

There are increasingly strict regulations surrounding the purchase and use of combustible tobacco products (i.e., cigarettes); simultaneously, the use of other tobacco products, including e-cigarettes (i.e., vaping products), has dramatically increased. However, public attitudes toward vaping vary widely, and the health effects of vaping are still largely unknown. As a popular social media, Twitter contains rich information shared by users about their behaviors and experiences, including opinions on vaping. It is very challenging to identify vaping-related tweets to source useful information manually. In the current study, we proposed to develop a detection model to accurately identify vaping-related tweets using machine learning and deep learning methods. Specifically, we applied seven popular machine learning and deep learning algorithms, including Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, Multilayer Perception, Transformer Neural Network, and stacking and voting ensemble models to build our customized classification model. We extracted a set of sample tweets during an outbreak of e-cigarette or vaping-related lung injury (EVALI) in 2019 and created an annotated corpus to train and evaluate these models. After comparing the performance of each model, we found that the stacking ensemble learning achieved the highest performance with an F1-score of 0.97. All models could achieve 0.90 or higher after tuning hyperparameters. The ensemble learning model has the best average performance. Our study findings provide informative guidelines and practical implications for the automated detection of themed social media data for public opinions and health surveillance purposes.

Monitoring People’s Emotions and Symptoms from Arabic Tweets during the COVID-19 Pandemic

Coronavirus-19 (COVID-19) started from Wuhan, China, in late December 2019. It swept most of the world’s countries with confirmed cases and deaths. The World Health Organization (WHO) declared the virus a pandemic on 11 March 2020 due to its widespread transmission. A public health crisis was declared in specific regions and nation-wide by governments all around the world. Citizens have gone through a wide range of emotions, such as fear of shortage of food, anger at the performance of governments and health authorities in facing the virus, sadness over the deaths of friends or relatives, etc. We present a monitoring system of citizens’ concerns using emotion detection in Twitter data. We also track public emotions and link these emotions with COVID-19 symptoms. We aim to show the effect of emotion monitoring on improving people’s daily health behavior and reduce the spread of negative emotions that affect the mental health of citizens. We collected and annotated 5.5 million tweets in the period from January to August 2020. A hybrid approach combined rule-based and neural network techniques to annotate the collected tweets. The rule-based technique was used to classify 300,000 tweets relying on Arabic emotion and COVID-19 symptom lexicons while the neural network was used to expand the sample tweets that were annotated using the rule-based technique. We used long short-term memory (LSTM) deep learning to classify all of the tweets into six emotion classes and two types (symptom and non-symptom tweets). The monitoring system shows that most of the tweets were posted in March 2020. The anger and fear emotions have the highest number of tweets and user interactions after the joy emotion. The results of user interaction monitoring show that people use likes and replies to interact with non-symptom tweets while they use re-tweets to propagate tweets that mention any of COVID-19 symptoms. Our study should help governments and decision-makers to dispel people’s fears and discover new symptoms associated with the symptoms that were declared by the WHO. It can also help in the understanding of people’s mental and emotional issues to address them before the impact of disease anxiety becomes harmful in itself.

486. Understanding Public Perception of COVID-19 Social Distancing on Twitter

BackgroundManaging and changing public opinion and behavior are vital for social distancing to successfully slow transmission of COVID-19, preserve hospital resources, and prevent overwhelming the healthcare system’s resources. We sought to leveraging organic, large-scale discussion on Twitter about social distancing to understand public’s beliefs and opinions on this policy.MethodsBetween March 27 and April 10, 2020, we sampled 574,903 English tweets that matched the two most trending social distancing hashtags at the time, #socialdistancing and #stayathome. We used natural language processing techniques to conduct a sentiment analysis that identifies tweet polarity and emotions. We also evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and compared the sentiment by topic.ResultsThere was net positive sentiment toward both #socialdistancing and #stayathome with mean sentiment scores of 0.150 (standard deviation [SD], 0.292) and 0.144 (SD, 0.287) respectively. Tweets were also more likely to be objective (median, 0.40; IQR, 0 to 0.6) with approximately 30% of all tweets labeled as completely objective. Approximately half (50.4%) of all tweets primarily expressed joy and one-fifth expressed fear and surprise each (Figure 1). These trends correlated well with topic clusters identified by frequency including leisure activities and community support (i.e., joy), concerns about food insecurity and effects of the quarantine (i.e., fear), and unpredictability of COVID and its unforeseen implications (i.e., surprise) (Table 1).Table 1. Topic clusters identified by topic modeling. Words contributing to the model are shown in decreasing order of weighting. The topics are labeled manually based on these words. The number of tweets primarily with that topic, mean sentiment, mean subjectivity, and sample tweets are also included. Figure 1. Emotion analysis for all tweets and stratified by tweets with the hashtag #socialdistancing and #stayathome. Comparison between the two hashtags is done using Chi-squared testing. Bonferroni correction was used to define statistical significance at a threshold of p = 0.008 (0.05/n, where n = 6 since 6 comparisons were completed). ConclusionThe positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing measures in the early stages of their implementation.Disclosures All Authors: No reported disclosures

COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.

BackgroundSince the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it.ObjectiveThe aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation.MethodsThis paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined.ResultsSocial network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter.ConclusionsThe combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

Hashtagging for Health Promotion: Constructing Meaning as an #AntibioticGuardian

This paper aims at investigating how the hashtag constructs meanings in public health campaigns in Twitter. These may have peculiar features due to the simultaneous influence of the character-constrained mode of Twitter, and of the conventional set of communication activities adopted to ensure their effectiveness. The research is based on a corpus of sample tweets (N=2,438) taken from #AntibioticGuardian, a campaign led by Public Health England to reduce Antimicrobial Resistance (AMR). The lexicogrammatical realisations, evaluative language and predominant discourse functions are analysed to seek the meanings that the hashtag can construct in each of the four communication activities, namely increasing awareness and knowledge, providing cues for action, demonstrating simple skills, and changing and/or reinforcing attitudes and behaviours. The method of positive discourse analysis (PDA) used offers the advantage of highlighting how the positive change advocated takes place linguistically; micro and macro data findings reveal a significant user-generated content (UGC) pattern reflecting new ways of making meaning, which moves far beyond the more traditional tagging practices.

OMG U #Cyberbully! An Exploration of Public Discourse About Cyberbullying on Twitter.

Cyberbullying, defined as bullying that takes place using technology, includes similar tactics found in traditional bullying as well as unique approaches such as viral repetition. Nationally, prevalence rates for cyberbullying range from 10% to as high as 40% of school-aged children, depending on the definition and measurement tool applied. The current study examines public tweets with keywords and hashtags related to cyberbullying posted during May 2016, using both human evaluation and computer examination to answer the following research questions: (1) What is the sentiment of tweets using cyberbullying keywords/hashtags? (2) What is the thematic content of the tweets? (3) What is the relationship between coding by researchers versus automated coding by Linguistic Inquiry and Word Count (LIWC) software? and (4) What is the content of the URLs attached to the tweets? A unique aspect of this study is the examination of the content of URLs included in the tweets, with the finding that the majority of the accessible URL references were to material that was positively focused. The majority of sample tweets referred to a cyberbully situation, contributed to a negative atmosphere, included references to known individuals, and suggested ongoing cyberbullying events. Results from this study suggest an opportunity for researchers, educators, and public health practitioners to use discourse on social media to inform interventions, to educate and share information, and to promote social well-being and mental health.

Fake News and Indifference to Truth: Dissecting Tweets and State of the Union Addresses by Presidents Obama and Trump

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identified as being more positive in sentiment than President Obama's 2016 SOUA. This is confirmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research.

Isolating Rumors Using Sentiment Analysis

In recent days, social media has become a platform to spread false facts all the way through internet. One of the growing data analytic engine from web informal organization says, twitter has become the prime source for spreading fake news facilitating numerous perpetrators around the globe. It has turned into a competent, speedy cum effortless hotspot for news-fans to just click and forward junk data. Individuals are opting to use twitter for searching information regarding crisis circumstances and everyday occasions. In twitter, the spread of fraudulent or inaccurate information during the emergency situations will affect the individuals and public in numerous ways, but the original news is more reliable when it is declared by the news channels. So, the importance of proposed framework is categorized in three steps; Initially, Twitter Scraper is applied to scrape the vast volume of tweets and metadata from the collected set of tweets for the study on former Chief Minister (state of Tamil Nadu) death case controversy during 2016. Then the threshold value based on negative polarity of common tweets for the scraped data is calculated, once the tweeted texts are different from the threshold condition it will be automatically tagged as ‘rumor’ (negative) or else tagged as ‘non-rumor’ (positive) using sentiment classifier. Finally, the proposed model on VADER based sentiment analysis identifies the false facts. It is obtained as a result of the sample tweets regularly training and testing it on whole datasets.

Fake News and Indifference to Truth: Dissecting Tweets and State of the Union Addresses by Presidents Obama and Trump

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identified as being more positive in sentiment than President Obama's 2016 SOUA. This is confirmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research.

Fake News and Indifference to Truth: Dissecting Tweets and State of the Union Addresses by Presidents Obama and Trump

State of the Union Addresses (SOUA) by two recent US Presidents, President Obama (2016) and President Trump (2018), and a series of recent of tweets by President Trump, are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they di_er, and their potential implications for the national mood and state of the economy. President Trump's 2018 SOUA and his sample tweets are identi_ed as being more positive in sentiment than President Obama's 2016 SOUA. This is con_rmed by bootstrapped t tests and non-parametric sign tests on components of the respective sentiment scores. The issue of whether overly positive pronouncements amount to self-promotion, rather than intrinsic merit or sentiment, is a topic for future research.

Extraction of actionable information from crowdsourced disaster data.

Natural disasters cause enormous damage to countries all over the world. To deal with these common problems, different activities are required for disaster management at each phase of the crisis. There are three groups of activities as follows: (1) make sense of the situation and determine how best to deal with it, (2) deploy the necessary resources, and (3) harmonize as many parties as possible, using the most effective communication channels. Current technological improvements and developments now enable people to act as real-time information sources. As a result, inundation with crowdsourced data poses a real challenge for a disaster manager. The problem is how to extract the valuable information from a gigantic data pool in the shortest possible time so that the information is still useful and actionable. This research proposed an actionable-data-extraction process to deal with the challenge. Twitter was selected as a test case because messages posted on Twitter are publicly available. Hashtag, an easy and very efficient technique, was also used to differentiate information. A quantitative approach to extract useful information from the tweets was supported and verified by interviews with disaster managers from many leading organizations in Thailand to understand their missions. The information classifications extracted from the collected tweets were first performed manually, and then the tweets were used to train a machine learning algorithm to classify future tweets. One particularly useful, significant, and primary section was the request for help category. The support vector machine algorithm was used to validate the results from the extraction process of 13,696 sample tweets, with over 74 percent accuracy. The results confirmed that the machine learning technique could significantly and practically assist with disaster management by dealing with crowdsourced data.

The Source and Credibility of Colorectal Cancer Information on Twitter.

Despite the rapid penetration of social media in modern life, there has been limited research conducted on whether social media serves as a credible source of health information. In this study, we propose to identify colorectal cancer information on Twitter and assess its informational credibility.We collected Twitter messages containing colorectal cancer-related keywords, over a 3-month period. A review of sample tweets yielded content and user categorization schemes. The results of the sample analysis were applied to classify all collected tweets and users, using a machine learning technique. The credibility of the information in the sampled tweets was evaluated.A total of 76,119 tweets were analyzed. Individual users authored the majority of tweets (n = 68,982, 90.6%). They mostly tweeted about news articles/research (n = 16,761, 22.0%) and risk/prevention (n = 14,767, 19.4%). Medical professional users generated only 2.0% of total tweets (n = 1509), and medical institutions rarely tweeted (n = 417, 0.6%). Organizations tended to tweet more about information than did individuals (85.2% vs 63.1%; P < 0.001). Credibility analysis of medically relevant sample tweets revealed that most were medically correct (n = 1763, 84.5%). Among those, more frequently retweeted tweets contained more medically correct information than randomly selected tweets (90.7% vs 83.2%; P < 0.01).Our results demonstrate an interest in and an engagement with colorectal cancer information from a large number and variety of users. Coupled with the Internet's potential to increase social support, Twitter may contribute to enhancing public health and empowering users, when used with proper caution.

Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter.

BackgroundElectronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter.ObjectiveThe objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data.MethodsA 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends.ResultsThe analysis revealed an increase in e-cigarette–related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined.ConclusionsEveryday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology.