Analiza raspoloženja tvitova predsjednika Trumpa: od pobjede na izborima do borbe protiv COVID-19

Twitter, as one of the popular social networks today and big data generator, can affect and change the public discourse, so political candidates are using it extensively as the vehicle for attracting and keeping their followers. Since Donald Trump\u27s 2016 presidency election, his Twitter account with millions of followers has become an important subject for various statistical analyses, mostly because of his controversy. Therefore, this paper uses sentiment analysis of a large set of his tweets to explore his influence, as well the set of affective and cognitive aspects of his messages. The results of this analysis indicate what kind of findings in political domain can be recognized from tweets, and how they can be interpreted.Twitter, kao jedna od popularnih društvenih mreža današnjice i generator velikih podataka, može utjecati i mijenjati javni diskurs, pa ga politički kandidati intenzivno koriste kao sredstvo za privlačenje i održavanje pinga svojih pratitelja. Od predsjedničkih izbora Donalda Trumpa 2016., njegov Twitter račun s milijunima pratitelja postao je važan predmet raznih statističkih analiza, ponajviše zbog njegove kontroverze. Stoga ovaj rad koristi analizu sentimenta velikog skupa njegovih tweetova kako bi istražio njegov utjecaj, kao i skup afektivnih i kognitivnih aspekata njegovih poruka. Rezultati ove analize ukazuju na to kakva se saznanja u političkoj domeni mogu prepoznati iz tweetova i kako ih se može interpretirati

Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice - referred to as cashtag piggybacking - perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market

Beyond Big Bird, Binders, and Bayonets: Humor and Visibility Among Connected Viewers of the 2012 US Presidential Debates

During the 2012 US presidential debates, more than five million connected viewers turned to social media to respond to the broadcast and talk politics with one another. Using a mixed-methods approach, this study examines the prevalence of humor and its relationship to visibility among connected viewers live-tweeting the debates. Based on a content analysis of tweets and accounts, we estimate that approximately one-fifth of the messages sent during the debates consisted of strictly humorous content. Using retweet frequency as a proxy for visibility, we found a positive relationship between the use of humor and the visibility of individual tweets. Not only was humor widespread in the discourse of connected viewers, but humorous messages enjoyed greater overall visibility. These findings suggest a strategic use of humor by political actors seeking greater shares of attention on social media

Common Core State Standards on Twitter: Public Sentiment and Opinion Leaders

The purpose of this study is to examine the public opinion on the Common Core State Standards (CCSS) on Twitter. Using Twitter API, we collected the tweets containing the hashtags #CommonCore and #CCSS for 12 months from 2014 to 2015. A Common Core corpus was created by compiling all the collected 660,051 tweets. The results of sentiment analysis suggest Twitter users expressed overwhelmingly negative sentiment towards the CCSS in all 50 states. Five topic clusters were detected by cluster analysis of the hashtag co-occurrence network. We also found that most of the opinion leaders were those who expressed negative sentiment towards the CCSS on Twitter. This study for the first time demonstrates how text mining techniques can be applied to education policy research, laying the foundation for real-time analytics of public opinion on education policies, thereby informing policymaking and implementation

Liquidity Risk and Investors' Mood: Linking the Financial Market Liquidity to Sentiment Analysis through Twitter in the S&P500 Index

[EN] Microblogging services can enrich the information investors use to make financial decisions on the stock markets. As liquidity has immediate consequences for a trader's movements, this risk is an attractive area of interest for both academics and those who participate in the financial markets. This paper focuses on market liquidity and studies the impact on liquidity and trading costs of the popular Twitter microblogging service. Sentiment analysis extracted from Twitter and different popular liquidity measures were gathered to analyze the relationship between liquidity and investors' opinions. The results, based on the analysis of the S&P 500 Index, found that the investors' mood had little influence on the spread of the index.Guijarro, F.; Moya Clemente, I.; Saleemi, J. (2019). Liquidity Risk and Investors' Mood: Linking the Financial Market Liquidity to Sentiment Analysis through Twitter in the S&P500 Index. Sustainability. 11(24):1-13. https://doi.org/10.3390/su11247048S113112

Commentary - Much ado about something else. Donald Trump, the US stock market, and the public interest ethics of social media communication

Trump’s use of social media during both his presidential campaign and term questions the principle that institutional responsibility in the digital realm implies treating the infosphere as a commons. We discuss the implications for the functioning of the stock market and the emerging public interest ethical issues related to the breakdown of this principle

Diffusion of Falsehoods on Social Media

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher for fake news, novel tweets, and tweets with negative sentiment and lower lexical structure. In addition, our results show that the impacts of sentiment are opposite for fake news versus real news. We also find that tweets on the environment have a lower retweet count than the baseline religious news and real social news tweets are shared more often than fake social news. Furthermore, our studies show the counter intuitive nature of current correction endeavors by FEMA and other fact checking organizations in combating falsehoods. Specifically, we show that even though fake news causes an increase in correction messages, they influenced the propagation of falsehoods. Finally our empirical results reveal that correction messages, positive tweets and emotionally charged tweets morph faster. Furthermore, we show that tweets with positive sentiment or are emotionally charged morph faster over time. Word count and past morphing history also positively affect morphing behavior

Mental health discourse and social media: Which mechanisms of cultural power drive discourse on Twitter

The global burden of mental health disorders has increased steadily during the past decade. Today, mental illness is the leading cause of total years lived with disability. At the same time, global mental health policies and budgets fall short of addressing the societal burden as mental health discourse languishes in the shadows due to stigma. As social media have become an increasingly popular source of i