Investigation into the Regular and Chaotic States of Twitter
The present paper is devoted to the investigation into the nonlinear dynamics of Twitter. A new model of Twitter as a thermodynamic non-equilibrium system is suggested. Dynamic variables of such system are represented by the variations of tweet/retweet number and instantaneous diversity between the densities of population on different levels around the equilibrium values. Regular and chaotic states of networks are described. It is pointed out, that the system is in a condition of an asymptotically stable equilibrium when the intensity values of an external information are small (the number of tweets eventually tends to its equilibrium value). If the intensity values of external information exceed the critical value, then the chaotic oscillations of tweets are to be observed. We have made the calculations of the correlation dimension and embedding dimension for the dynamics of the 10 most popular @ (TOP 100 by data of Twitter Counter). The results show, that all observed time series have clearly deﬁned chaotic dynamical nature.
The question about possibilities to use Twitter users’ moods to increase accuracy of stock price movement prediction draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ mood to improve accuracy of predictions for Gold and Silver stock market prices. We used a lexicon-based approach to categorize the mood of users expressed in Twitter posts and to analyze 755 million tweets downloaded from February 13, 2013 to September 29, 2013. As forecasting technique, we select Support Vector Machines (SVM), which have shown the best performance. Results of SVM application to prediction the stock market prices for Gold and Silver are discussed.
Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users’ moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.
The research deals with the construction, implementation and analysis of the model of the non-equilibrium financial market using econophysical approach and the theory of nonlinear oscillations. We used the scaled variation of supply and demand prices and elasticity of these two variables as dynamic variables in the simulation of the non-equilibrium financial market. View of the dynamic variables data was determined based on the strength of econophysical prerequisites using the model of hydrodynamic type. As a result, we found that the non-equilibrium market can be described with a good degree of accuracy with oscillator models with nonlinear rigidity and a self-oscillating system with inertial self-excitation. The most important states of model of oscillation non-equilibrium model of the market were found, including the appearance of chaos and its mechanisms. We have made the calculations of the correlation dimension for the financial time series. The results show that all observed time series have a clearly defined chaotic dynamic nature.
Research question: This paper investigates how football sponsorship influences the financial performance of sponsors. We suggest a new instrumental variable to avoid endogeneity.
Research methods: We use an instrumental variable regression framework combined with a fixed effects model. The number of tweets containing both team and sponsor names are collected to use as the instrumental variable.
Results and findings: We analyze top European leagues. Our results show that football sponsorship is more charity than commercial investment. The analysis of determinants of becoming a sponsor and sponsorship amount shows that companies owned by individuals are more likely to become a sponsor.
Implications: Shareholders should be aware of sponsorship deals, and senior management should analyze the financial assumptions of such projects carefully.
According to a number of scholars, Twitter possesses big potential to become a “crossroads of discourses” due to its openness, de-hierarchization, and spontaneity (Miller, 2010; Shirky, 2008). At the same time, substantial criticism has risen towards political and deliberative efficacy of Twitter (Fuchs, 2014). The authors aim at analyzing the features of the Twitter-based agenda setting within the hybrid media system in Russia (Chadwick, 2013; Bodrunova and Litvinenko, 2013a). The research question is whether the use of Twitter in the Russian socio-political context potentially leads to the formation of the “crossroads of opinions” or, in contrast, to closing-up of political discussion and to further fragmentation of public discourse. The research focuses on structural and content aspects of discussion on anti-migrant bashings in Biryulyovo (Moscow) that happened in October 2013. Our research methods include automated vocabulary-based web crawling, word frequency analysis, manual coding of tweets, and interpretation of statistical data. Preliminary results suggest an unexpectedly high level of mediatization of the discussion; the hypothesis about the “crossroads” nature of the discussion on the Russian Twitter seems to be proven, which makes this platform differ from the Russian Facebook where, according to another recent study (Bodrunova and Litvinenko, 2013b), political discussions are held mostly in closed-up communicative milieus, or “echo chambers” (Sunstein, 2007).