Precocious identification of popular topics on Twitter with the employment of predictive clustering
The present paper outlines a novel approach to predict popularity of topics for social network Twitter; the method is designed to identify precociously the topics able to demonstrate “explosive” growth in popularity. First of all, the predictive clustering method ascertains real (not written in hash-tags!) topics of tweets and then predicts popularity rates for the topics. The same clustering algorithm is employed both to ascertain the real topic of a message and to cluster segments of time series (in order to predict topics popularity), namely, maximum likelihood adaptive neural system based upon modelling field theory. In the course of wide-ranging simulation, typical variants of “pre-explosive” dynamics were revealed; some of them were turned out to be equal to heuristic techniques to predict topics popularity well known for PR community collaborating with the network (“crab,” “Pesavento’s butterfly,” etc.).
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.
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).
Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. e model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.
We consider certain spaces of functions on the circle, which naturally appear in harmonic analysis, and superposition operators on these spaces. We study the following question: which functions have the property that each their superposition with a homeomorphism of the circle belongs to a given space? We also study the multidimensional case.
We consider the spaces of functions on the m-dimensional torus, whose Fourier transform is p -summable. We obtain estimates for the norms of the exponential functions deformed by a C1 -smooth phase. The results generalize to the multidimensional case the one-dimensional results obtained by the author earlier in “Quantitative estimates in the Beurling—Helson theorem”, Sbornik: Mathematics, 201:12 (2010), 1811 – 1836.
We consider the spaces of function on the circle whose Fourier transform is p-summable. We obtain estimates for the norms of exponential functions deformed by a C1 -smooth phase.
I give the explicit formula for the (set-theoretical) system of Resultants of m+1 homogeneous polynomials in n+1 variables