The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index.We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Twitter-based analysis of the dynamics of collective attention to political parties

Large-scale data from social media have a significant potential to describe complex phenomena in the real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the election outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media

Carbon nanofiber growth in plasma-enhanced chemical vapor deposition

A theoretical model to describe the plasma-assisted growth of carbon nanofibers (CNFs) is proposed. Using the model, the plasma-related effects on the nanofiber growth parameters, such as the growth rate due to surface and bulk diffusion, the effective carbon flux to the catalyst surface, the characteristic residence time and diffusion length of carbon atoms on the catalyst surface, and the surface coverages, have been studied. The dependence of these parameters on the catalyst surface temperature and ion and etching gas fluxes to the catalyst surface is quantified. The optimum conditions under which a low-temperature plasma environment can benefit the CNF growth are formulated. These results are in good agreement with the available experimental data on CNF growth and can be used for optimizing synthesis of related nanoassemblies in low-temperature plasma-assisted nanofabrication. © 2008 American Institute of Physics

Toward a better understanding of emotional dynamics on Facebook

On online social media users tend to aggregate in echo chambers, where they shape and reinforce their worldview by discussing and interacting with like-minded people. Such a scenario fosters misinformation spreading, which may influence public opinion. To determine the main factors behind narratives’ emergence, characterizing polarization dynamics and users’ emotional response to social contents is, thus, crucial. In this paper, we address such a challenge by looking at two different and contrasting narratives, science and conspiracy. We introduce a new metric, the bipolarity, and show how it can help in finding non-trivial proxies of the debate’s polarization. Our approach may provide interesting insights for a better understanding of both emotional and polarization dynamics on online social media

Constraintpropagation in Qualitative Modelling: Domain Variables Improve Diagnostic Efficiency

This paper shows how a specific constraint propagation technique - namely domain variables - can speed up qualitative diagnosis considerably. We are using the KARDIO system, a qualitative simulation model of the electrical activity of the heart, to exemplify our points. Furthermore we describe how the domain handling mechanism itself can be implemented in PROLOG efficiently. For a class of applications, where the constraint solver only performs a minor part of the computation our approach is comparable to or better than specialised constraint logic programming systems with regard to overall runtime. Additionally we gain the benefit of being able to specify all of the system in a single language. Keywords: Constraint Propagation, Unification, Qualitative Modelling, Implementation. 1 Introduction This paper shows how a specific constraint propagation technique - namely domain variables [1] - can speed up qualitative diagnosis considerably. We are using the KARDIO system [2], a quali..