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

A primer to traction force microscopy

Traction force microscopy (TFM) has emerged as a versatile technique for the measurement of single-cell-generated forces. TFM has gained wide use among mechanobiology laboratories, and several variants of the original methodology have been proposed. However, issues related to the experimental setup and, most importantly, data analysis of cell traction datasets may restrain the adoption of TFM by a wider community. In this review, we summarize the state of the art in TFM-related research, with a focus on the analytical methods underlying data analysis. We aim to provide the reader with a friendly compendium underlying the potential of TFM and emphasizing the methodological framework required for a thorough understanding of experimental data. We also compile a list of data analytics tools freely available to the scientific community for the furtherance of knowledge on this powerful technique

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

Temperature-sensitive poly(vinyl alcohol)/poly(methacrylate-co-N-isopropyl acrylamide) microgels for doxorubicin delivery

Microgels based on poly(vinyl alcohol), PVA, grafted with methacrylate side chains, MA, incorporating N-isopropylacrylamide, NiPAAm, monomer, were prepared by water-in-water emulsion polymerization method. These systems exhibit a spherical shape and a volume-phase transition, that is, shrinking, below physiological temperature. The behavior of these microgels were studied with respect to their average size and size distribution, swelling, and release properties. It was observed that the stirring speed is a key parameter for controlling the amount of incorporated NiPAAm, the particle size and the sharpness of the volume-phase transition. The volume-phase transition temperature, VPPT, of the microgels was evaluated around 38 and 34 T for microgels with a NiPAAm/methacrylate molar ratio of 0.8 and 2.4, respectively. Water uptake increased with the amount of NiPAAm monomer present in the polymer network. In vitro biocompatibility of microgels was assessed with respect to NIH3T3 mouse fibroblasts. O-Succinoylated microgels were loaded with doxorubicin by exploiting the favorable electrostatic interaction between negatively charged microgel surface and positively charged doxorubicin. The drug release was influenced by the microgels surface/volume ratio. At physiological temperatures, above the VPTT exhibited by these systems, the release was enhanced by the specific area increase. This study provides the background for the design of an injectable device suitable for the controlled delivery of doxorubicin based on the volume-phase transition of microgels

Plasma control of morpho-dimensional selectivity of hematite nanostructures

Highly controllablefabrication of the nanowire, nanocone, and mixed nanowire/nanowall arrays of iron oxide (hematite, α-Fe2O3) nanostructures in a simple, environment-friendly process is achieved by exposing the metal foils to low-temperature oxygen plasmas. Very dense forests of thin (≈50 nm) and long (up to several μm) nanowires are grown on the electrically biased substrates, whereas the use of the electrically insulated substrate resulted in the formation of a mixed array of nanowires and nanowalls. The proposed mechanism of the nanostructure growth is supported by the numerical simulations demonstrating the key role of the plasma environment in the growth morphology selection