arXiv subjects

Ryota Kobayashi

Publications and source records attributed to Ryota Kobayashi.

24 records · Page 2Linked to original sources

arXiv2021

Estimating Community Feedback Effect on Topic Choice in Social Media with Predictive Modeling

Social media users post content on various topics. A defining feature of social media is that other users can provide feedback -- called community feedback -- to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback influences the choice of topics on which a social media user posts. However, it is challenging to test this hypothesis as user heterogeneity and external confounders complicate measuring the feedback effect. Here, we investigate this hypothesis with a predictive approach based on an interpretable model of an author's decision to continue the topic of their previous post. We explore the confounding factors, including author's topic preferences and unobserved external factors such as news and social events, by optimizing the predictive accuracy. This approach enables us to identify which users are susceptible to community feedback. Overall, we find that 33\% and 14\% of active users in Reddit and Twitter, respectively, are influenced by community feedback. The model suggests that this feedback alters the probability of topic continuation up to 14\%, depending on the user and the amount of feedback.

cs.SI↗

arXiv2017

Mn$_2$VAl Heusler alloy thin films: Appearance of antiferromagnetism and an exchange bias in a layered structure with Fe

Mn$_2$VAl Heusler alloy films were epitaxially grown on MgO(100) single crystal substrates by means of ultra-high-vacuum magnetron sputtering. A2 and L2$_1$ type Mn$_2$VAl order was controlled by the deposition temperatures. A2-type Mn$_2$VAl films showed no spontaneous magnetization and L2$_1$-type Mn$_2$VAl films showed ferrimagnetic behavior with a maximum saturation magnetization of 220 emu/cm$^3$ at room temperature. An antiferromagnetic reflection was observed with neutron diffraction at room temperature for an A2-type Mn$_2$VAl film deposited at 400$^\circ$C. A bilayer sample of the antiferromagnetic A2 Mn$_2$VAl and Fe showed an exchange bias of 120 Oe at 10 K.

physics.app-ph↗

arXiv2016

Input-output relationship in social communications characterized by spike train analysis

We study the dynamical properties of human communication through different channels, i.e., short messages, phone calls, and emails, adopting techniques from neuronal spike train analysis in order to characterize the temporal fluctuations of successive inter-event times. We first measure the so-called local variation (LV) of incoming and outgoing event sequences of users, and find that these in- and out- LV values are positively correlated for short messages, and uncorrelated for phone calls and emails. Second, we analyze the response-time distribution after receiving a message to focus on the input-output relationship in each of these channels. We find that the time scales and amplitudes of response are different between the three channels. To understand the impacts of the response-time distribution on the correlations between the LV values, we develop a point process model whose activity rate is modulated by incoming and outgoing events. Numerical simulations of the model indicate that a quick response to incoming events and a refractory effect after outgoing events are key factors to reproduce the positive LV correlations.

physics.soc-ph↗

arXiv2016

TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics

Online social networking services allow their users to post content in the form of text, images or videos. The main mechanism driving content diffusion is the possibility for users to re-share the content posted by their social connections, which may then cascade across the system. A fundamental problem when studying information cascades is the possibility to develop sound mathematical models, whose parameters can be calibrated on empirical data, in order to predict the future course of a cascade after a window of observation. In this paper, we focus on Twitter and, in particular, on the temporal patterns of retweet activity for an original tweet. We model the system by Time-Dependent Hawkes process (TiDeH), which properly takes into account the circadian nature of the users and the aging of information. The input of the prediction model are observed retweet times and structural information about the underlying social network. We develop a procedure for parameter optimization and for predicting the future profiles of retweet activity at different time resolutions. We validate our methodology on a large corpus of Twitter data and demonstrate its systematic improvement over existing approaches in all the time regimes.

cs.SI↗

arXiv2015

Fluctuation scaling in neural spike trains

Fluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power function relationships between the mean and variance of either inter-event intervals or counting statistics, depending on measurement variables. In this article, fluctuation scaling has been formulated for a series of events in which scaling laws in the inter-event intervals and counting statistics were related. We have considered the first-passage time of an Ornstein-Uhlenbeck process and used a conductance-based neuron model with excitatory and inhibitory synaptic inputs to demonstrate the emergence of fluctuation scaling with various exponents, depending on the input regimes and the ratio between excitation and inhibition. Furthermore, we have discussed the possible implication of these results in the context of neural coding.

physics.data-an↗

arXiv2008

Influence of firing mechanisms on gain modulation

We studied the impact of a dynamical threshold on the f-I curve-the relationship between the input and the firing rate of a neuron-in the presence of background synaptic inputs. First, we found that, while the leaky integrate-and-fire model cannot reproduce the f-I curve of a cortical neuron, the leaky integrate-and-fire model with dynamical threshold can reproduce it very well. Second, we found that the dynamical threshold modulates the onset and the asymptotic behavior of the f-I curve. These results suggest that a cortical neuron has an adaptation mechanism and that the dynamical threshold has some significance for the computational properties of a neuron.

q-bio.NC↗