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Social media has become a powerful and efficient platform for information diffusion. The increasing pervasiveness of social media use, however, has brought about the problems of fraudulent accounts that are intended to diffuse misinformation or malicious contents. Twitter recently released comprehensive archives of fraudulent tweets that are possibly connected to a propaganda effort of Internet Research Agency (IRA) on the 2016 U.S. presidential election. To understand information diffusion in fraudulent networks, we analyze structural properties of the IRA retweet network, and develop deep neural network models to detect fraudulent tweets. The structure analysis reveals key characteristics of the fraudulent network. The experiment results demonstrate the superior performance of the deep learning technique to a traditional classification method in detecting fraudulent tweets. The findings have potential implications for curbing online misinformation.

Despite the tremendous role of online consumer reviews (OCRs) in facilitating consumer purchase decision making, the potential inconsistency between product ratings and review content could cause the uncertainty and confusions of prospect consumers toward a product. This research is aimed to investigate such inconsistency so as to better assist potential consumers with making purchase decisions. First, this study extracted a reviewer’s sentiments from review text via sentiment analysis. Then, it examined the correlation and inconsistency between product ratings and review sentiments via Pearson correlation coefficients (PCC) and box plots. Next, we compared such inconsistency patterns between fake and authentic reviews. Based on an analysis of 24,539 Yelp reviews, we find that although the ratings and sentiments are highly correlated, the inconsistency between the two is more salient in fake reviews than in authentic reviews. The comparison also reveals different inconsistency patterns between the two types of reviews.

Introduction:Current brain-computer interfaces (BCIs) primarily rely on visual feedback. However, visual feedback may not be sufficient for applications such as movement restoration, where somatosensory feedback plays a crucial role. For electrocorticography (ECoG)-based BCIs, somatosensory feedback can be elicited by cortical surface electro-stimulation [1]. However, simultaneous cortical stimulation and recording is challenging due to stimulation artifacts. Depending on the orientation of stimulating electrodes, their distance to the recording site, and the stimulation intensity, these artifacts may overwhelm the neural signals of interest and saturate the recording bioamplifiers, making it impossible to recover the underlying information [2]. To understand how these factors affect artifact propagation, we performed a preliminary characterization of ECoG signals during cortical stimulation.Materials/Methods/ResultsECoG electrodes were implanted in a 39-year old epilepsy patient as shown in Fig. 1. Pairs of adjacent electrodes were stimulated as a part of language cortical mapping. For each stimulating pair, a charge-balanced biphasic square pulse train of current at 50 Hz was delivered for five seconds at 2, 4, 6, 8 and 10 mA. ECoG signals were recorded at 512 Hz. The signals were then high-pass filtered (≥1.5 Hz, zero phase), and the 5-second stimulation epochs were segmented. Within each epoch, artifact-induced peaks were detectedmore »for each electrode, except the stimulating pair, where signals were clipped due to amplifier saturation. These peaks were phase-locked across electrodes and were 20 ms apart, thus matching the pulse train frequency. The response was characterized by calculating the median peak within the 5-second epochs. Fig. 1 shows a representative response of the right temporal grid (RTG), with the stimulation channel at RTG electrodes 14 and 15. It also shows a hypothetical amplifier saturation contour of an implantable, bi-directional, ECoG-based BCI prototype [2], assuming the supply voltage of 2.2 V and a gain of 66 dB. Finally, we quantify the worstcase scenario by calculating the largest distance between the saturation contour and the midpoint of each stimulating channel.Discussion:Our results indicate that artifact propagation follows a dipole potential distribution with the extent of the saturation region (the interior of the white contour) proportional to the stimulation amplitude. In general, the artifacts propagated farthest when a 10 mA current was applied with the saturation regions extending from 17 to 32 mm away from the midpoint of the dipole. Consistent with the electric dipole model, this maximum spread happened along the direction of the dipole moment. An exception occurred at stimulation channel RTG11-16, for which an additional saturation contour emerged away from the dipole contour (not shown), extending the saturation region to 41 mm. Also, the worst-case scenario was observed at 6 mA stimulation amplitude. This departure could be a sign of a nonlinear, switch-like behavior, wherein additional conduction pathways could become engaged in response to sufficiently high stimulation.Significance:While ECoG stimulation is routinely performed in the clinical setting, quantitative studies of the resulting signals are lacking. Our preliminary study demonstrates that stimulation artifacts largely obey dipole distributions, suggesting that the dipole model could be used to predict artifact propagation. Further studies are necessary to ascertain whether these results hold across other subjects and combinations of stimulation/recording grids. Once completed, these studies will reveal practical design constraints for future implantable bi-directional ECoG-based BCIs. These include parameters such as the distances between and relative orientations of the stimulating and recording electrodes, the choice of the stimulating electrodes, the optimal placement of the reference electrode, and the maximum stimulation amplitude. These findings would also have important implications for the design of custom, low-power bioamplifiers for implantable bi-directional ECoG-based BCIs.References:[1] Hiremath, S. V., et al. "Human perception of electrical stimulation on the surface of somatosensory cortex." PloS one 12.5 (2017): e0176020.[2] Rouse, A. G., et al. "A chronic generalized bi-directional brain-machine interface." Journal of Neural Engineering 8.3 (2011): 036018« less