COVID-19 Vaccines: Characterizing Misinformation Campaigns and Vaccine Hesitancy on Twitter

Vaccine hesitancy and misinformation on social media has increased concerns about COVID-19 vaccine uptake required to achieve herd immunity and overcome the pandemic. However anti-science and political misinformation and conspiracies have been rampant throughout the pandemic. For COVID-19 vaccines, we investigate misinformation and conspiracy campaigns and their characteristic behaviours. We identify whether coordinated efforts are used to promote misinformation in vaccine related discussions, and find accounts coordinately promoting a `Great Reset' conspiracy group promoting vaccine related misinformation and strong anti-vaccine and anti-social messages such as boycott vaccine passports, no lock-downs and masks. We characterize other misinformation communities from the information diffusion structure, and study the large anti-vaccine misinformation community and smaller anti-vaccine communities, including a far-right anti-vaccine conspiracy group. In comparison with the mainstream and health news, left-leaning group, which are more pro-vaccine, the right-leaning group is influenced more by the anti-vaccine and far-right misinformation/conspiracy communities. The misinformation communities are more vocal either specific to the vaccine discussion or political discussion, and we find other differences in the characteristic behaviours of different communities. Lastly, we investigate misinformation narratives and tactics of information distortion that can increase vaccine hesitancy, using topic modeling and comparison with reported vaccine side-effects (VAERS) finding rarer side-effects are more frequently discussed on social media

Chirality driven topological electronic structure of DNA-like materials

Topological aspects of the geometry of DNA and similar chiral molecules have received a lot of attention, while the topology of their electronic structure is less explored. Previous experiments have revealed that DNA can efficiently filter spin-polarized electrons between metal contacts, a process called chiral-induced spin-selectivity (CISS). However, the underlying correlation between chiral structure and electronic spin remains elusive. In this work, we reveal an orbital texture in the band structure, a topological characteristic induced by the chirality. We find that this orbital texture enables the chiral molecule to polarize the quantum orbital. This orbital polarization effect (OPE) induces spin polarization assisted by the spin-orbit interaction from a metal contact and leads to magnetorestistance and chiral separation. The orbital angular momentum of photoelectrons also plays an essential role in related photoemission experiments. Beyond CISS, we predict that OPE can induce spin-selective phenomena even in achiral but inversion-breaking materials.Comment: 24 pages, 4 figures, and Supplementary Material

The Indirect Effects of Trading Restrictions: Evidence from a Quasi-Natural Experiment

Stock market trading restrictions directly affect stock prices and liquidity via constraints on investors’ transactions. They also have indirect effects by altering the information environment. We isolate these indirect effects by analyzing the effect of stock market restrictions on the corporate bond market. Using the staggered relaxation of the restrictions on margin trading and short selling in the Chinese stock market as a quasi-natural experiment, we find that the relaxation of these restrictions on a firm’s stock reduces the credit spread of its corporate bond. This effect is more pronounced for firms with more opaque information or lower credit ratings

Interpretable Outlier Summarization

Outlier detection is critical in real applications to prevent financial fraud, defend network intrusions, or detecting imminent device failures. To reduce the human effort in evaluating outlier detection results and effectively turn the outliers into actionable insights, the users often expect a system to automatically produce interpretable summarizations of subgroups of outlier detection results. Unfortunately, to date no such systems exist. To fill this gap, we propose STAIR which learns a compact set of human understandable rules to summarize and explain the anomaly detection results. Rather than use the classical decision tree algorithms to produce these rules, STAIR proposes a new optimization objective to produce a small number of rules with least complexity, hence strong interpretability, to accurately summarize the detection results. The learning algorithm of STAIR produces a rule set by iteratively splitting the large rules and is optimal in maximizing this objective in each iteration. Moreover, to effectively handle high dimensional, highly complex data sets which are hard to summarize with simple rules, we propose a localized STAIR approach, called L-STAIR. Taking data locality into consideration, it simultaneously partitions data and learns a set of localized rules for each partition. Our experimental study on many outlier benchmark datasets shows that STAIR significantly reduces the complexity of the rules required to summarize the outlier detection results, thus more amenable for humans to understand and evaluate, compared to the decision tree methods

Price of Stability in Quality-Aware Federated Learning

Federated Learning (FL) is a distributed machine learning scheme that enables clients to train a shared global model without exchanging local data. The presence of label noise can severely degrade the FL performance, and some existing studies have focused on algorithm design for label denoising. However, they ignored the important issue that clients may not apply costly label denoising strategies due to them being self-interested and having heterogeneous valuations on the FL performance. To fill this gap, we model the clients' interactions as a novel label denoising game and characterize its equilibrium. We also analyze the price of stability, which quantifies the difference in the system performance (e.g., global model accuracy, social welfare) between the equilibrium outcome and the socially optimal solution. We prove that the equilibrium outcome always leads to a lower global model accuracy than the socially optimal solution does. We further design an efficient algorithm to compute the socially optimal solution. Numerical experiments on MNIST dataset show that the price of stability increases as the clients' data become noisier, calling for an effective incentive mechanism.Comment: Accepted to IEEE GLOBECOM 202