Selfish mining, originally discovered by Eyal et al. [9], is a well-known attack where selfish miner, under certain conditions, can gain disproportionate share of reward deviating from the honest behavior. In this paper, we expand mining strategy space to include novel "stubborn" strategies that, for large range parameters, earn miner more revenue. Consequently, show that not (in general) optimal. Further, how further amplify its non-trivially composing attacks with network-level eclipse...

Weighted signed networks (WSNs) are in which edges labeled with positive and negative weights. WSNs can capture like/dislike, trust/distrust, other social relationships between people. In this paper, we consider the problem of predicting weights such networks. We propose two novel measures node behavior: goodness a intuitively captures how much is liked/trusted by nodes, while fairness fair rating nodes' likeability or trust level. provide axioms that these notions need to satisfy show past...

Rating platforms enable large-scale collection of user opinion about items(e.g., products or other users). However, untrustworthy users give fraudulent ratings for excessive monetary gains. In this paper, we present REV2, a system to identify such users. We propose three interdependent intrinsic quality metrics---fairness user, reliability rating and goodness product. The fairness quantify the trustworthiness rating, respectively, quantifies Intuitively, is fair if it provides reliable...

Wikipedia is a major source of information for many people. However, false on raises concerns about its credibility. One way in which may be presented the form hoax articles, i.e., articles containing fabricated facts nonexistent entities or events. In this paper we study by focusing that have been created throughout history. We make several contributions. First, assess real-world impact measuring how long they survive before being debunked, pageviews receive, and heavily are referred to...

Modeling sequential interactions between users and items/products is crucial in domains such as e-commerce, social networking, education. Representation learning presents an attractive opportunity to model the dynamic evolution of items, where each user/item can be embedded a Euclidean space its modeled by embedding trajectory this space. However, existing methods generate embeddings only when take actions do not explicitly future Here we propose JODIE, coupled recurrent neural network that...

False information can be created and spread easily through the web social media platforms, resulting in widespread real-world impact. Characterizing how false proliferates on platforms why it succeeds deceiving readers are critical to develop efficient detection algorithms tools for early detection. A recent surge of research this area has aimed address key issues using methods based feature engineering, graph mining, modeling. Majority primarily focused two broad categories information:...

Citations have long been used to characterize the state of a scientific field and identify influential works. However, writers use citations for different purposes, this varied purpose influences uptake by future scholars. Unfortunately, our understanding how scholars frame has limited small-scale manual citation analysis individual papers. We perform largest behavioral study date, analyzing works their contributions through types framing affects as whole. introduce new dataset nearly 2,000...

Users organize themselves into communities on web platforms. These can interact with one another, often leading to conflicts and toxic interactions. However, little is known about the mechanisms of interactions between how they impact users.

Misinformation about the COVID-19 pandemic proliferated widely on social media platforms during course of health crisis. Experts have speculated that consuming misinformation online can potentially worsen mental individuals, by causing heightened anxiety, stress, and even suicidal ideation. The present study aims to quantify causal relationship between sharing misinformation, a strong indicator experiencing exacerbated anxiety. We conduct large-scale observational spanning over 80 million...

The study of network robustness is a critical tool in the characterization and sense making complex interconnected systems such as infrastructure, communication social networks. While significant research has been conducted these areas, gaps surveying literature still exist. Answers to key questions are currently scattered across multiple scientific fields numerous papers. In this survey, we distill findings domains provide researchers crucial access important information by(1) summarizing...

In online discussion communities, users can interact and share information opinions on a wide variety of topics. However, some may create multiple identities, or sockpuppets, engage in undesired behavior by deceiving others manipulating discussions. this work, we study sockpuppetry across nine show that sockpuppets differ from ordinary terms their posting behavior, linguistic traits, as well social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more...

The spread of COVID-19 has sparked racism and hate on social media targeted towards Asian communities. However, little is known about how racial spreads during a pandemic the role counterspeech in mitigating this spread. In work, we study evolution anti-Asian speech through lens Twitter. We create COVID-HATE, largest dataset spanning 14 months, containing over 206 million tweets, network with 127 nodes. By creating novel hand-labeled 3,355 train text classifier to identify hateful tweets...

We study the problem of detecting vandals on Wikipedia before any human or known vandalism detection system reports flagging potential so that such users can be presented early to administrators. leverage multiple classical ML approaches, but develop 3 novel sets features. Our Vandal Behavior (WVB) approach uses a set user editing patterns as features classify some vandals. Transition Probability Matrix (WTPM) derived from transition probability matrix and then reduces it via neural net...

Fact checking by professionals is viewed as a vital defense in the fight against misinformation. While fact important and its impact has been significant, checks could have limited visibility may not reach intended audience, such those deeply embedded polarized communities. Concerned citizens (i.e., crowd), who are users of platforms where misinformation appears, can play crucial role disseminating fact-checking information countering spread To explore if this case, we conduct data-driven...

The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form first line defense by fact-checking popular false claims, they do not engage directly in conversations with spreaders. On other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter – recent research has shown that 96% counter-misinformation responses are made users. However, also found 2/3 times, these rude lack evidence. This...

Multidimensional visible spectroscopy using pulse shaping to produce pulses with stable controllable phases and delays has emerged as an elegant tool acquire electronic spectra faster greatly reduced instrumental data processing errors. Recent migration of this approach acousto-optic modulator (AOM) the mid-infrared region proved useful for acquiring two dimensional infrared (2D IR) vibrational echo spectra. The measurement spectral diffusion in 2D IR experiments hinges on obtaining accurate...

Real-world graphs such as social networks, communication and rating networks are constantly evolving over time. Many deep learning architectures have been developed to learn effective node representations using both graph structure dynamics. While being crucial for practical applications, the robustness of these representation learners dynamic in presence adversarial attacks is highly understudied. In this work, we design a novel attack on discrete-time models where desire perturb input...