OpenRaiser/Intern-Atlas huggingface dataset:
Towards Understanding Cryptocurrency Derivatives: A Case Study of BitMEX
Authors: Kyle Soska, Jin-Dong Dong, Alex Khodaverdian, Ariel Zetlin-Jones, Bryan R. Routledge, Nicolas Christin
Venue: WWW
Year: 2021

Since 2018, the cryptocurrency trading landscape has evolved from a collection of spot markets (fiat for cryptocurrency) to a hybrid ecosystem featuring complex and popular derivatives products. In this paper we explore this new paradigm through a study of BitMEX, one of the first and most successful derivatives platforms for leveraged cryptocurrency trading. BitMEX trades on average over 3 billion dollars worth of volume per day, and allows users to go long or short Bitcoin with up to 100x leverage. We analyze the evolution of BitMEX products—both settled and perpetual offerings that have become the standard across other cryptocurrency derivatives platforms. We additionally utilize on-chain forensics, public liquidation events, and a site-wide chat room to describe the diverse ensemble of amateur and professional traders that forms this community. These traders range from wealthy agents running automated strategies, to individuals trading small, risky positions and focusing on very short time-frames. Finally, we discuss how derivative trading has impacted cryptocurrency asset prices, notably how it has led to dramatic price movements in the underlying spot markets.
Cross-city Analysis of Location-based Sentiment in User-generated Text
Authors: Christopher Stelzmüller, Sebastian Tanzer, Markus Schedl
Venue: WWW
Year: 2021

Geolocated user-generated content is a promising source of data reflecting how citizens live and feel. Information extracted from this source is being increasingly used for urban planning and policy evaluation purposes. While a lot of existing research focuses on the relationship between locations and sentiment in social media postings, we aim to uncover relations between location and sentiment that are consistent over cities around the world. In this paper, we therefore analyze the relationship between multiple categories of points of interest (POIs) in the OpenStreetMap dataset and the sentiment of English microblogging messages sent nearby using a three-stage processing pipeline: (1) extract sentiment scores from geolocated microblogs posted on Twitter, (2) spatial aggregation of sentiment in cities and POIs, (3) analyze relationships in aggregated sentiment. We identify differences in Twitter users’ sentiments within cities based on POIs, and we investigate the temporal dynamics of these sentiments and compare our findings between major cities in multiple countries.
HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering
Authors: Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Pérez, Maksims Volkovs
Venue: WWW
Year: 2021

Hyperbolic spaces offer a rich setup to learn embeddings with superior properties that have been leveraged in areas such as computer vision, natural language processing and computational biology. Recently, several hyperbolic approaches have been proposed to learn robust representations for users and items in the recommendation setting. However, these approaches don’t capture the higher order relationships that typically exist in the recommendation domain. Graph convolutional neural networks (GCNs) on the other hand excel at capturing higher order information by applying multiple levels of aggregation to local representations. In this paper we combine these frameworks in a novel way, by proposing a hyperbolic GCN model for collaborative filtering. We demonstrate that our model can be effectively learned with a margin ranking loss, and show that hyperbolic space has desirable properties under the rank margin setting. At test time, inference in our model is done using the hyperbolic distance which preserves the structure of the learned space. We conduct extensive empirical analysis on three public benchmarks and compare against a large set of baselines. Our approach achieves highly competitive results and outperforms leading baselines including the Euclidean GCN counterpart. We further study the properties of the learned hyperbolic embeddings and show that they offer meaningful insights into the data. Full code for this work is available here: https://github.com/layer6ai-labs/HGCF.
A Kind of Prediction Based on SOM Neural Clustering and Combination Weighting Evaluation Method
Authors: Ye Sun, Bing Tian, Xiaoyue Liu, Dufeng Chen, Yuliang Wu
Venue: WWW
Year: 2021

Rockburst disasters seriously threaten the safety and production progress of construction workers. In order to improve the accuracy of rockburst tendency prediction and ensure the rationality of index weights and classification and identification, a SOM clustering-combined weighting VIKOR model is proposed to predict rockburst. Based on the comprehensive analysis of the conditions of rockburst, the samples are classified from three indicators: rock brittleness index, tangential stress index and elastic strain energy index. This method accurately classifies samples through a self-organizing feature mapping network, and calculates the weights of different indicators through a combination weighting method, and finally sorts the rockburst grades through a multi-criteria compromise solution sorting method. This method makes the multi-information fusion of rockburst prediction more objective and operability. Comparing the engineering examples, it is found that the simulation calculation of the VIKOR rockburst prediction method of SOM neural network clustering and combination weighting is basically consistent with the engineering examples.
Automating Fairness Configurations for Machine Learning
Authors: Haipei Sun, Yiding Yang, Yanying Li, Huihui Liu, Xinchao Wang, Wendy Hui Wang
Venue: WWW
Year: 2021

Recent years have witnessed substantial efforts devoted to ensuring algorithmic fairness for machine learning (ML), spanning from formalizing fairness metrics to designing fairness-enhancing methods. These efforts lead to numerous possible choices in terms of fairness definitions and fairness-enhancing algorithms. However, finding the best fairness configuration (including both fairness definition and fairness-enhancing algorithms) for a specific ML task is extremely challenging in practice. The large design space of fairness configurations combined with the tremendous cost required for fairness deployment poses a major obstacle to this endeavor. This raises an important issue: can we enable automated fairness configurations for a new ML task on a potentially unseen dataset? To this point, we design Auto-Fair, a system that provides recommendations of fairness configurations by ranking all fairness configuration candidates based on their evaluations on prior ML tasks. At the core of Auto-Fair lies a meta-learning model that ranks all fairness configuration candidates by utilizing: (1) a set of meta-features that are derived from both datasets and fairness configurations that were used in prior evaluations; and (2) the knowledge accumulated from previous evaluations of fairness configurations on related ML tasks and datasets. The experimental results on 350 different fairness configurations and 1,500 data samples demonstrate the effectiveness of Auto-Fair.
Where are you taking me?Understanding Abusive Traffic Distribution Systems
Authors: Janos Szurdi, Meng Luo, Brian Kondracki, Nick Nikiforakis, Nicolas Christin
Venue: WWW
Year: 2021

Illicit website owners frequently rely on traffic distribution systems (TDSs) operated by less-than-scrupulous advertising networks to acquire user traffic. While researchers have described a number of case studies on various TDSs or the businesses they serve, we still lack an understanding of how users are differentiated in these ecosystems, how different illicit activities frequently leverage the same advertisement networks and, subsequently, the same malicious advertisers. We design ODIN (Observatory of Dynamic Illicit ad Networks), the first system to study cloaking, user differentiation and business integration at the same time in four different types of traffic sources: typosquatting, copyright-infringing movie streaming, ad-based URL shortening, and illicit online pharmacy websites. ODIN performed 874,494 scrapes over two months (June 19, 2019–August 24, 2019), posing as six different types of users (e.g., mobile, desktop, and crawler) and accumulating over 2TB of data. We observed 81% more malicious pages compared to using only the best performing crawl profile by itself. Three of the traffic sources we study redirect users to the same traffic broker domain names up to 44% of the time and all of them often expose users to the same malicious advertisers. Our experiments show that novel cloaking techniques could decrease by half the number of malicious pages observed. Worryingly, popular blacklists do not just suffer from the lack of coverage and delayed detection, but miss the vast majority of malicious pages targeting mobile users. We use these findings to design a classifier, which can make precise predictions about the likelihood of a user being redirected to a malicious advertiser.
"Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19
Authors: Fatemeh Tahmasbi, Leonard Schild, Chen Ling, Jeremy Blackburn, Gianluca Stringhini, Yang Zhang, Savvas Zannettou
Venue: WWW
Year: 2021

The outbreak of the COVID-19 pandemic has changed our lives in unprecedented ways. In the face of the projected catastrophic consequences, most countries have enacted social distancing measures in an attempt to limit the spread of the virus. Under these conditions, the Web has become an indispensable medium for information acquisition, communication, and entertainment. At the same time, unfortunately, the Web is being exploited for the dissemination of potentially harmful and disturbing content, such as the spread of conspiracy theories and hateful speech towards specific ethnic groups, in particular towards Chinese people and people of Asian descent since COVID-19 is believed to have originated from China. In this paper, we make a first attempt to study the emergence of Sinophobic behavior on the Web during the outbreak of the COVID-19 pandemic. We collect two large datasets from Twitter and 4chan’s Politically Incorrect board (/pol/) over a time period of approximately five months and analyze them to investigate whether there is a rise or important differences with regard to the dissemination of Sinophobic content. We find that COVID-19 indeed drives the rise of Sinophobia on the Web and that the dissemination of Sinophobic content is a cross-platform phenomenon: it exists on fringe Web communities like /pol/, and to a lesser extent on mainstream ones like Twitter. Using word embeddings over time, we characterize the evolution of Sinophobic slurs on both Twitter and /pol/. Finally, we find interesting differences in the context in which words related to Chinese people are used on the Web before and after the COVID-19 outbreak: on Twitter we observe a shift towards blaming China for the situation, while on /pol/ we find a shift towards using more (and new) Sinophobic slurs.
ConceptGuide: Supporting Online Video Learning with Concept Map-based Recommendation of Learning Path
Authors: Chien-Lin Tang, Jingxian Liao, Hao-Chuan Wang, Ching-Ying Sung, Wen-Chieh Lin
Venue: WWW
Year: 2021

People increasingly use online video platforms, e.g., YouTube, to locate educational videos to acquire knowledge or skills to meet personal learning needs. However, most of existing video platforms display video search results in generic ranked lists based on relevance to queries. The design of relevance-oriented information display does not take into account the inner structure of the knowledge domain, and may not suit the need of online learners. In this paper, we present ConceptGuide, a prototype system for learning orientations to support ad hoc online learning from unorganized video materials. ConceptGuide features a computational pipeline that performs content analysis on the transcripts of YouTube videos retrieved for a topic, and generates concept-map-based visual recommendations of inter-concept and inter-video links, forming learning pathways as structures for learners to consume. We evaluated ConceptGuide by comparing the design to the general-purpose interface of YouTube in learning experiences and behaviors. ConceptuGuide was found to improve the efficiency of video learning and helped learners explore the knowledge of interest in many constructive ways.
aiai at the FinSBD-3 task: Structure Boundary Detection of Noisy Financial Texts in English and French Using Data Augmentation and Hybrid Deep Learning Model
Authors: Ke Tian, Hua Chen
Venue: WWW
Year: 2021

Both authors contributed equally to this research. This paper presents the method that we tackled the FinSBD-3 shared task (structure boundary detection) to extract the boundaries of sentences, lists, and items, including structure elements like footer, header, tables from noisy unstructured English and French financial texts. The deep attention model based on word embedding using data augmentation and BERT model named as hybrid deep learning model to detect the sentence, list-item, footer, header, tables boundaries in noisy English and French texts and classify the list-item sentences into list & different item types using deep attention model. The experiment is shown that the proposed method could be an effective solution to deal with the FinSBD-3 shared task. The submitted result ranks first based on the task metrics in the final leader board.
aiai at the FinSim-2 task: Finance Domain Terms Automatic Classification Via Word Ontology and Embedding
Authors: Ke Tian, Hua Chen
Venue: WWW
Year: 2021

This paper describes the method that we submitted to the FinSim-2 task on learning similarities for the financial domain. This task aims to automatically classify the Financial domain terms into the most relevant hypernym (or top-level) concept in an external ontology. This paper shows the result of experiments using the Catboost, Attention-LSTM, BERT, RoBERTa to develop an automatic finance domain classifier via word ontology and embedding. The experiment result demonstrates that each model could be an effective method to tackle the FinSim-2 task, respectively.
Revisiting the Evaluation Protocol of Knowledge Graph Completion Methods for Link Prediction
Authors: Sudhanshu Tiwari, Iti Bansal, Carlos R. Rivero
Venue: WWW
Year: 2021

Completion methods learn models to infer missing (subject, predicate, object) triples in knowledge graphs, a task known as link prediction. The training phase is based on samples of positive triples and their negative counterparts. The test phase consists of ranking each positive triple with respect to its negative counterparts based on the scores obtained by a learned model. The best model ranks all positive triples first. Metrics like mean rank, mean reciprocal rank and hits at k are used to assess accuracy. Under this generic evaluation protocol, we observe several shortcomings: 1) Current metrics assume that each measurement is upper bounded by the same constant value and, therefore, are oblivious to the fact that, in link prediction, each positive triple may have a different number of negative counterparts, which alters the difficulty of ranking positive triples. 2) Benchmarking datasets contain anomalies (unrealistic redundancy) that allegedly simplifies link prediction; however, current instantiations of the generic evaluation protocol do not integrate anomalies, which are just discarded based on a user-defined threshold. 3) Benchmarking datasets have been randomly split, which typically alters the graph topology and results in the training split not resembling the original dataset. 4) A single model is typically kept based on its accuracy over the validation split using a given metric; however, since metrics aggregate ranks into a single value, there may be no significant differences among the ranks produced by several models, which must be all evaluated in the test phase. In this paper, we contribute to the evaluation of link prediction as follows: 1) We propose a variation of the mean rank that considers the number of negative counterparts. 2) We define the anomaly coefficient of a predicate and integrate such coefficient in the protocol. 3) We propose a downscaling algorithm to generate training splits that reflect the original graph topology based on a nonparametric, unpaired statistical test. 4) During validation, we discard a learned model only if its output ranks are significantly different than other ranks based on a nonparametric, paired statistical test. Our experiments over seven well-known datasets show that translation-based methods (TransD, TransE and TransH) significantly outperform recent methods, which entails that our understanding of the accuracy of completion methods for link prediction is far from perfect.
DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior
Authors: Patara Trirat, Jae-Gil Lee
Venue: WWW
Year: 2021

Because traffic accidents cause huge social and economic losses, it is of prime importance to precisely predict the traffic accident risk for reducing future accidents. In this paper, we propose a Deep Fusion network for citywide Traffic Accident Risk prediction (DF-TAR) with dangerous driving statistics that contain the frequencies of various dangerous driving offences in each region. Our unique contribution is to exploit these statistics, obtained by processing the data from in-vehicle sensors, for modeling the traffic accident risk. Toward this goal, we first examine the correlation between dangerous driving offences and traffic accidents, and the analysis shows a strong correlation between them in terms of both location and time. Specifically, quick start (0.83), rapid acceleration (0.76), and sharp turn (0.76) are the top three offences that have the highest average correlation scores. We then train the DF-TAR model using the dangerous driving statistics as well as external environmental features. By extensive experiments on various frameworks, the DF-TAR model is shown to improve the accuracy of the baseline models by up to 54% by virtue of the integration of dangerous driving into the modeling of traffic accident risk.
Auditing Source Diversity Bias in Video Search Results Using Virtual Agents
Authors: Aleksandra Urman, Mykola Makhortykh, Roberto Ulloa
Venue: WWW
Year: 2021

We audit the presence of domain-level source diversity bias in video search results. Using a virtual agent-based approach, we compare outputs of four Western and one non-Western search engines for English and Russian queries. Our findings highlight that source diversity varies substantially depending on the language with English queries returning more diverse outputs. We also find disproportionately high presence of a single platform, YouTube, in top search outputs for all Western search engines except Google. At the same time, we observe that Youtube’s major competitors such as Vimeo or Dailymotion do not appear in the sampled Google’s video search results. This finding suggests that Google might be downgrading the results from the main competitors of Google-owned Youtube and highlights the necessity for further studies focusing on the presence of own-content bias in Google’s search results.
WikiShark: An Online Tool for Analyzing Wikipedia Traffic and Trends
Authors: Elad Vardi, Lev Muchnik, Alex Conway, Micha Breakstone
Venue: WWW
Year: 2021

Wikipedia is a major source of information utilized by internet users around the globe for fact-checking and access to general, encyclopedic information. For researchers, it offers an unprecedented opportunity to measure how societies respond to events and how our collective perception of the world evolves over time and in response to events. Wikipedia use and the reading patterns of its users reflect our collective interests and the way they are expressed in our search for information – whether as part of fleeting, zeitgeist-fed trends or long-term – on most every topic, from personal to business, through political, health-related, academic and scientific. In a very real sense, events are defined by how we interpret them and how they affect our perception of the context in which they occurred, rendering Wikipedia invaluable for understanding events and their context. This paper introduces WikiShark (www.wikishark.com) – an online tool that allows researchers to analyze Wikipedia traffic and trends quickly and effectively, by (1) instantly querying pageview traffic data; (2) comparing traffic across articles; (3) surfacing and analyzing trending topics; and (4) easily leveraging findings for use in their own research.
BBR Bufferbloat in DASH Video
Authors: Santiago Vargas, Rebecca Drucker, Aiswarya Renganathan, Aruna Balasubramanian, Anshul Gandhi
Venue: WWW
Year: 2021

BBR is a new congestion control algorithm and is seeing increased adoption especially for video traffic. BBR solves the bufferbloat problem in legacy loss-based congestion control algorithms where application performance drops considerably when router buffers are deep. BBR regulates traffic such that router queues don’t build up to avoid the bufferbloat problem while still maintaining high throughput. However, our analysis shows that video applications experience significantly poor performance when using BBR under deep buffers. In fact, we find that video traffic sees inflated latencies because of long queues at the router, ultimately degrading video performance. To understand this dichotomy, we study the interaction between BBR and DASH video. Our investigation reveals that BBR under deep buffers and high network burstiness severely overestimates available bandwidth and does not converge to steady state, both of which results in BBR sending substantially more data into the network, causing a queue buildup. This elevated packet sending rate under BBR is ultimately caused by the router’s ability to absorb bursts in traffic, which destabilizes BBR’s bandwidth estimation and overrides BBR’s expected logic for exiting the startup phase. We design a new bandwidth estimation algorithm and apply it to BBR (and a still-unreleased, newer version of BBR called BBR2). Our modified BBR and BBR2 both see significantly improved video QoE even under deep buffers.
AI Principles in Identifying Toxicity in Online Conversation: Keynote at the Third Workshop on Fairness, Accountability, Transparency, Ethics and Society on the Web
Authors: Lucy Vasserman
Venue: WWW
Year: 2021

Jigsaw’s Perspective API aims to protect voices in online conversation by developing and serving machine learning models that identify toxicity text. This talk will share how the team behind Perspective thinks about the issues of Fairness, Accountability, Transparency, Ethics and Society through the lens of Google’s AI Principles. For the Perspective team, building technology that is fair and ethical is a continuous, ongoing effort. The talk will cover concrete strategies the Perspective team has already used to mitigate bias in ML models as well as new strategies currently being explored. Finally, with examples of how Perspective is being used in the real world, the talk will show how machine learning, combined with thoughtful human moderation and participation, can help improve online conversations.