OpenRaiser/Intern-Atlas huggingface dataset:
Measuring the Privacy vs. Compatibility Trade-off in Preventing Third-Party Stateful Tracking
Authors: Jordan Jueckstock, Peter Snyder, Shaown Sarker, Alexandros Kapravelos, Benjamin Livshits
Venue: WWW
Year: 2022

Despite active privacy research on sophisticated web tracking techniques (e.g., fingerprinting, cache collusion, bounce tracking, CNAME cloaking), most tracking on the web is basic “stateful” tracking enabled by classical browser storage policies sharing per-site storage across all HTTP contexts. Alternative, privacy-preserving storage policies, especially for third-party contexts, have been proposed and even deployed, but these can break websites that presume traditional, non-partitioned storage. Such breakage discourages privacy-preserving experimentation, cementing the dismal status quo. Our work measures the privacy vs. compatibility trade-offs of representative third-party storage policies to enable design of browsers that are both compatible and privacy respecting. Our contributions include web-scale measurements of page behaviors under multiple third-party storage policies inspired by production browsers. We define metrics for measuring aggregate effects on web privacy and compatibility, including a novel system for quantitatively estimating aggregate website breakage under different policies. We find that making third-party storage partitioned by first-party, and lifetimes by site-session achieves the best privacy and compatibility trade-off. We provide complete measurement datasets and storage policy implementations.
Sequence-Based Extractive Summarisation for Scientific Articles
Authors: Daniel Kershaw, Rob Koeling
Venue: WWW
Year: 2022

This paper presents the results of research on supervised extractive text summarisation for scientific articles. We show that a simple sequential tagging model based only on the text within a document achieves high results against a simple classification model. Improvements can be achieved through additional sentence-level features, though these were minimal. Through further analysis, we show the potential of the sequential model relying on the structure of the document depending on the academic discipline which the document is from.
Quantifying the Topic Disparity of Scientific Articles
Authors: Munjung Kim, Jisung Yoon, Woo-Sung Jung, Hyunuk Kim
Venue: WWW
Year: 2022

Citation count is a popular index for assessing scientific papers. However, it depends on not only the quality of a paper but also various factors, such as conventionality, journal, team size, career age, and gender. Here, we examine the extent to which the conventionality of a paper is related to its citation count by using our measure, topic disparity. The topic disparity is the cosine distance between a paper and its discipline on a neural embedding space. Using this measure, we show that the topic disparity is negatively associated with citation count, even after controlling journal impact, team size, and the career age and gender of the first and last authors. This result indicates that less conventional research tends to receive fewer citations than conventional research. The topic disparity can be used to complement citation count and to recommend papers at the periphery of a discipline because of their less conventional topics.
Examining the ORKG towards Representation of Control Theoretic Knowledge - Preliminary Experiences and Conclusions
Authors: Carsten Knoll
Venue: WWW
Year: 2022

Control theory is an interdisciplinary academic domain which contains sophisticated elements from various sub-domains of both mathematics and engineering. The issue of knowledge transfer thus poses a considerable challenge w.r.t. transfer between researchers focusing on different niches as well as w.r.t. transfer into potential application domains. The paper investigates the Open Research Knowledge Graph (ORKG) as medium to facilitate such knowledge transfer. In particular it investigates the current state of control theoretic knowledge represented in the ORKG and describes the process of extending that knowledge as well as the observed challenges thereby. The main results are a) a list of best practice suggestions for the ORKG contributions and b) a list of improvement suggestions for the further development of the ORKG and similar platforms. All relevant claims w.r.t. the ORKG are backed by SPARQL queries and some further evaluation code, both publicly available for the sake of reproducibility.
Capturing Diverse and Precise Reactions to a Comment with User-Generated Labels
Authors: Eun-Young Ko, Eunseo Choi, Jeong-woo Jang, Juho Kim
Venue: WWW
Year: 2022

Simple up/downvotes, arguably the most widely used reaction design across social media platforms, allow users to efficiently express their opinions and quickly evaluate others’ opinions from aggregated votes. However, such design forces users to project their diverse opinions onto dichotomized reactions and provides limited information to readers on why a comment was up/downvoted. We explore user-generated labels (UGLs) as an alternative reaction design to capture the rich context of user reactions to comments. We conducted a between-subjects study with 218 participants to understand how people use and are influenced by UGLs compared to up/downvotes. Specifically, we examine how UGLs affect users’ ability to express and perceive diverse opinions. Participants generated 234 unique labels on diverse aspects of a comment. Leaving more reactions than participants in the up/downvotes condition, participants reported that the ability to express their opinions improved with UGLs. UGLs also enabled participants to better understand the multifacetedness of public evaluation of a comment.
Can I only share my eyes? A Web Crowdsourcing based Face Partition Approach Towards Privacy-Aware Face Recognition
Authors: Ziyi Kou, Lanyu Shang, Yang Zhang, Siyu Duan, Dong Wang
Venue: WWW
Year: 2022

Human face images represent a rich set of visual information for online social media platforms to optimize the machine learning (ML)/AI models in their data-driven facial applications (e.g., face detection, face recognition). However, there exists a growing privacy concern from social media users to share their online face images that will be annotated by unknown crowd workers and analyzed by ML/AI researchers in the model training and optimization process. In this paper, we focus on a privacy-aware face recognition problem where the goal is to empower the facial applications to train their face recognition models with images shared by social media users while protecting the identity of the users. Our problem is motivated by the limitation of current privacy-aware face recognition approaches that mainly prevent algorithmic attacks by manipulating face images but largely ignore the potential privacy leakage related to human activities (e.g., crowdsourcing annotation). To address such limitations, we develop FaceCrowd, a web crowdsourcing based face partition approach to improve the performance of current face recognition models by designing a novel crowdsourced partial face graph generated from privacy-preserved social media face images. We evaluate the performance of FaceCrowd using two real-world human face datasets that consist of large-scale human face images. The results show that FaceCrowd not only improves the accuracy of the face recognition models but also effectively protects the identity information of the social media users who share their face images.
Geospatial Interlinking with JedAI-spatial
Authors: Manolis Koubarakis
Venue: WWW
Year: 2022

In this talk I will present the framework JedAI-spatial for the interlinking of geospatial data encoded in RDF.
A View into YouTube View Fraud
Authors: Dhruv Kuchhal, Frank Li
Venue: WWW
Year: 2022

Social media platforms are driven by user engagement metrics. Unfortunately, such metrics are susceptible to manipulation and expose the platforms to abuse. Video view fraud is a unique class of fake engagement abuse on video-sharing platforms, such as YouTube, where the view count of videos is artificially inflated. There exists limited research on such abuse, and prior work focused on automated or bot-driven approaches. In this paper, we explore organic or human-driven approaches to view fraud, conducting a case study on a long-running YouTube view fraud campaign operated on a popular free video streaming service, 123Movies. Before 123Movies users are allowed to access a stream on the service, they must watch an unsolicited YouTube video displayed as a pre-roll advertisement. Due to 123Movies’ popularity, this activity drives large-scale YouTube view fraud. In this study, we reverse-engineer how 123Movies distributes these YouTube videos as pre-roll advertisements, and track the YouTube videos involved over a 9-month period. For a subset of these videos, we monitor their view counts and metrics for their respective YouTube channels over the same period. Our analysis reveals the characteristics of YouTube channels and videos participating in this view fraud, as well as the efficacy of such view fraud efforts. Ultimately, our study provides empirical grounding on organic YouTube view fraud.
Comprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing
Authors: Tin Kuculo
Venue: WWW
Year: 2022

Recent work has utilised knowledge-aware approaches to natural language understanding, question answering, recommendation systems, and other tasks. These approaches rely on well-constructed and large-scale knowledge graphs that can be useful for many downstream applications and empower knowledge-aware models with commonsense reasoning. Such knowledge graphs are constructed through knowledge acquisition tasks such as relation extraction and knowledge graph completion. This work seeks to utilise and build on the growing body of work that uses findings from the field of natural language processing (NLP) to extract knowledge from text and build knowledge graphs. The focus of this research project is on how we can use transformer-based approaches to extract and contextualise event information, matching it to existing ontologies, to build comprehensive knowledge graph-based event representations. Specifically, sub-event extraction is used as a way of creating sub-event-aware event representations. These event representations are then further enriched through fine-grained location extraction and contextualised through the alignment of historically relevant quotes.
Deriving Customer Experience Implicitly from Social Media
Authors: Aditya Kumar, Sneh Gupta, Ankit Sahu, Mayank Kant
Venue: WWW
Year: 2022

Organizations that focus on maximizing satisfaction, a consistent and seamless experience throughout the entire customer journey are the ones who dominate the market. Net Promoter Score (NPS) is a widely accepted metric to measure the customer experience, and the most common way to calculate it to date is by conducting a survey. But this comes with a bottleneck. The whole process can be costly, low-sample, responder-biased, and issues could be limited to the questionnaire used for the survey. We have devised a mechanism to approximate it implicitly from the mentions extracted from the four major social media platforms - Twitter, Facebook, Instagram, and YouTube. Our Data Cleaning pipeline discards the viral and promotional content (from brands, sellers, marketplaces, or public figures), and the Machine Learning pipeline captures the different customer journey nodes specific to e-commerce (like discovery, delivery, pricing) with their appropriate sentiment. Since the framework is generic and relies only on publicly available social media data, any organization can estimate its NPS after making suitable adjustments depending on the industry and geography. Our NPS model has a Mean absolute percentage error (MAPE) of 1.9%, Pearson correlation of 79%, and enables us to understand the actual drivers at the weekly level.
Towards Building Live Open Scientific Knowledge Graphs
Authors: Anh Le-Tuan, Carlos Franzreb, Danh Le Phuoc, Sonja Schimmler, Manfred Hauswirth
Venue: WWW
Year: 2022

Due to the large number and heterogeneity of data sources, it becomes increasingly difficult to follow the research output and the scientific discourse. For example, a publication listed on DBLP may be discussed on Twitter and its underlying data set may be used in a different paper published on arXiv. The scientific discourse this publication is involved in is divided among not integrated systems, and for researchers it might be very hard to follow all discourses a publication or data set may be involved in. Also, many of these data sources—DBLP, arXiv, or Twitter, to name a few—are often updated in real-time. These systems are not integrated (silos), and there is no system for users to query the content/data actively or, what would be even more beneficial, in a publish/subscribe fashion, i.e., a system would actively notify researchers of work interesting to them when such work or discussions become available. In this position paper, we introduce our concept of a live open knowledge graph which can integrate an extensible set of existing or new data sources in a streaming fashion, continuously fetching data from these heterogeneous sources, and interlinking and enriching it on-the-fly. Users can subscribe to continuously query the content/data of their interest and get notified when new content/data becomes available. We also highlight open challenges in realizing a system enabling this concept at scale.
BZNet: Unsupervised Multi-scale Branch Zooming Network for Detecting Low-quality Deepfake Videos
Authors: Sangyup Lee, Jaeju An, Simon S. Woo
Venue: WWW
Year: 2022

Generating a deep learning-based fake video has become no longer rocket science. The advancement of automated Deepfake (DF) generation tools that mimic certain targets has rendered society vulnerable to fake news or misinformation propagation. In real-world scenarios, DF videos are compressed to low-quality (LQ) videos, taking up less storage space and facilitating dissemination through the web and social media. Such LQ DF videos are much more challenging to detect than high-quality (HQ) DF videos. To address this challenge, we rethink the design of standard deep learning-based DF detectors, specifically exploiting feature extraction to enhance the features of LQ images. We propose a novel LQ DF detection architecture, multi-scale Branch Zooming Network (BZNet), which adopts an unsupervised super-resolution (SR) technique and utilizes multi-scale images for training. We train our BZNet only using highly compressed LQ images and experiment under a realistic setting, where HQ training data are not readily accessible. Extensive experiments on the FaceForensics++ LQ and GAN-generated datasets demonstrate that our BZNet architecture improves the detection accuracy of existing CNN-based classifiers by 4.21% on average. Furthermore, we evaluate our method against a real-world Deepfake-in-the-Wild dataset collected from the internet, which contains 200 videos featuring 50 celebrities worldwide, outperforming the state-of-the-art methods by 4.13%.
Contrastive Learning for Knowledge Tracing
Authors: Wonsung Lee, Jaeyoon Chun, Youngmin Lee, Kyoungsoo Park, Sungrae Park
Venue: WWW
Year: 2022

Knowledge tracing is the task of understanding student’s knowledge acquisition processes by estimating whether to solve the next question correctly or not. Most deep learning-based methods tackle this problem by identifying hidden representations of knowledge states from learning histories. However, due to the sparse interactions between students and questions, the hidden representations can be easily over-fitted and often fail to capture student’s knowledge states accurately. This paper introduces a contrastive learning framework for knowledge tracing that reveals semantically similar or dissimilar examples of a learning history and stimulates to learn their relationships. To deal with the complexity of knowledge acquisition during learning, we carefully design the components of contrastive learning, such as architectures, data augmentation methods, and hard negatives, taking into account pedagogical rationales. Our extensive experiments on six benchmarks show statistically significant improvements from the previous methods. Further analysis shows how our methods contribute to improving knowledge tracing performances.
Link: Black-Box Detection of Cross-Site Scripting Vulnerabilities Using Reinforcement Learning
Authors: Soyoung Lee, Seongil Wi, Sooel Son
Venue: WWW
Year: 2022

Black-box web scanners have been a prevalent means of performing penetration testing to find reflected cross-site scripting (XSS) vulnerabilities. Unfortunately, off-the-shelf black-box web scanners suffer from unscalable testing as well as false negatives that stem from a testing strategy that employs fixed attack payloads, thus disregarding the exploitation of contexts to trigger vulnerabilities. To this end, we propose a novel method of adapting attack payloads to a target reflected XSS vulnerability using reinforcement learning (RL). We present Link, a general RL framework whose states, actions, and a reward function are designed to find reflected XSS vulnerabilities in a black-box and fully automatic manner. Link finds 45, 213, and 60 vulnerabilities with no false positives in Firing-Range, OWASP, and WAVSEP benchmarks, respectively, outperforming state-of-the-art web scanners in terms of finding vulnerabilities and ending testing campaigns earlier. Link also finds 43 vulnerabilities in 12 real-world applications, demonstrating the promising efficacy of using RL in finding reflected XSS vulnerabilities.
Context-based Collective Preference Aggregation for Prioritizing Crowd Opinions in Social Decision-making
Authors: Jiyi Li
Venue: WWW
Year: 2022

Given a social issue that needs to be solved, decision-makers need to listen to the crowd opinions and preferences. However, existing online voting systems with limited capabilities cannot conduct such investigations. Our idea is that decision-makers can collect many human opinions from crowds on the web and then prioritize them for social decision-making. A solution of the prioritization entails collecting a large amount of pairwise preference comparisons from crowds and utilizing the aggregated preference labels as the collective preferences on the opinions. In practice, because there is a large number of combinations of all candidate opinion pairs, we can only collect a small number of labels for a small subset of pairs. How to utilize only a small number of pairwise crowd preferences on the opinions to estimate collective preferences is the problem. Existing works on preference aggregation methods for general scenarios utilize only the pairwise preference labels. In our scenario, additional contextual information, such as the text contents of the opinions, can potentially promote the aggregation performance. Therefore, we propose preference aggregation approaches that can effectively incorporate contextual information by externally or internally building the relations between the opinion contexts and preference scores. We propose approaches for both the homogeneous and heterogeneous settings of modeling the evaluators. The experiments conducted on real datasets collected from real-world crowdsourcing platform show that our approaches can generate better aggregation results than the baselines for estimating collective preferences, especially when there are only a small number of preference labels available.
Seesaw Counting Filter: An Efficient Guardian for Vulnerable Negative Keys During Dynamic Filtering
Authors: Meng Li, Deyi Chen, Haipeng Dai, Rongbiao Xie, Siqiang Luo, Rong Gu, Tong Yang, Guihai Chen
Venue: WWW
Year: 2022

Bloom filter is an efficient data structure for filtering negative keys (keys not in a given set) with substantially small space. However, in real-world applications, there widely exist vulnerable negative keys, which will bring high costs if not being properly filtered, especially when positive keys are added/deleted dynamically. To address the problem, we propose SeeSaw Counting Filter (SSCF), which is innovated with encapsulating the vulnerable negative keys into a unified counter array named seesaw counter array, and dynamically modulating (or varying) the applied hash functions to guard the encapsulated keys from being misidentified. Moreover, we propose ada-SSCF to handle the scenarios where the vulnerable negative keys cannot be obtained in advance. We extensively evaluate our SSCF, which shows that SSCF outperforms the cutting-edge filters by 3 × on averages regarding accuracy while ensuring a low operation latency. All source codes are in [2].
FingFormer: Contrastive Graph-based Finger Operation Transformer for Unsupervised Mobile Game Bot Detection
Authors: Wenbin Li, Xiaokai Chu, Yueyang Su, Di Yao, Shiwei Zhao, Runze Wu, Shize Zhang, Jianrong Tao, Hao Deng, Jingping Bi
Venue: WWW
Year: 2022

This paper studies the task of detecting bots for online mobile games. Considering the fact of lacking labeled cheating samples and restricted available data in the real detection systems, we aim to study the finger operations captured by screen sensors to infer the potential bots in an unsupervised way. In detail, we introduce a Transformer-style detection model, namely FingFormer. It studies the finger operations in the format of graph structure in order to capture the spatial and temporal relatedness between the two hands’ operations. To optimize the model in an unsupervised way, we introduce two contrastive learning strategies to refine both finger moving patterns and players’ operation habits. We conduct extensive experiments under different experimental environments, including the synthetic dataset, the offline dataset, as well as the large-scale online data flow from three mobile games. The multi-facet experiments illustrate the proposed model is both effective and general to detect the bots for different mobile games.
TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection
Authors: Sijia Li, Gaopeng Gou, Chang Liu, Chengshang Hou, Zhenzhen Li, Gang Xiong
Venue: WWW
Year: 2022

In recent years, phishing scams have become the most serious type of crime involved in Ethereum, the second-largest blockchain platform. The existing phishing scams detection technology on Ethereum mostly uses traditional machine learning or network representation learning to mine the key information from the transaction network to identify phishing addresses. However, these methods adopt the last transaction record or even completely ignore these records, and only manual-designed features are taken for the node representation. In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum. Specifically, in the temporal edges representation module, we model the temporal relationship of historical transaction records between nodes to construct the edge representation of the Ethereum transaction network. Moreover, the edge representations around the node are aggregated to fuse topological interactive relationships into its representation, also named as trading features, in the edge2node module. We further combine trading features with common statistical and structural features obtained by graph neural networks to identify phishing addresses. Evaluated on real-world Ethereum phishing scams datasets, our TTAGN (92.8% AUC, and 81.6% F1-score) outperforms the state-of-the-art methods, and the effectiveness of temporal edges representation and edge2node module is also demonstrated.