OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Intent Detection in Customer Service with Social Media Data
Authors: Jiantao Huang, Yi-Ru Liou, Hsin-Hsi Chen
Venue: WWW
Year: 2021

Intent detection plays an important role in customer service dialog systems for providing high-quality service in the financial industry. The lack of publicly available datasets and high annotation cost are two challenging issues in this research direction. To overcome these challenges, we propose a social media enhanced self-training approach for intent detection by using label names only. The experimental results show the effectiveness of the proposed method.
Characterizing Opinion Dynamics and Group Decision Making in Wikipedia Content Discussions
Authors: Khandaker Tasnim Huq, Giovanni Luca Ciampaglia
Venue: WWW
Year: 2021

Wikipedia, the online encyclopedia, is a trusted source of knowledge for millions of individuals worldwide. As everyone can start a new article, it is often necessary to decide whether certain entries meet the standards for inclusion set forth by the community. These decisions (which are known as “Article for Deletion”, or AfD) are taken by groups of editors in a deliberative fashion, and are known for displaying a number of common biases associated to group decision making. Here, we present an analysis of 1,967,768 AfD discussions between 2005 and 2018. We perform a signed network analysis to capture the dynamics of agreement and disagreement among editors. We measure the preference of each editor for voting toward either inclusion or deletion. We further describe the evolution of individual editors and their voting preferences over time, finding four major opinion groups. Finally, we develop a predictive model of discussion outcomes based on latent factors. Our results shed light on an important, yet overlooked, aspect of curation dynamics in peer production communities, and could inform the design of improved processes of collective deliberation on the web.
Managing Knowledge in Energy Data Spaces
Authors: Valentina Janev, Maria-Esther Vidal, Kemele M. Endris, Dea Pujic
Venue: WWW
Year: 2021

Data in the energy domain grows at unprecedented rates and is usually generated by heterogeneous energy systems. Despite the great potential that big data-driven technologies can bring to the energy sector, general adoption is still lagging. Several challenges related to controlled data exchange and data integration are still not wholly achieved. As a result, fragmented applications are developed against energy data silos, and data exchange is limited to few applications. In this paper, we analyze the challenges and requirements related to energy-related data applications. We also evaluate the use of Energy Data Ecosystems (EDEs) as data-driven infrastructures to overcome the current limitations of fragmented energy applications. EDEs are inspired by the International Data Space (IDS) initiative launched in Germany at the end of 2014 with an overall objective to take both the development and use of the IDS reference architecture model to a European/global level. The reference architecture model consists of four architectures related to business, security, data and service, and software aspects. This paper illustrates the applicability of EDEs and IDS reference architecture in real-world scenarios from the energy sector. The analyzed scenario is positioned in the context of the EU-funded H2020 project PLATOON.
Modeling Text Data Over Time - Example on Job Postings
Authors: Jakob Jelencic
Venue: WWW
Year: 2021

Modelling multilingual text data over time is a challenging task. This PhD is focused on semantic representation of domain specific short to mid length time stamped textual data. The proposed method is evaluated on the example of job postings, where we are modeling demand on IT jobs. More specifically, we addresses the following three problems: unifying the representation of multilingual text data; clustering similar textual data; using the proposed semantic representation to model and predict future demand of jobs. This works starts with a problem statement, followed by a description of the proposed approach and methodology and is concluded with an overview of the first results and summary of the ongoing research.
HINTS: Citation Time Series Prediction for New Publications via Dynamic Heterogeneous Information Network Embedding
Authors: Song Jiang, Bernard Koch, Yizhou Sun
Venue: WWW
Year: 2021

Accurate prediction of scientific impact is important for scientists, academic recommender systems, and granting organizations alike. Existing approaches rely on many years of leading citation values to predict a scientific paper’s citations (a proxy for impact), even though most papers make their largest contributions in the first few years after they are published. In this paper, we tackle a new problem: predicting a new paper’s citation time series from the date of publication (i.e., without leading values). We propose HINTS, a novel end-to-end deep learning framework that converts citation signals from dynamic heterogeneous information networks (DHIN) into citation time series. HINTS imputes pseudo-leading values for a paper in the years before it is published from DHIN embeddings, and then transforms these embeddings into the parameters of a formal model that can predict citation counts immediately after publication. Empirical analysis on two real-world datasets from Computer Science and Physics show that HINTS is competitive with baseline citation prediction models. While we focus on citations, our approach generalizes to other “cold start” time series prediction tasks where relational data is available and accurate prediction in early timestamps is crucial.
Financial Fraud Detection on Micro-credit Loan Scenario via Fuller Location Information Embedding
Authors: Junxiang Jiang, Boyi Ni, Chunping Wang
Venue: WWW
Year: 2021

Micro-credit loan serves as an indispensable supplementary loan for people lacking verifiable credit records or unqualified to get conventional bank loans. And financial fraud detection is one of the mainstream methods to control loan risk. Its goal is to utilize a set of corresponding features (e.g., customer behaviors) to predict whether a customer will fail to make required payments in the future. To the best of our knowledge, few works pay attention to permanent residential locations of customers. However, the real data study shows that customer location information potentially provides additional power in financial fraud detection. Three challenges place barriers to make full use of location information: (1) Data sparsity makes financial fraud detection model hard to learn the relationship between location information and fraud behaviors; (2) Financial fraud detection model considering location information alone without resident personality, might weaken fraud distinguish power of location information; (3) The representation of location information should be effective and easy to apply, for being used in various applications. In this paper, we propose Fuller Location Information Embedding (FLIE) network. FLIE ideally handles above challenges, which performance verified by experiments on the tasks of fraudulent customer prediction and customer segmentation.
Progressive Semantic Reasoning for Image Inpainting
Authors: Junjie Jin, Xinrong Hu, Kai He, Tao Peng, Junping Liu, Jie Yang
Venue: WWW
Year: 2021

Image inpainting aims to reconstruct the missing or unknown region for a given image. As one of the most important topics from image processing, this task has attracted increasing research interest over the past few decades. Learning-based methods have been employed to solve this task, and achieved superior performance. Nevertheless, existing methods often produce artificial traces, due to the lack of constraints on image characterization under different semantics. To accommodate this issue, we propose a novel artistic Progressive Semantic Reasoning (PSR) network in this paper, which is composed of three shared parameters from the generation network superposition. More precisely, the proposed PSR algorithm follows a typical end-to-end training procedure, that learns low-level semantic features and further transfers them to a high-level semantic network for inpainting purposes. Furthermore, a simple but effective Cross Feature Reconstruction (CFR) strategy is proposed to tradeoff semantic information from different levels. Empirically, the proposed approach is evaluated via intensive experiments using a variety of real-world datasets. The results confirm the effectiveness of our algorithm compared with other state-of-the-art methods. The source code can be found from https://github.com/sfwyly/PSR-Net.
Heterogeneous Graph Neural Network via Attribute Completion
Authors: Di Jin, Cuiying Huo, Chundong Liang, Liang Yang
Venue: WWW
Year: 2021

Heterogeneous information networks (HINs), also called heterogeneous graphs, are composed of multiple types of nodes and edges, and contain comprehensive information and rich semantics. Graph neural networks (GNNs), as powerful tools for graph data, have shown superior performance on network analysis. Recently, many excellent models have been proposed to process hetero-graph data using GNNs and have achieved great success. These GNN-based heterogeneous models can be interpreted as smooth node attributes guided by graph structure, which requires all nodes to have attributes. However, this is not easy to satisfy, as some types of nodes often have no attributes in heterogeneous graphs. Previous studies take some handcrafted methods to solve this problem, which separate the attribute completion from the graph learning process and, in turn, result in poor performance. In this paper, we hold that missing attributes can be acquired by a learnable manner, and propose a general framework for Heterogeneous Graph Neural Network via Attribute Completion (HGNN-AC), including pre-learning of topological embedding and attribute completion with attention mechanism. HGNN-AC first uses existing HIN-Embedding methods to obtain node topological embedding. Then it uses the topological relationship between nodes as guidance to complete attributes for no-attribute nodes by weighted aggregation of the attributes from these attributed nodes. Our complement mechanism can be easily combined with an arbitrary GNN-based heterogeneous model making the whole system end-to-end. We conduct extensive experiments on three real-world heterogeneous graphs. The results demonstrate the superiority of the proposed framework over state-of-the-art baselines.
Understanding the Impact of Encrypted DNS on Internet Censorship
Authors: Lin Jin, Shuai Hao, Haining Wang, Chase Cotton
Venue: WWW
Year: 2021

DNS traffic is transmitted in plaintext, resulting in privacy leakage. To combat this problem, secure protocols have been used to encrypt DNS messages. Existing studies have investigated the performance overhead and privacy benefits of encrypted DNS communications, yet little has been done from the perspective of censorship. In this paper, we study the impact of the encrypted DNS on Internet censorship in two aspects. On one hand, we explore the severity of DNS manipulation, which could be leveraged for Internet censorship, given the use of encrypted DNS resolvers. In particular, we perform 7.4 million DNS lookup measurements on 3,813 DoT and 75 DoH resolvers and identify that 1.66% of DoT responses and 1.42% of DoH responses undergo DNS manipulation. More importantly, we observe that more than two-thirds of the DoT and DoH resolvers manipulate DNS responses from at least one domain, indicating that the DNS manipulation is prevalent in encrypted DNS, which can be further exploited for enhancing Internet censorship. On the other hand, we evaluate the effectiveness of using encrypted DNS resolvers for censorship circumvention. Specifically, we first discover those vantage points that involve DNS manipulation through on-path devices, and then we apply encrypted DNS resolvers at these vantage points to access the censored domains. We reveal that 37% of the domains are accessible from the vantage points in China, but none of the domains is accessible from the vantage points in Iran, indicating that the censorship circumvention of using encrypted DNS resolvers varies from country to country. Moreover, for a vantage point, using a different encrypted DNS resolver does not lead to a noticeable difference in accessing the censored domains.
Dr.Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond
Authors: Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth A. Loparo, Liang Zhao
Venue: WWW
Year: 2021

During the pandemic caused by coronavirus disease (COVID-19), social media has played an important role by enabling people to discuss their experiences and feelings of this global crisis. To help combat the prolonged pandemic that has exposed vulnerabilities impacting community resilience, in this paper, based on our established large-scale COVID-19 related social media data, we propose and develop an integrated framework (named Dr.Emotion) to learn disentangled representations of social media posts (i.e., tweets) for emotion analysis and thus to gain deep insights into public perceptions towards COVID-19. In Dr.Emotion, for given social media posts, we first post-train a transformer-based model to obtain the initial post embeddings. Since users may implicitly express their emotions in social media posts which could be highly entangled with other descriptive information in the post content, to address this challenge for emotion analysis, we propose an adversarial disentangler by integrating emotion-independent (i.e., sentiment-neutral) priors of the posts generated by another post-trained transformer-based model to separate and disentangle the implicitly encoded emotions from the content in latent space for emotion classification at the first attempt. Extensive experimental studies are conducted to fully evaluate Dr.Emotion and promising results demonstrate its performance in emotion analysis by comparison with the state-of-the-art baseline methods. By exploiting our developed Dr.Emotion, we further perform emotion analysis over a large number of social media posts and provide in-depth investigation from both temporal and geographical perspectives, based on which additional work can be conducted to extract and transform the constructive ideas, experiences and support into actionable information to improve community resilience in responses to a variety of crises created by COVID-19 and well beyond.
Towards Realistic and ReproducibleWeb Crawl Measurements
Authors: Jordan Jueckstock, Shaown Sarker, Peter Snyder, Aidan Beggs, Panagiotis Papadopoulos, Matteo Varvello, Benjamin Livshits, Alexandros Kapravelos
Venue: WWW
Year: 2021

Accurate web measurement is critical for understanding and improving security and privacy online. Such measurements implicitly assume that automated crawls generalize to typical web user experience. But anecdotal evidence suggests the web behaves differently when seen via well-known measurement endpoints or measurement automation frameworks, for various reasons. Our work improves the state of web privacy and security by investigating how key measurements differ when using naive crawling tool defaults vs. careful attempts to match “real” users across the Tranco top 25k web domains. We find web privacy and security measurements significantly affected by vantage point and browser configuration. We conclude that unless researchers ensure their web measurement tools match real world user experience, the research community is likely missing important signals systematically. For example, we find browser configuration alone causing shifts in 19% of known ad and tracking domains encountered and altering the loading frequency of up to 10% of distinct JavaScript code units executed. We find network vantage point having similar, though less dramatic, effects on the same web metrics. To ensure reproducibility, we carefully document our methodology and publish both our code and collected data.
Information flow on COVID-19 over Wikipedia: A case study of 11 languages
Authors: Changwook Jung, Inho Hong, Diego Sáez-Trumper, Damin Lee, Jaehyeon Myung, Danu Kim, Jinhyuk Yun, Woo-Sung Jung, Meeyoung Cha
Venue: WWW
Year: 2021

Wikipedia has been a critical information source during the COVID-19 pandemic. Analyzing how information is created, edited, and viewed on this platform can help gain new insights for risk communication strategies for the next pandemic. Here, we study the content editor and viewer patterns on the COVID-19 related documents on Wikipedia using a near-complete dataset gathered of 11 languages over 238 days in 2020. Based on the analysis of the daily access and edit logs on the identified Wikipedia pages, we discuss how the regional and cultural closeness factors affect information demand and supply.
Analyzing European Migrant-related Twitter Deliberations
Authors: Aparup Khatua, Wolfgang Nejdl
Venue: WWW
Year: 2021

Machine-driven topic identification of online contents is a prevalent task in the natural language processing (NLP) domain. Social media deliberation reflects society's opinion, and a structured analysis of these contents allows us to decipher the same. We employ an NLP-based approach for investigating migration-related Twitter discussions. Besides traditional deep learning-based models, we have also considered pre-trained transformer-based models for analyzing our corpus. We have successfully classified multiple strands of public opinion related to European migrants. Finally, we use 'BertViz' to visually explore the interpretability of better performing transformer-based models.
War of Words II: Enriched Models of Law-Making Processes
Authors: Victor Kristof, Aswin Suresh, Matthias Grossglauser, Patrick Thiran
Venue: WWW
Year: 2021

The European Union law-making process is an instance of a peer-production system. We mine a rich dataset of law edits and introduce models predicting their adoption by parliamentary committees. Edits are proposed by parliamentarians, and they can be in conflict with edits of other parliamentarians and with the original proposition in the law. Our models combine three different categories of features: (a) Explicit features extracted from data related to the edits, the parliamentarians, and the laws, (b) latent features that capture bi-linear interactions between parliamentarians and laws, and (c) text features of the edits. We show experimentally that this combination enables us to accurately predict the success of the edits. Furthermore, it leads to model parameters that are interpretable, hence provides valuable insight into the law-making process.
TAKCO: A Platform for Extracting Novel Facts from Tables
Authors: Benno Kruit, Peter Boncz, Jacopo Urbani
Venue: WWW
Year: 2021

Web tables contain a large amount of useful knowledge. Takco is a new large-scale platform designed for extracting facts from tables that can be added to Knowledge Graphs (KGs) like Wikidata. Focusing on achieving high precision, current techniques are biased towards extracting redundant facts, i.e., facts already in the KG. Takco aims to find more novel facts, still at high precision. Our demonstration has two goals. The first one is to illustrate the main features of Takco’s novel interpretation algorithm. The second goal is to show to what extent other state-of-the-art systems are biased towards the extraction of redundant facts using our platform, thus raising awareness on this important problem.
Semantic Search via Entity-Types: The SEMANNOREX Framework
Authors: Amit Kumar, Govind, Marc Spaniol
Venue: WWW
Year: 2021

Capturing and exploiting a content’s semantic is a key success factor for Web search. To this end, it is crucial to - ideally automatically - extract the core semantics of the data being processed and link this information with some formal representation, such as an ontology. By intertwining both, search becomes semantic by simultaneously allowing end-users a structured access to the data via the underlying ontology. Connecting both, we introduce the SEMANNOREX framework in order to provide semantically enriched access to a news corpus from Websites and Wikinews.
Extraction and Evaluation of Statistical Information from Social and Behavioral Science Papers
Authors: Sree Sai Teja Lanka, Sarah Michele Rajtmajer, Jian Wu, C. Lee Giles
Venue: WWW
Year: 2021

With substantial and continuing increases in the number of published papers across the scientific literature, development of reliable approaches for automated discovery and assessment of published findings is increasingly urgent. Tools which can extract critical information from scientific papers and metadata can support representation and reasoning over existing findings, and offer insights into replicability, robustness and generalizability of specific claims. In this work, we present a pipeline for the extraction of statistical information (p-values, sample size, number of hypotheses tested) from full-text scientific documents. We validate our approach on 300 papers selected from the social and behavioral science literatures, and suggest directions for next steps.
taka at the FinSBD-3 task: Tables and Figures Extraction using Object Detection Techniques
Authors: Tien-Dung Le
Venue: WWW
Year: 2021

FinSBD-3 is a shared task organized in the context of the 1st workshop on Financial Technology on the Web. The task focuses on extracting the entire structure of noisy PDF financial documents that include 1) sentences, lists, items, and organization of lists and items; 2) figures and tables; 3) headers and footers. This paper describes the approach that allows us to extract the figures and tables using their visual cues. We applied the object segmentation techniques in image processing to detect the location of figures and tables in the PDF files. A post-processing method is then executed in order to find exact content. The result shows the potential of this approach.
TLS 1.3 in Practice: How TLS 1.3 Contributes to the Internet
Authors: Hyunwoo Lee, Doowon Kim, Yonghwi Kwon
Venue: WWW
Year: 2021

Transport Layer Security (TLS) has become the norm for secure communication over the Internet. In August 2018, TLS 1.3, the latest version of TLS, was approved, providing improved security and performance of the previous TLS version. In this paper, we take a closer look at TLS 1.3 deployments in practice regarding adoption rate, security, performance, and implementation by applying temporal, spatial, and platform-based approaches on 687M connections. Overall, TLS 1.3 has rapidly been adopted mainly due to third-party platforms such as Content Delivery Networks (CDNs) makes a significant contribution to the Internet. In fact, it deprecates vulnerable cryptographic primitives and substantially reduces the time required to perform the TLS 1.3 full handshake compared to the TLS 1.2 handshake. We quantify these aspects and show TLS 1.3 is beneficial to websites that do not rely on the third-party platforms. We also review Common Vulnerabilities and Exposures (CVEs) regarding TLS libraries and show that many of recent vulnerabilities can be easily addressed by upgrading to TLS 1.3. However, some websites exhibit unstable support for TLS 1.3 due to multiple platforms with different TLS versions or migration to other platforms, which means that a website can show the lower TLS version at a certain time or from a certain region. Furthermore, we find that most of the implementations (including TLS libraries) do not fully support the new features of TLS 1.3 such as downgrade protection and certificate extensions.