OpenRaiser/Intern-Atlas huggingface dataset:
Fairness beyond "equal": The Diversity Searcher as a Tool to Detect and Enhance the Representation of Socio-political Actors in News Media
Authors: Bettina Berendt, Özgür Karadeniz, Stefan Mertens, Leen D&apos, Haenens
Venue: WWW
Year: 2021

“Fairness” is a multi-faceted concept that is contested within and across disciplines. In machine learning, it usually denotes some form of equality of measurable outcomes of algorithmic decision making. In this paper, we start from a viewpoint of sociology and media studies, which highlights that to even claim fair treatment, individuals and groups first have to be visible. We draw on a notion and a quantitative measure of diversity that expresses this wider requirement. We used the measure to design and build the Diversity Searcher, a Web-based tool to detect and enhance the representation of socio-political actors in news media. We show how the tool's combination of natural language processing and a rich user interface can help news producers and consumers detect and understand diversity-relevant aspects of representation, which can ultimately contribute to enhancing diversity and fairness in media. We comment on our observation that, through interactions with target users during the construction of the tool, NLP results and interface questions became increasingly important, such that the formal measure of diversity has become a catalyst for functionality, but in itself less important.
A Novel Macro-Micro Fusion Network for User Representation Learning on Mobile Apps
Authors: Shuqing Bian, Wayne Xin Zhao, Kun Zhou, Xu Chen, Jing Cai, Yancheng He, Xingji Luo, Ji-Rong Wen
Venue: WWW
Year: 2021

The evolution of mobile apps has greatly changed the way that we live. It becomes increasingly important to understand and model the users on mobile apps. Instead of focusing on some specific app alone, it has become a popular paradigm to study the user behavior on various mobile apps in a symbiotic environment. In this paper, we study the task of user representation learning with both macro and micro interaction data on mobile apps. Specifically, macro and micro interaction refer to user-app interaction or user-item interaction on some specific app, respectively. By combining the two kinds of user data, it is expected to derive a more comprehensive, robust user representation model on mobile apps. In order to effectively fuse the information across the macro and micro views, we propose a novel macro-micro fusion network for user representation learning on mobile apps. With a Transformer architecture as the base model, we design a representation fusion component that is able to capture the category-based semantic alignment at the user level. After such semantic alignment, the information across the two views can be adaptively fused in our approach. Furthermore, we adopt mutual information maximization to derive a self-supervised loss to enhance the learning of our fusion network. Extensive experiments with three downstream tasks on two real-world datasets have demonstrated the effectiveness of our approach.
EMR: Scalable Clustering of Big HR Data using Evolutionary MapReduce
Authors: Mahdi Bohlouli, Zhonghua He
Venue: WWW
Year: 2021

Nowadays, the volume and variety of generated data, how to process it and accordingly create value through scalable analytics are main challenges to industries and real-world practices such as talent analytics. For instance, large enterprises and job centres have to progress data intensive matching of job seekers to various job positions at the same time. In other words, it should result in the large scale assignment of best-fit (right) talents (Person) with right expertise (Profession) to the right job (Position) at the right time (Period). We call this definition as a 4P rule in this paper. All enterprises should consider 4P rule in their daily recruitment processes towards efficient workforce development strategies. Such consideration demands integrating large volumes of disparate data from various sources and strongly needs the use of scalable algorithms and analytics. The diversity of the data in human resource management requires speeding up analytical processes. The main challenge here is not only how and where to store the data, but also the analysing it towards creating value (knowledge discovery). In this paper, we propose a generic Career Knowledge Representation (CKR) model in order to be able to model most competences that exist in a wide variety of careers. A regenerated job qualification data of 15 million employees with 84 dimensions (competences) from real HRM data has been used in test and evaluation of proposed Evolutionary MapReduce K-Means method in this research. This proposed EMR method shows faster and more accurate experimental results in comparison to similar approaches and has been tested with real large scale datasets and achieved results are already discussed.
SciBiD: Novel Scientometrics and NoSQL-enabled Scalable and domain-specific Analysis of Big Scholar Data
Authors: Mahdi Bohlouli, Jonathan Hermann, Fabian Sunnus
Venue: WWW
Year: 2021

It is quite important how to correlate and find out relationships between how people grow up and succeed in their research field compared to their field’s grows. In many cases, people refer only to indices such as citation count, h-index, i10-index and compare scientists from different fields in a similar situation with the same variables. It is not a fair comparison since fields are different in development and being cited. In this paper, we used the acceleration concept from physics and propose a new method with new metrics to efficiently and fairly evaluate scientists according to the real-time analysis of their recent status compared to their field’s grows. This considers various inputs such as whether a person is beginner scientists or professional and applies all such key inputs in the evaluation. The evaluation is also over time. The results showed better evaluation compared to state-of-the-art metrics.
Constructing Explainable Opinion Graphs from Reviews
Authors: Nofar Carmeli, Xiaolan Wang, Yoshihiko Suhara, Stefanos Angelidis, Yuliang Li, Jinfeng Li, Wang-Chiew Tan
Venue: WWW
Year: 2021

The Web is a major resource of both factual and subjective information. While there are significant efforts to organize factual information into knowledge bases, there is much less work on organizing opinions, which are abundant in subjective data, into a structured format. We present ExplainIt, a system that extracts and organizes opinions into an opinion graph, which are useful for downstream applications such as generating explainable review summaries and facilitating search over opinion phrases. In such graphs, a node represents a set of semantically similar opinions extracted from reviews and an edge between two nodes signifies that one node explains the other. ExplainIt mines explanations in a supervised method and groups similar opinions together in a weakly supervised way before combining the clusters of opinions together with their explanation relationships into an opinion graph. We experimentally demonstrate that the explanation relationships generated in the opinion graph are of good quality and our labeled datasets for explanation mining and grouping opinions are publicly available at https://github.com/megagonlabs/explainit.
Challenges and Opportunities in Using Data Science for Homelessness Service Provision
Authors: Charalampos Chelmis, Wenting Qi, Wonhyung Lee
Venue: WWW
Year: 2021

Homelessness service provision, a task of great societal relevance, requires solutions to several urgent problems facing our humanity. Data science, that has recently emerged as a potential catalyst in addressing long standing problems related to human services, offers immense potential. However, homelessness service provision presents unignorable challenges (e.g., assessment methods and data bias) that are are seldom found in other domains, requiring cross-discipline collaborations and cross-pollination of ideas. This work summarizes the challenges offered by homelessness service provision tasks, as well as the problems and the opportunities that exist for advancing both data science and human services. We begin by highlighting typical goals of homelessness service provision, and subsequently describe homelessness service data along with their properties, that make it challenging to use traditional data science methods. Along the way, we discuss some of the existing efforts and promising directions for data science, and conclude by discussing the importance of a deep collaboration between data science and domain experts for synergistic advancements in both disciplines.
Towards a Better Understanding of Query Reformulation Behavior in Web Search
Authors: Jia Chen, Jiaxin Mao, Yiqun Liu, Fan Zhang, Min Zhang, Shaoping Ma
Venue: WWW
Year: 2021

As queries submitted by users directly affect search experiences, how to organize queries has always been a research focus in Web search studies. While search request becomes complex and exploratory, many search sessions contain more than a single query thus reformulation becomes a necessity. To help users better formulate their queries in these complex search tasks, modern search engines usually provide a series of reformulation entries on search engine result pages (SERPs), i.e., query suggestions and related entities. However, few existing work have thoroughly studied why and how users perform query reformulations in these heterogeneous interfaces. Therefore, whether search engines provide sufficient assistance for users in reformulating queries remains under-investigated. To shed light on this research question, we conducted a field study to analyze fine-grained user reformulation behaviors including reformulation type, entry, reason, and the inspiration source with various search intents. Different from existing efforts that rely on external assessors to make judgments, in the field study we collect both implicit behavior signals and explicit user feedback information. Analysis results demonstrate that query reformulation behavior in Web search varies with the type of search tasks. We also found that the current query suggestion/related query recommendations provided by search engines do not offer enough help for users in complex search tasks. Based on the findings in our field study, we design a supervised learning framework to predict: 1) the reason behind each query reformulation, and 2) how users organize the reformulated query, both of which are novel challenges in this domain. This work provides insight into complex query reformulation behavior in Web search as well as the guidance for designing better query suggestion techniques in search engines.
Cross-domain Knowledge Distillation for Retrieval-based Question Answering Systems
Authors: Cen Chen, Chengyu Wang, Minghui Qiu, Dehong Gao, Linbo Jin, Wang Li
Venue: WWW
Year: 2021

Question Answering (QA) systems have been extensively studied in both academia and the research community due to their wide real-world applications. When building such industrial-scale QA applications, we are facing two prominent challenges, i.e., i) lacking a sufficient amount of training data to learn an accurate model and ii) requiring high inference speed for online model serving. There are generally two ways to mitigate the above-mentioned problems. One is to adopt transfer learning to leverage information from other domains; the other is to distill the “dark knowledge” from a large teacher model to small student models. The former usually employs parameter sharing mechanisms for knowledge transfer, but does not utilize the “dark knowledge” of pre-trained large models. The latter usually does not consider the cross-domain information from other domains. We argue that these two types of methods can be complementary to each other. Hence in this work, we provide a new perspective on the potential of the teacher-student paradigm facilitating cross-domain transfer learning, where the teacher and student tasks belong to heterogeneous domains, with the goal to improve the student model’s performance in the target domain. Our framework considers the “dark knowledge” learned from large teacher models and also leverages the adaptive hints to alleviate the domain differences between teacher and student models. Extensive experiments have been conducted on two text matching tasks for retrieval-based QA systems. Results show the proposed method has better performance than the competing methods including the existing state-of-the-art transfer learning methods. We have also deployed our method in an online production system and observed significant improvements compared to the existing approaches in terms of both accuracy and cross-domain robustness.
Timing-Driven X-architecture Steiner Minimum Tree Construction Based on Social Learning Multi-Objective Particle Swarm Optimization
Authors: Xiaohua Chen, Ruping Zhou, Genggeng Liu, Zhen Chen, Wenzhong Guo
Venue: WWW
Year: 2021

The construction of timing-driven Steiner minimum tree is a critical issue in VLSI routing design. Meanwhile, since the interconnection model of X-architecture can make full use of routing resources compared to the traditional Manhattan architecture, constructing a Timing-Driven X-architecture Steiner Minimum Tree (TDXSMT) is of great significance to improving routing performance. In this paper, an efficient algorithm based on Social Learning Multi-Objective Particle Swarm Optimization (SLMOPSO) is proposed to construct a TDXSMT with minimizing the maximum source-to-sink pathlength. An X-architecture Prim-Dijkstra model is presented to construct an initial Steiner tree which can optimize both the wirelength and the maximum source-to-sink pathlength. In order to find a better solution, an SLMOPSO method based on the nearest and best select strategy is presented to improve the global exploration capability of the algorithm. Besides, the mutation and crossover operators are utilized to achieve the discrete particle update process, thereby better solving the discrete TDXSMT problem. The experimental results indicate that the proposed algorithm has an excellent trade-off between the wirelength and maximum source-to-sink pathlength of the routing tree and can greatly optimize the timing delay.
PolyU-CBS at the FinSim-2 Task: Combining Distributional, String-Based and Transformers-Based Features for Hypernymy Detection in the Financial Domain
Authors: Emmanuele Chersoni, Chu-Ren Huang
Venue: WWW
Year: 2021

In this contribution, we describe the systems presented by the PolyU CBS Team at the second Shared Task on Learning Semantic Similarities for the Financial Domain (FinSim-2), where participating teams had to identify the right hypernyms for a list of target terms from the financial domain. For this task, we ran our classification experiments with several distributional, string-based, and Transformer features. Our results show that a simple logistic regression classifier, when trained on a combination of word embeddings, semantic and string similarity metrics and BERT-derived probabilities, achieves a strong performance (above 90%) in financial hypernymy detection.
Structural Analysis of Wikigraph to Investigate Quality Grades of Wikipedia Articles
Authors: Anamika Chhabra, Shubham Srivastava, S. R. S. Iyengar, Poonam Saini
Venue: WWW
Year: 2021

The quality of Wikipedia articles is manually evaluated which is time inefficient as well as susceptible to human bias. An automated assessment of these articles may help in minimizing the overall time and manual errors. In this paper, we present a novel approach based on the structural analysis of Wikigraph to automate the estimation of the quality of Wikipedia articles. We examine the network built using the complete set of English Wikipedia articles and identify the variation of network signatures of the articles with respect to their quality. Our study shows that these signatures are useful for estimating the quality grades of un-assessed articles with an accuracy surpassing the existing approaches in this direction. The results of the study may help in reducing the need for human involvement for quality assessment tasks.
Assessing the quality of health-related Wikipedia articles with generic and specific metrics
Authors: Luis Couto, Carla Teixeira Lopes
Venue: WWW
Year: 2021

Wikipedia is an online, free, multi-language, and collaborative encyclopedia, currently one of the most significant information sources on the web. The open nature of Wikipedia contributions raises concerns about the quality of its information. Previous studies have addressed this issue using manual evaluations and proposing generic measures for quality assessment. In this work, we focus on the quality of health-related content. For this purpose, we use general and health-specific features from Wikipedia articles to propose health-specific metrics. We evaluate these metrics using a set of Wikipedia articles previously assessed by WikiProject Medicine. We conclude that it is possible to combine generic and specific metrics to determine health-related content’s information quality. These metrics are computed automatically and can be used by curators to identify quality issues. Along with the explored features, these metrics can also be used in approaches that automatically classify the quality of Wikipedia health-related articles.
Search Engines vs. Symptom Checkers: A Comparison of their Effectiveness for Online Health Advice
Authors: Sebastian Cross, Ahmed Mourad, Guido Zuccon, Bevan Koopman
Venue: WWW
Year: 2021

Increasingly, people go online to seek health advice. They commonly use the symptoms they are experiencing to identify the health conditions they may have (self-diagnosis task) as well as to determine an appropriate action to take (triaging task); e.g., should they seek emergent medical attention or attempt to treat themselves at home? This paper investigates the effectiveness of two of the most common methods people use for self-diagnosis and triaging: online symptom checkers and traditional web search engines. To this end, we conducted a user study with 64 real-world users performing 8 simulated self-diagnosis tasks. Participants were exposed to both a representative symptom checker and a search engine. The results of our study provides empirical evidence for whether using a search engine for health information improves people’s understanding of their health condition and their ability to act on them, compared to interacting with a symptom checker, which bases its interaction model on a question-answering process. Additionally, recorded answers to qualitative questionnaires from study participants provide insights into which style of interaction and system they prefer to use for obtaining medical information, and how helpful they thought each system was. These findings can help inform the development of better search engines and symptom checkers that support people seeking health advice online.
Does Gender Matter in the News? Detecting and Examining Gender Bias in News Articles
Authors: Jamell Dacon, Haochen Liu
Venue: WWW
Year: 2021

To attract unsuspecting readers, news article headlines and abstracts are often written with speculative sentences or clauses. Male dominance in the news is very evident, whereas females are seen as “eye candy” or “inferior”, and are underrepresented and under-examined within the same news categories as their male counterparts. In this paper, we present an initial study on gender bias in news abstracts in two large English news datasets used for news recommendation and news classification. We perform three large-scale, yet effective text-analysis fairness measurements on 296,965 news abstracts. In particular, to our knowledge we construct two of the largest benchmark datasets of possessive (gender-specific and gender-neutral) nouns and attribute (career-related and family-related) words datasets1 which we will release to foster both bias and fairness research aid in developing fair NLP models to eliminate the paradox of gender bias. Our studies demonstrate that females are immensely marginalized and suffer from socially-constructed biases in the news. This paper individually devises a methodology whereby news content can be analyzed on a large scale utilizing natural language processing (NLP) techniques from machine learning (ML) to discover both implicit and explicit gender biases.
IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation
Authors: Ovidiu Dan, Vaibhav Parikh, Brian D. Davison
Venue: WWW
Year: 2021

Many online services, including search engines, content delivery networks, ad networks, and fraud detection utilize IP geolocation databases to map IP addresses to their physical locations. However, IP geolocation databases are often inaccurate. We present a novel IP geolocation technique based on combining propagating IP location information through traceroutes with IP interpolation. Using a large ground truth set, we show that physical locations of IP addresses can be propagated along traceroute paths. We also experiment with and expand upon the concept of IP range location interpolation, where we use the location of individual addresses in an IP range to assign a location to the entire range. The results show that our approach significantly outperforms commercial geolocation by up to 31 percentage points. We open source several components to aid in reproducing our results.
A Brief Analysis of Bengali Wikipedia's Journey to 100, 000 Articles
Authors: Ankan Ghosh Dastider
Venue: WWW
Year: 2021

The Bengali Wikipedia has recently crossed the milestone of 100,000 articles after a journey of almost 17 years in December 2020. In this journey, the Bengali language edition of the world’s largest encyclopedia has experienced multiple changes with a promising increase in the overall performance considering the growth of community members and content. This paper analyzes the various associating factors throughout this journey including the number of active editors, number of content pages, pageview, etc., along with the connection to outreach activities with these parameters. The gender gap has been a worldwide problem and is quite prevalent in Bengali Wikipedia as well, which seems to be unchanged over the years and consequentially, leaving a conspicuous disparity in the movement. The paper inspects the present scenario of Bengali Wikipedia through quantitative factors with a relative comparison with other regional languages.
Visualising Scientific Topic Evolution
Authors: Panagiotis Deligiannis, Thanasis Vergoulis, Serafeim Chatzopoulos, Christos Tryfonopoulos
Venue: WWW
Year: 2021

The automatic extraction of topics is a standard technique for summarizing text corpora from various domains (e.g., news articles, transport or logistic reports, scientific publications) that has several applications. Since, in many cases, topics are subject to continuous change there is the need to monitor the evolution of a set of topics of interest, as the corresponding corpora are updated. The evolution of scientific topics, in particular, is of great interest for researchers, policy makers, fund managers, and other professionals/engineers in the research and academic community. In this work, we demonstrate a prototype that provides intuitive visualisations for the evolution of scientific topics providing insights about topic transformation, merging, and splitting during the recent years. Although the prototype works on top of a scientific text corpus, its implementation is generic and can be easily applied on texts from other domains, as well.