OpenRaiser/Intern-Atlas huggingface dataset:
Open traffic lights: a strategy for publishing and preserving traffic lights data
Authors: Brecht Van de Vyvere, Pieter Colpaert, Erik Mannens, Ruben Verborgh
Venue: WWW
Year: 2019

For better traffic flow and making better policy decisions, the city of Antwerp is connecting traffic lights to the Internet. The live “time to green” only tells a part of the story: also the historical values need to be preserved and need to be made accessible to everyone. We propose (i) an ontology for describing the topology of an intersection and the signal timing of traffic lights, (ii) a specification to publish these historical and live data with Linked Data Fragments and (iii) a method to preserve the published data in the long-term. We showcase the applicability of our specification with the opentrafficlights.org project where an end-user can see the live count-down as well as a line chart showing the historic “time to green” of a traffic light. We found that publishing traffic lights data as time sorted Linked Data Fragments allow synchronizing and reusing an archive to retrieve historical observations. Long-term preservation with tape storage becomes feasible when archives shift from byte preservation to knowledge preservation by combining Linked Data Fragments. This is a print-version of a paper first written for the Web. The Web-version is available at https://brechtvdv.github.io/Article-Open-Traffic-Lights
Misinfosec
Authors: Christopher R. Walker, Sara-Jayne Terp, Pablo C. Breuer, Courtney L. Crooks
Venue: WWW
Year: 2019

State actors, private influence operators and grassroots groups are all exploiting the openness and reach of the Internet to manipulate populations at a distance, extending their decades-long struggle for “hearts and minds” via propaganda, influence operations and information warfare. Computational propaganda fueled by AI makes matters worse. The structure and propagation patterns of these attacks have many similarities to those seen in information security and computer hacking. The Credibility Coalition's MisinfosecWG working group is analyzing those similarities, including information security frameworks that could give the truth-based community better ways to describe, identify and counter misinformation-based attacks. Specifically, we place misinformation components into a framework commonly used to describe information security incidents. We anticipate that our work will give responders the ability to transfer other information security principles to the misinformation sphere, and to plan defenses and countermoves .
Generalists and Specialists: Using Community Embeddings to Quantify Activity Diversity in Online Platforms
Authors: Isaac Waller, Ashton Anderson
Venue: WWW
Year: 2019

In many online platforms, people must choose how broadly to allocate their energy. Should one concentrate on a narrow area of focus, and become a specialist, or apply oneself more broadly, and become a generalist? In this work, we propose a principled measure of how generalist or specialist a user is, and study behavior in online platforms through this lens. To do this, we construct highly accurate community embeddings that represent communities in a high-dimensional space. We develop sets of community analogies and use them to optimize our embeddings so that they encode community relationships extremely well. Based on these embeddings, we introduce a natural measure of activity diversity, the GS-score. Applying our embedding-based measure to online platforms, we observe a broad spectrum of user activity styles, from extreme specialists to extreme generalists, in both community membership on Reddit and programming contributions on GitHub. We find that activity diversity is related to many important phenomena of user behavior. For example, specialists are much more likely to stay in communities they contribute to, but generalists are much more likely to remain on platforms as a whole. We also find that generalists engage with significantly more diverse sets of users than specialists do. Furthermore, our methodology leads to a simple algorithm for community recommendation, matching state-of-the-art methods like collaborative filtering. Our methods and results introduce an important new dimension of online user behavior and shed light on many aspects of online platform use.
The Silent Majority Speaks: Inferring Silent Users' Opinions in Online Social Networks
Authors: Lei Wang, Jianwei Niu, Xuefeng Liu, Kaili Mao
Venue: WWW
Year: 2019

With the blossoming of social networking platforms like Twitter and Facebook, how to infer the opinions of online social network users on specific topics they had not directly given yet, has received much attention. Existing solutions mainly rely on one's previous posted messages. However, recent studies show that over 40% of users opt to be silent all or most of the time and post very few messages. Consequently, the performance of existing solutions will drop dramatically when they are applied to infer silent users' opinions, and how to infer the opinions of these silent users becomes a meaningful while challenging task. Inspired by the collaborative filtering techniques in cold-start recommendations, we infer the opinions of silent users by leveraging the text content posted by active users and their relationships between silent users. Specifically, we first consider both observed and pseudo relationships among users, and cluster users into communities in order to extract various kinds of features for opinion inference. We then design a coupled sparse matrix factorization (CSMF) model to capture the complex relations among these features. Extensive experiments on real-world data from Twitter show that our CSMF model achieves over 80% accuracy for the inference of silent users' opinions.
Towards Efficient Sharing: A Usage Balancing Mechanism for Bike Sharing Systems
Authors: Shuai Wang, Tian He, Desheng Zhang, Yunhuai Liu, Sang Hyuk Son
Venue: WWW
Year: 2019

With the rapid development of sharing economy, massive sharing systems such as Uber, Airbnb, and bikeshare have percolated into people's daily life. The sharing economy, at its core, is to achieve efficient use of resources. The actual usage of shared resources, however, is unclear to us. Little measurement or analysis, if any, has been conducted to investigate the resource usage status with the large-scale data collected from these sharing systems. In this paper, we analyze the bike usage status in three typical bikeshare systems based on 140-month multi-event data. Our analysis shows that the most used 20% of bikes account for 45% of usage, while the least used 20% of bikes account for less than 1% of usage. To efficiently utilize shared bikes, we propose a usage balancing design called eShare which has three components: (i) a statistical model based on archived data to infer historical usage; (ii) an entropy-based prediction model based on both real-time and archived data to infer future usage; (iii) a model-driven optimal calibration engine for bike selection to dynamically balance usage. We develop an ID swapping based evaluation methodology and measure the efficiency of eShare with data from three systems including the world's largest bikeshare system with 84,000 bikes and 3,300 stations. Our results show that eShare not only fully utilizes shared bikes but also improves service quality.
Preliminary Investigation of Alleviating User Cold-Start Problem in E-commerce with Deep Cross-Domain Recommender System
Authors: Hanxin Wang, Daichi Amagata, Takuya Maekawa, Takahiro Hara, Hao Niu, Kei Yonekawa, Mori Kurokawa
Venue: WWW
Year: 2019

Many current applications use recommender systems to predict user preferences, aiming at improving user experience and increasing the amount of sales and the usage time that users spent on the application. However, it is not an easy task to recommend items to new users accurately because of the user cold-start problem, which means that recommendation performance will degrade on users with little interaction, particularly for latent users who have never used the service before. In this work, we combine an online shopping domain with information from an ad platform, and then apply deep learning to build a cross-domain recommender system based on shared users of these two domains, to alleviate the user cold-start problem. Experimental results show the effectiveness of our deep cross-domain recommender system on handling user cold-start problem. By our framework, it is possible to recommend products to users of other domain through ad distribution in a more accurate level, and to increase sales amount of online shopping.
A Family of Fuzzy Orthogonal Projection Models for Monolingual and Cross-lingual Hypernymy Prediction
Authors: Chengyu Wang, Yan Fan, Xiaofeng He, Aoying Zhou
Venue: WWW
Year: 2019

Hypernymy is a semantic relation, expressing the “is-a” relation between a concept and its instances. Such relations are building blocks for large-scale taxonomies, ontologies and knowledge graphs. Recently, much progress has been made for hypernymy prediction in English using textual patterns and/or distributional representations. However, applying such techniques to other languages is challenging due to the high language dependency of these methods and the lack of large training datasets of lower-resourced languages. In this work, we present a family of fuzzy orthogonal projection models for both monolingual and cross-lingual hypernymy prediction. For the monolingual task, we propose a Multi-Wahba Projection (MWP) model to distinguish hypernymy vs. non-hypernymy relations based on word embeddings. This model establishes distributional fuzzy mappings from embeddings of a term to those of its hypernyms and non-hypernyms, which consider the complicated linguistic regularities of these relations. For cross-lingual hypernymy prediction, a Transfer MWP (TMWP) model is proposed to transfer the semantic knowledge from the source language to target languages based on neural word translation. Additionally, an Iterative Transfer MWP (ITMWP) model is built upon TMWP, which augments the training sets of target languages when target languages are lower-resourced with limited training data. Experiments show i) MWP outperforms previous methods over two hypernymy prediction tasks for English; and ii) TMWP and ITMWP are effective to predict hypernymy over seven non-English languages.
Quality-Sensitive Training! Social Advertisement Generation by Leveraging User Click Behavior
Authors: Yongzhen Wang, Heng Huang, Yuliang Yan, Xiaozhong Liu
Venue: WWW
Year: 2019

Social advertisement has emerged as a viable means to improve purchase sharing in the context of e-commerce. However, humanly generating lots of advertising scripts can be prohibitive to both e-platforms and online sellers, and moreover, developing the desired auto-generator will need substantial gold-standard training samples. In this paper, we put forward a novel seq2seq model to generate social advertisements automatically, in which a quality-sensitive loss function is proposed based on user click behavior to differentiate training samples of varied qualities. Our motivation is to leverage the clickthrough data as a kind of quality indicator to measure the textual fitness of each training sample quantitatively, and only those ground truths that satisfy social media users will be considered the eligible and able to optimize the social advertisement generation. Specifically, under the qualified case, the ground truth should be utilized to supervise the whole training phase as much as possible, whereas in the opposite situation, the generated result ought to preserve the semantics of original input to the greatest extent. Simulation experiments on a large-scale dataset demonstrate that our approach achieves a significant superiority over two existing methods of distant supervision and three state-of-the-art NLG solutions.
Learning Task-Specific City Region Partition
Authors: Hongjian Wang, Porter Jenkins, Hua Wei, Fei Wu, Zhenhui Li
Venue: WWW
Year: 2019

The proliferation of publicly accessible urban data provide new insights on various urban tasks. A frequently used approach is to treat each region as a data sample and build a model over all the regions to observe the correlations between urban features (e.g., demographics) and the target variable (e.g., crime count). To define regions, most existing studies use fixed grids or pre-defined administrative boundaries (e.g., census tracts or community areas). In reality, however, definitions of regions should be different depending on tasks (e.g., regional crime count prediction vs. real estate prices estimation). In this paper, we propose a new problem of task-specific city region partitioning, aiming to find the best partition in a city w.r.t. a given task. We prove this is an NP-hard search problem with no trivial solution. To learn the partition, we first study two variants of Markov Chain Monte Carlo (MCMC). We further propose a reinforcement learning scheme for effective sampling the search space. We conduct experiments on two real datasets in Chicago (i.e., crime count and real estate price) to demonstrate the effectiveness of our proposed method.
Understanding the Evolution of Mobile App Ecosystems: A Longitudinal Measurement Study of Google Play
Authors: Haoyu Wang, Hao Li, Yao Guo
Venue: WWW
Year: 2019

The continuing expansion of mobile app ecosystems has attracted lots of efforts from the research community. However, although a large number of research studies have focused on analyzing the corpus of mobile apps and app markets, little is known at a comprehensive level on the evolution of mobile app ecosystems. Because the mobile app ecosystem is continuously evolving over time, understanding the dynamics of app ecosystems could provide unique insights that cannot be achieved through studying a single static snapshot. In this paper, we seek to shed light on the dynamics of mobile app ecosystems. Based on 5.3 million app records (with both app metadata and apks) collected from three snapshots of Google Play over more than three years, we conduct the first study on the evolution of app ecosystems from different aspects. Our results suggest that although the overall ecosystem shows promising progress in regard of app popularity, user ratings, permission usage and privacy policy declaration, there still exists a considerable number of unsolved issues including malicious apps, update issues, third-party tracking threats, improper app promotion behaviors, and spamming/malicious developers. Our study shows that understanding the evolution of mobile app ecosystems can help developers make better decision on developing and releasing apps, provide insights for app markets to identifying misbehaviors, and help mobile users to choose desired apps.
Learning Binary Hash Codes for Fast Anchor Link Retrieval across Networks
Authors: Yongqing Wang, Huawei Shen, Jinhua Gao, Xueqi Cheng
Venue: WWW
Year: 2019

Users are usually involved in multiple social networks, without explicit anchor links that reveal the correspondence among different accounts of the same user across networks. Anchor link prediction aims to identify the hidden anchor links, which is a fundamental problem for user profiling, information cascading, and cross-domain recommendation. Although existing methods perform well in the accuracy of anchor link prediction, the pairwise search manners on inferring anchor links suffer from big challenge when being deployed in practical systems. To combat the challenges, in this paper we propose a novel embedding and matching architecture to directly learn binary hash code for each node. Hash codes offer us an efficient index to filter out the candidate node pairs for anchor link prediction. Extensive experiments on synthetic and real world large-scale datasets demonstrate that our proposed method has high time efficiency without loss of competitive prediction accuracy in anchor link prediction.
Aspect-level Sentiment Analysis using AS-Capsules
Authors: Yequan Wang, Aixin Sun, Minlie Huang, Xiaoyan Zhu
Venue: WWW
Year: 2019

Aspect-level sentiment analysis aims to provide complete and detailed view of sentiment analysis from different aspects. Existing solutions usually adopt a two-staged approach: first detecting aspect category in a document, then categorizing the polarity of opinion expressions for detected aspect(s). Inevitably, such methods lead to error accumulation. Moreover, aspect detection and aspect-level sentiment classification are highly correlated with each other. The key issue here is how to perform aspect detection and aspect-level sentiment classification jointly, and effectively. In this paper, we propose the aspect-level sentiment capsules model (AS-Capsules), which is capable of performing aspect detection and sentiment classification simultaneously, in a joint manner. AS-Capsules utilizes the correlation between aspect and sentiment through shared components including capsule embedding, shared encoders, and shared attentions. AS-Capsules is also capable of communicating with different capsules through a shared Recurrent Neural Network (RNN). More importantly, AS-Capsules model does not require any linguistic knowledge as additional input. Instead, through the attention mechanism, this model is able to attend aspect related words and sentiment words corresponding to different aspect(s). Experiments show that the AS-Capsules model achieves state-of-the-art performances on a benchmark dataset for aspect-level sentiment analysis.
FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System
Authors: Jianyu Wang, Rui Wen, Chunming Wu, Yu Huang, Jian Xiong
Venue: WWW
Year: 2019

Online review system enables users to submit reviews about the products. However, the openness of Internet and monetary rewards for crowdsourcing tasks stimulate a large number of fraudulent users to write fake reviews and post advertisements to interfere the rank of apps. Existing methods for detecting spam reviews have been successful but they usually aims at e-commerce (e.g. Amazon, eBay) and recommendation (e.g. Yelp, Dianping) systems. Since the behaviors of fraudulent users are complexity and varying across different review platforms, existing methods are not suitable for fraudster detection in online app review system. To shed light on this question, we are among the first to analyze the intentions of fraudulent users from different review platforms and categorize them by utilizing characteristics of contents (similarity, special symbols) and behaviors (timestamps, device, login status). With a comprehensive analysis of spamming activities and relationships between normal and malicious users, we design and present FdGars, the first graph convolutional network approach for fraudster detection in online app review system. Then we evaluate FdGars on real-world large-scale dataset (with 82,542 nodes and 42,433,134 edges) from Tencent App Store. The result demonstrates that F1-score of FdGars can achieve 0.938+, which outperforms several baselines and state-of-art fraudsters detecting methods. Moreover, we deploy FdGars on Tencent Beacon Anti-Fraud Platform to show its effectiveness and scalability. To the best of our knowledge, this is the first work to use graph convolutional networks for fraudster detection in the large-scale online app review system. It is worth to mention that FdGars can uncover malicious accounts even the data lack of labels in anti-spam tasks.
Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems
Authors: Chenyang Wang, Min Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma
Venue: WWW
Year: 2019

Repeat consumption is a common scenario in daily life, such as repurchasing items and revisiting websites, and is a critical factor to be taken into consideration for recommender systems. Temporal dynamics play important roles in modeling repeat consumption. It is noteworthy that for items with distinct lifetimes, consuming tendency for the next one fluctuates differently with time. For example, users may repurchase milk weekly, but it is possible to repurchase mobile phone after a long period of time. Therefore, how to adaptively incorporate various temporal patterns of repeat consumption into a holistic recommendation model has been a new and important problem. In this paper, we propose a novel unified model with introducing Hawkes Process into Collaborative Filtering (CF). Different from most previous work which ignores various time-varying patterns of repeat consumption, the model explicitly addresses two item-specific temporal dynamics: (1) short-term effect and (2) life-time effect, which is named as Short-Term and Life-Time Repeat Consumption (SLRC) model. SLRC learns importance of the two factors for each item dynamically by interpretable parameters. According to extensive experiments on four datasets in diverse scenarios, including two public collections, SLRC is superior to previous approaches for repeat consumption modeling. Moreover, due to the high flexibility of SLRC, various existing recommendation algorithms are shown to be easily leveraged in this model to achieve significant improvements. In addition, SLRC is good at balancing recommendation for novel items and consumed items (exploration and exploitation). We also find that the learned parameters is highly interpretable, and hence the model is able to be leveraged to discover items' lifetimes, and to distinguish different types of items such as durable and fast-moving consumer goods.
Enlisting the Public to Build a Healthier Web Information Commons
Authors: Claire Wardle
Venue: WWW
Year: 2019

Over the past three years, platforms, governments and a plethora of nonprofit initiatives have prioritized fighting online misinformation through a variety of different means. Yet the current framework is too fragmented to deliver global results. The big tech platforms have data, but no public accountability. Governments (mostly) have democratic legitimacy, but little information on what is actually going on in the platforms they're itching to regulate. And nonprofit initiatives too often lack the scale to affect change at the level needed. What if we came up with a dramatically new deliberative process that involves a global community of concerned citizens ready to share information and participate in consultations to improve collective decision-making? What if a more accountable, diverse and verifiable Web were still possible?
Emojis in Sentence Processing: An Electrophysiological Approach
Authors: Benjamin Weissman
Venue: WWW
Year: 2019

This paper discusses two multi-experiment studies using the ERP methodology to investigate neural correlates of processing linguistic emojis. The first study examined the use of wink emojis used to mark irony and found the same ERP response complex that has been found in response to word-generated irony. Contingent upon individual differences in interpretation, these emojis are processed the same way as ironic words. The second study investigated the prediction of non-face emojis substituted for nouns. When predictability was high, unexpected emojis elicited the same ERP response patterns as words. Overall, the results of these two studies suggest that emojis used linguistically are processed in the same way as words and that individuals can integrate input from multiple modalities into a holistic representation of a single utterance.