OpenRaiser/Intern-Atlas huggingface dataset:
Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network
Authors: Shumin Deng, Ningyu Zhang, Wen Zhang, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen
Venue: WWW
Year: 2019

Deep neural networks have achieved promising results in stock trend prediction. However, most of these models have two common drawbacks, including (i) current methods are not sensitive enough to abrupt changes of stock trend, and (ii) forecasting results are not interpretable for humans. To address these two problems, we propose a novel Knowledge-Driven Temporal Convolutional Network (KDTCN) for stock trend prediction and explanation. Firstly, we extract structured events from financial news, and utilize external knowledge from knowledge graph to obtain event embeddings. Then, we combine event embeddings and price values together to forecast stock trend. We evaluate the prediction accuracy to show how knowledge-driven events work on abrupt changes. We also visualize the effect of events and linkage among events based on knowledge graph, to explain why knowledge-driven events are common sources of abrupt changes. Experiments demonstrate that KDTCN can (i) react to abrupt changes much faster and outperform state-of-the-art methods on stock datasets, as well as (ii) facilitate the explanation of prediction particularly with abrupt changes.
Heterographic Pun Recognition via Pronunciation and Spelling Understanding Gated Attention Network
Authors: Yufeng Diao, Hongfei Lin, Liang Yang, Xiaochao Fan, Di Wu, Dongyu Zhang, Kan Xu
Venue: WWW
Year: 2019

Heterographic pun plays a critical role in human writing and literature, which usually has a similar sounding or spelling structure. It is important and difficult research to recognize the heterographic pun because of the ambiguity. However, most existing methods for this task only focus on designing features with rule-based or machine learning methods. In this paper, we propose an end-to-end computational approach - Pronunciation Spelling Understanding Gated Attention (PSUGA) network. For pronunciation, we exploit the hierarchical attention model with phoneme embedding. While for spelling, we consider the character-level, word-level, tag-level, position-level and contextual-level embedding with attention model. To deal with the two parts, we present a gated attention mechanism to control the information integration. We have conducted extensive experiments on SemEval2017 task7 and Pun of the Day datasets. Experimental results show that our approach significantly outperforms state-of-the-art methods.
Car Theft Reports: a Temporal Analysis from a Social Media Perspective
Authors: Juglar Diaz, Barbara Poblete
Venue: WWW
Year: 2019

Complex human behaviors related to crime require multiple sources of information to understand them. Social Media is a place where people share opinions and news. This allows events in the physical world like crimes to be reflected on Social Media. In this paper we study crimes from the perspective of Social Media, specifically car theft and Twitter. We use data of car theft reports from Twitter and car insurance companies in Chile to perform a temporal analysis. We found that there is an increasing correlation in recent years between the number of car theft reports in Twitter and data collected from insurance companies. We performed yearly, monthly, daily and hourly analyses. Though Twitter is an unstructured source and very noisy, it allows you to estimate the volume of thefts that are reported by the insurers. We experimented with a Moving Average to predict the tendency in the number of car theft reported to insurances using Twitter data and found that one month is the best time window for prediction.
Epitaph or Breaking News? Analyzing and Predicting the Stability of Knowledge Base Properties
Authors: Ioannis Dikeoulias, Jannik Strötgen, Simon Razniewski
Venue: WWW
Year: 2019

Knowledge bases (KBs) contain huge amounts of facts about entities, their properties, and relations between them. They are thus the key asset in any intelligent system for tasks such as structured search and question answering. However, due to dynamics in the real world, properties and relations change over time, and stored knowledge may become outdated. While KB information evolves steadily, there is no information whether or not a KB property might be subject to change with high probability or whether it is likely to be stable. Systems exploiting KB information, however, could benefit a lot if they had access to this kind of information. In this paper, we analyze and predict the stability of KB entries, which allows to accompany entries with stability scores. Our predictive model exploits entity-based features and learns through historic data. A particular challenge to determine stability scores is that KB entries are not only added or modified due to real-world changes but also to reduce the incompleteness of KBs in general. Nevertheless, our evaluation of sample properties demonstrates the effectiveness of our method for predicting the one-year stability of KB properties.
Evaluating Login Challenges as aDefense Against Account Takeover
Authors: Periwinkle Doerfler, Kurt Thomas, Maija Marincenko, Juri Ranieri, Yu Jiang, Angelika Moscicki, Damon McCoy
Venue: WWW
Year: 2019

In this paper, we study the efficacy of login challenges at preventing account takeover, as well as evaluate the amount of friction these challenges create for normal users. These secondary authentication factors-presently deployed at Google, Microsoft, and other major identity providers as part of risk-aware authentication-trigger in response to a suspicious login or account recovery attempt. Using Google as a case study, we evaluate the effectiveness of fourteen device-based, delegation-based, knowledge-based, and resource-based challenges at preventing over 350,000 real-world hijacking attempts stemming from automated bots, phishers, and targeted attackers. We show that knowledge-based challenges prevent as few as 10% of hijacking attempts rooted in phishing and 73% of automated hijacking attempts. Device-based challenges provide the best protection, blocking over 94% of hijacking attempts rooted in phishing and 100% of automated hijacking attempts. We evaluate the usability limitations of each challenge based on a sample of 1.2M legitimate users. Our results illustrate that login challenges act as an important barrier to hijacking, but that friction in the process leads to 52% of legitimate users failing to sign-in-though 97% of users eventually access their account in a short period.
Collaborative Explanation of Deep Models with Limited Interaction for Trade Secret and Privacy Preservation
Authors: Josep Domingo-Ferrer, Cristina Pérez-Solà, Alberto Blanco-Justicia
Venue: WWW
Year: 2019

An ever increasing number of decisions affecting our lives are made by algorithms. For this reason, algorithmic transparency is becoming a pressing need: automated decisions should be explainable and unbiased. A straightforward solution is to make the decision algorithms open-source, so that everyone can verify them and reproduce their outcome. However, in many situations, the source code or the training data of algorithms cannot be published for industrial or intellectual property reasons, as they are the result of long and costly experience (e.g. this is typically the case in banking or insurance). We present an approach whereby individual subjects on whom automated decisions are made can elicit in a collaborative and privacy-preserving manner a rule-based approximation of the model underlying the decision algorithm, based on limited interaction with the algorithm or even only on how they have been classified. Furthermore, being rule-based, the approximation thus obtained can be used to detect potential discrimination. We present empirical work to demonstrate the practicality of our ideas.
The FAIR TRADE Framework for Assessing Decentralised Data Solutions
Authors: John Domingue, Allan Third, Manoharan Ramachandran
Venue: WWW
Year: 2019

Decentralised data solutions bring their own sets of capabilities, requirements and issues not necessarily present in centralised solutions. In order to compare the properties of different approaches or tools for management of decentralised data, it is important to have a common evaluation framework. We present a set of dimensions relevant to data management in decentralised contexts and use them to define principles extending the FAIR framework, initially developed for open research data. By characterising a range of different data solutions or approaches by how TRusted, Autonomous, Distributed and dEcentralised, in addition to how Findable, Accessible, Interoperable and Reusable, they are, we show that our FAIR TRADE framework is useful for describing and evaluating the management of decentralised data solutions, and aim to contribute to the development of best practice in a developing field.
Characterizing and comparing Portuguese and English Wikipedia medicine-related articles
Authors: Gil Domingues, Carla Teixeira Lopes
Venue: WWW
Year: 2019

Wikipedia is the largest on-line collaborative encyclopedia, containing information from a plethora of fields, including medicine. It has been shown that Wikipedia is one of the top visited sites by readers looking for information on this topic. The large reliance on Wikipedia for this type of information drives research towards the analysis of the quality of its articles. In this work, we evaluate and compare the quality of medicine-related articles in the English and Portuguese Wikipedia. For that we use metrics such as authority, completeness, complexity, informativeness, consistency, currency and volatility, and domain-specific measurements, in order to evaluate and compare the quality of medicine related articles in the English and Portuguese Wikipedia. We were able to conclude that the English articles score better across most metrics than the Portuguese articles.
Deriving User- and Content-specific Rewards for Contextual Bandits
Authors: Paolo Dragone, Rishabh Mehrotra, Mounia Lalmas
Venue: WWW
Year: 2019

Bandit algorithms have gained increased attention in recommender systems, as they provide effective and scalable recommendations. These algorithms use reward functions, usually based on a numeric variable such as click-through rates, as the basis for optimization. On a popular music streaming service, a contextual bandit algorithm is used to decide which content to recommend to users, where the reward function is a binarization of a numeric variable that defines success based on a static threshold of user streaming time: 1 if the user streamed for at least 30 seconds and 0 otherwise. We explore alternative methods to provide a more informed reward function, based on the assumptions that streaming time distribution heavily depends on the type of user and the type of content being streamed. To automatically extract user and content groups from streaming data, we employ ”co-clustering”, an unsupervised learning technique to simultaneously extract clusters of rows and columns from a co-occurrence matrix. The streaming distributions within the co-clusters are then used to define rewards specific to each co-cluster. Our proposed co-clustered based reward functions lead to improvement of over 25% in expected stream rate, compared to the standard binarized rewards.
Improving Multiclass Classification in Crowdsourcing by Using Hierarchical Schemes
Authors: Xiaoni Duan, Keishi Tajima
Venue: WWW
Year: 2019

In this paper, we propose a method of improving accuracy of multiclass classification tasks in crowdsourcing. In crowdsourcing, it is important to assign appropriate workers to appropriate tasks. In multiclass classification, different workers are good at different subcategories. In our method, we reorganize a given flat classification task into a hierarchical classification task consisting of several subtasks, and assign each worker to an appropriate subtask. In this approach, it is important to choose a good hierarchy. In our method, we first post a flat classification task with a part of data and collect statistics on each worker's ability. Based on the obtained statistics, we simulate all candidate hierarchical schemes, estimate their expected accuracy, choose the best scheme, and post it with the rest of data. In our method, it is also important to allocate workers to appropriate subtasks. We designed several greedy worker allocation algorithms. The results of our experiments show that our method improves the accuracy of multiclass classification tasks.
On-Device Algorithms for Public-Private Data with Absolute Privacy
Authors: Alessandro Epasto, Hossein Esfandiari, Vahab S. Mirrokni
Venue: WWW
Year: 2019

Motivated by the increasing need to preserve privacy in digital devices, we introduce the on-device public-private model of computation. Our motivation comes from social-network based recommender systems in which the users want to receive recommendations based on the information available on their devices, as well as the suggestions of their social contacts, without sharing such information or contacts with the central recommendation system. Our model allows us to solve many algorithmic problems while providing absolute (deterministic) guarantees of the privacy of on-device data and the user's contacts. In fact, we ensure that the private data and private contacts are never revealed to the central system. Our restrictive model of computation presents several interesting algorithmic challenges because any computation based on private information and contacts must be performed on local devices of limited capabilities. Despite these challenges, under realistic assumptions of inter-device communication, we show several efficient algorithms for fundamental data mining and machine learning problems, ranging from k-means clustering to heavy hitters. We complement this analysis with strong impossibility results for efficient private algorithms without allowing inter-device communication. In our experimental evaluation, we show that our private algorithms provide results almost as accurate as those of the non-private ones while speeding up the on-device computations by orders of magnitude.
Product-Aware Helpfulness Prediction of Online Reviews
Authors: Miao Fan, Chao Feng, Lin Guo, Mingming Sun, Ping Li
Venue: WWW
Year: 2019

Helpful reviews are essential for e-commerce and review websites, as they can help customers make quick purchase decisions and merchants to increase profits. Due to a great number of online reviews with unknown helpfulness, it recently leads to promising research on building automatic mechanisms to assess review helpfulness. The mainstream methods generally extract various linguistic and embedding features solely from the text of a review as the evidence for helpfulness prediction. We, however, consider that the helpfulness of a review should be fully aware of the metadata (such as the title, the brand, the category, and the description) of its target product, besides the textual content of the review itself. Hence, in this paper we propose an end-to-end deep neural architecture directly fed by both the metadata of a product and the raw text of its reviews to acquire product-aware review representations for helpfulness prediction. The learned representations do not require tedious labor on feature engineering and are expected to be more informative as the target-aware evidence to assess the helpfulness of online reviews. We also construct two large-scale datasets which are a portion of the real-world web data in Amazon and Yelp, respectively, to train and test our approach. Experiments are conducted on two different tasks: helpfulness identification and regression of online reviews, and results demonstrate that our approach can achieve state-of-the-art performance with substantial improvements.
Measurement and Early Detection of Third-Party Application Abuse on Twitter
Authors: Shehroze Farooqi, Zubair Shafiq
Venue: WWW
Year: 2019

Third-party applications present a convenient way for attackers to orchestrate a large number of fake and compromised accounts on popular online social networks. Despite recent high-profile reports of third-party application abuse on popular online social networks, prior work lacks automated approaches for accurate and early detection of abusive applications. In this paper, we perform a longitudinal study of abusive third-party applications on Twitter that perform a variety of malicious and spam activities in violation of Twitter's Terms of Service (ToS). Our measurements spanning over a period of 16 months demonstrate an ongoing arms race between attackers continuously registering and abusing new applications and Twitter trying to detect them. We find that hundreds of thousands of abusive applications remain undetected by Twitter for several months while posting tens of millions of tweets. We propose a machine learning approach for accurate and early detection of abusive Twitter applications by analyzing their first few tweets. The evaluation shows that our machine learning approach can accurately detect abusive application with 92.7% precision and 87.0% recall by analyzing their first seven tweets. The deployment of our machine learning approach in the wild shows that attackers continue to abuse third-party applications despite Twitter's recent countermeasures targeting third-party applications.
The World Wants Mangoes and Kangaroos: A Study of New Emoji Requests Based on Thirty Million Tweets
Authors: Yunhe Feng, Wenjun Zhou, Zheng Lu, Zhibo Wang, Qing Cao
Venue: WWW
Year: 2019

As emojis become prevalent in personal communications, people are always looking for new, interesting emojis to express emotions, show attitudes, or simply visualize texts. In this study, we collected more than thirty million tweets mentioning the word “emoji” in a one-year period to study emoji requests on Twitter. First, we filtered out bot-generated tweets and extracted emoji requests from the raw tweets using a comprehensive list of linguistic patterns. Then, we examined patterns of new emoji requests by exploring their time, locations, and context. Finally, we summarized users' advocacy behaviors and identified expressions of equity, diversity, and fairness issues due to unreleased but expected emojis, and concluded the significance of new emojis on society. To the best of our knowledge, this paper is the first to conduct a systematic, large-scale study on new emoji requests.
DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data
Authors: Jie Feng, Mingyang Zhang, Huandong Wang, Zeyu Yang, Chao Zhang, Yong Li, Depeng Jin
Venue: WWW
Year: 2019

Online services are playing critical roles in almost all aspects of users' life. Users usually have multiple online identities (IDs) in different online services. In order to fuse the separated user data in multiple services for better business intelligence, it is critical for service providers to link online IDs belonging to the same user. On the other hand, the popularity of mobile networks and GPS-equipped smart devices have provided a generic way to link IDs, i.e., utilizing the mobility traces of IDs. However, linking IDs based on their mobility traces has been a challenging problem due to the highly heterogeneous, incomplete and noisy mobility data across services. In this paper, we propose DPLink, an end-to-end deep learning based framework, to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties. DPLink is made up by a feature extractor including a location encoder and a trajectory encoder to extract representative features from trajectory and a comparator to compare and decide whether to link two trajectories as the same user. Particularly, we propose a pre-training strategy with a simple task to train the DPLink model to overcome the training difficulties introduced by the highly heterogeneous nature of different source mobility data. Besides, we introduce a multi-modal embedding network and a co-attention mechanism in DPLink to deal with the low-quality problem of mobility data. By conducting extensive experiments on two real-life ground-truth mobility datasets with eight baselines, we demonstrate that DPLink outperforms the state-of-the-art solutions by more than 15% in terms of hit-precision. Moreover, it is expandable to add external geographical context data and works stably with heterogeneous noisy mobility traces. Our code is publicly available1.
Privacy-aware Linked Widgets
Authors: Javier D. Fernández, Fajar J. Ekaputra, Peb Ruswono Aryan, Amr Azzam, Elmar Kiesling
Venue: WWW
Year: 2019

The European General Data Protection Regulation (GDPR) brings new challenges for companies, who must demonstrate that their systems and business processes comply with usage constraints specified by data subjects. However, due to the lack of standards, tools, and best practices, many organizations struggle to adapt their infrastructure and processes to ensure and demonstrate that all data processing is in compliance with users’ given consent. The SPECIAL EU H2020 project has developed vocabularies that can formally describe data subjects’ given consent as well as methods that use this description to automatically determine whether processing of the data according to a given policy is compliant with the given consent. Whereas this makes it possible to determine whether processing was compliant or not, integration of the approach into existing line of business applications and ex-ante compliance checking remains an open challenge. In this short paper, we demonstrate how the SPECIAL consent and compliance framework can be integrated into Linked Widgets, a mashup platform, in order to support privacy-aware ad-hoc integration of personal data. The resulting environment makes it possible to create data integration and processing workflows out of components that inherently respect usage policies of the data that is being processed and are able to demonstrate compliance. We provide an overview of the necessary meta data and orchestration towards a privacy-aware linked data mashup platform that automatically respects subjects’ given consents. The evaluation results show the potential of our approach for ex-ante usage policy compliance checking within the Linked Widgets Platforms and beyond.
Indoor Towers, DPIs, and More People in Parks at Night: New Trends in Mobile Phone Location Research
Authors: Leo Ferres
Venue: WWW
Year: 2019

In this keynote, I will present some results we have obtained using location data from mobile phones interacting with information (news) websites, mobile apps like Pokemon GO and Twitter, and large physical spaces like shopping malls. I will draw some conclusions and generally discuss about the properties of mobile phone data for location-based research to finally close with some remarks about privacy and data security.