OpenRaiser/Intern-Atlas huggingface dataset:
Learning Travel Time Distributions with Deep Generative Model
Authors: Xiucheng Li, Gao Cong, Aixin Sun, Yun Cheng
Venue: WWW
Year: 2019

Travel time estimation of a given route with respect to real-time traffic condition is extremely useful for many applications like route planning. We argue that it is even more useful to estimate the travel time distribution, from which we can derive the expected travel time as well as the uncertainty. In this paper, we develop a deep generative model - DeepGTT - to learn the travel time distribution for any route by conditioning on the real-time traffic. DeepGTT interprets the generation of travel time using a three-layer hierarchical probabilistic model. In the first layer, we present two techniques, amortization and spatial smoothness embeddings, to share statistical strength among different road segments; a convolutional neural net based representation learning component is also proposed to capture the dynamically changing real-time traffic condition. In the middle layer, a nonlinear factorization model is developed to generate auxiliary random variable i.e., speed. The introduction of this middle layer separates the statical spatial features from the dynamically changing real-time traffic conditions, allowing us to incorporate the heterogeneous influencing factors into a single model. In the last layer, an attention mechanism based function is proposed to collectively generate the observed travel time. DeepGTT describes the generation process in a reasonable manner, and thus it not only produces more accurate results but also is more efficient. On a real-world large-scale data set, we show that DeepGTT produces substantially better results than state-of-the-art alternatives in two tasks: travel time estimation and route recovery from sparse trajectory data.
CrowdGuard: Characterization and Early Detection of Collective Content Polluters in Online Social Networks
Authors: Ke Li, Bin Guo, Qiuyun Zhang, Jianping Yuan, Zhiwen Yu
Venue: WWW
Year: 2019

Recently, content polluters post malicious information in Online Social Networks (OSNs), which is a more and more serious problem that poses a serious threat to the privacy information, account security, user experience, etc. They continuously simulate the behaviors of legitimate accounts in various ways, and evade detection systems against them. In this paper, we focus on one kind of content polluter, namely collective content polluter (hereinafter referred to as CCP). Existing works either focus on individual polluters or require long periods of data records for detection, making their detection methods less robust and lagging behind. It is thus necessary to analyze the characteristics of collective content polluters and study the methods for early detection. This paper proposes a CCP early detection method called CrowdGuard. It analyzes the crowd behaviors of collective content polluters and legitimate accounts, extracts distinctive features, and leverages the Gaussian Mixture Model (GMM) method to cluster the two groups of accounts (legitimate users and polluters) to achieve early detection. Using the public dataset including thousands of collective content polluters on Twitter about a political election, we design an experimental scenario simulating early detection and evaluate the performance of CrowdGuard. The results show that CrowdGuard outperforms existing methods and is adequate for early detection.
Domain Generation Algorithms detection through deep neural network and ensemble
Authors: Shuaiji Li, Tao Huang, Zhiwei Qin, Fanfang Zhang, Yinhong Chang
Venue: WWW
Year: 2019

Digital threats such as backdoors, trojans, info-stealers and bots can be especially damaging nowadays as they actively steal information or allow remote control for nefarious purposes. A common attribute amongst such malware is the need for network communication and many of them use domain generation algorithms (DGAs) to pseudo-randomly generate numerous domains to communicate with each other to avoid being take-down by blacklisting method. DGAs are constantly evolving and these generated domains are mixed with benign queries in network communication traffic each day, which raises a high demand for an efficient real-time DGA classifier on domains in DNS log. Previous works either rely on group contextual/statistical features or extra host-based information and thus need long time window, or depend on lexical features extracted from domain strings to build real-time classifiers, or directly build an end-to-end deep neural network to make prediction from domain strings. Pros and cons exist for either way in experiments. In this paper, we propose several new real-time detection models and frameworks which utilize meta-data generated from domains and combine the advantages of a deep neural network model and a lexical features based model using the ensemble technique. Our proposed model obtains performance higher than all state-of-art methods so far to the best knowledge of the authors, with both precision and recall at 99.8% on a widely used public dataset.
Classifying Extremely Short Texts by Exploiting Semantic Centroids in Word Mover's Distance Space
Authors: Changchun Li, Jihong Ouyang, Ximing Li
Venue: WWW
Year: 2019

Automatically classifying extremely short texts, such as social media posts and web page titles, plays an important role in a wide range of content analysis applications. However, traditional classifiers based on bag-of-words (BoW) representations often fail in this task. The underlying reason is that the document similarity can not be accurately measured under BoW representations due to the extreme sparseness of short texts. This results in significant difficulty to capture the generality of short texts. To address this problem, we use a better regularized word mover's distance (RWMD), which can measure distances among short texts at the semantic level. We then propose a RWMD-based centroid classifier for short texts, named RWMD-CC. Basically, RWMD-CC computes a representative semantic centroid for each category under the RWMD measure, and predicts test documents by finding the closest semantic centroid. The testing is much more efficient than the prior art of K nearest neighbor classifier based on WMD. Experimental results indicate that our RWMD-CC can achieve very competitive classification performance on extremely short texts.
TweetSenti: Target-dependent Tweet Sentiment Analysis
Authors: Quanzhi Li, Qiong Zhang, Luo Si
Venue: WWW
Year: 2019

TweetSenti is a system for analyzing the sentiment of an entity in tweets. A sentence or tweet may contain multiple entities, and they do not always have the same sentiment polarity. Therefore, it is necessary to detect the sentiment for a specific target entity. This type of target-dependent (entity level) sentiment analysis has become attractive and has been used in many applications, but it is still a challenging task. TweetSenti employs a new approach for detecting the entity level sentiment. Our model splits a sentence into a left context and a right context according to the target entity, and it also exploits two different types of word embeddings to represent a word, the general word embedding and the sentiment specific word embedding. A hybrid neural network is used to capture both the sequence and structure information of the two sides of the target entity. The sequence information is learned by attention-based bi-directional LSTM models. The structure information is captured by multi-context CNN models. Based on this algorithm, we built a web-based application that users can interact with and analyze an entity's sentiment in Twitter at real-time.
Empirical Analysis of Bias in Voice-based Personal Assistants
Authors: Lanna Lima, Vasco Furtado, Elizabeth Furtado, Virgilio de Almeida
Venue: WWW
Year: 2019

Voice-based assistants are becoming increasingly widespread all over the world. However, the performance of these assistants in the interaction with users of languages and accents of developing countries is not clear yet. Eventual bias against specific language or accent of different groups of people in developing countries is maybe a factor to increase the digital gap in these countries. Our research aims at analysing the presence of bias in the interaction via audio. We carried out experiments to verify the quality of the recognition of phrases spoken by different groups of people. We evaluated the behaviour of Google Assistant and Siri for groups of people formed according to gender and regions that have different accents. Preliminary results indicate that accent and mispronunciation due to regional differences are not being properly considered by the assistants we have analyzed.
Forecasting U.S. Domestic Migration Using Internet Search Queries
Authors: Allen Yilun Lin, Justin Cranshaw, Scott Counts
Venue: WWW
Year: 2019

Roughly one in ten Americans move every year, bringing significant social and economic impact to both the places they move from and places they move to. We show that migration intent mined from internet search queries can forecast domestic migration and provide new insights beyond government data. We extract from a major search engine (Bing.com) 120 million raw queries with migration intent from 2014 to 2016, including origin and destination geographies, and the specific intent for migration such as whether the potential migration is housing or employment related. Using these queries, we map U.S. state level migration flows, validate them against government data, and demonstrate that adding search query-based metrics explains variance in migration prediction above robust baseline models. In addition, we show that the specific migration intent extracted from these queries unpack the differential demands of migrants with different demographic backgrounds and geographic interests. Examples include interactions between age, education, and income, and migration attributes such as buying versus renting housing and employment in technology versus manual labor job sectors. We discuss how local government, policy makers, and computational social scientists can benefit from this information.
User-Video Co-Attention Network for Personalized Micro-video Recommendation
Authors: Shang Liu, Zhenzhong Chen, Hongyi Liu, Xinghai Hu
Venue: WWW
Year: 2019

With the increasing popularity of micro-video sharing where people shoot short-videos effortlessly and share their daily stories on social media platforms, the micro-video recommendation has attracted extensive research efforts to provide users with micro-videos that interest them. In this paper, a hypothesis we explore is that, not only do users have multi-modal interest, but micro-videos have multi-modal targeted audience segments. As a result, we propose a novel framework User-Video Co-Attention Network (UVCAN), which can learn multi-modal information from both user and microvideo side using attention mechanism. In addition, UVCAN reasons about the attention in a stacked attention network fashion for both user and micro-video. Extensive experiments on two datasets collected from Toffee present superior results of our proposed UVCAN over the state-of-the-art recommendation methods, which demonstrate the effectiveness of the proposed framework.
Neural Variational Correlated Topic Modeling
Authors: Luyang Liu, Heyan Huang, Yang Gao, Yongfeng Zhang, Xiaochi Wei
Venue: WWW
Year: 2019

With the rapid development of the Internet, millions of documents, such as news and web pages, are generated everyday. Mining the topics and knowledge on them has attracted a lot of interest on both academic and industrial areas. As one of the prevalent unsupervised data mining tools, topic models are usually explored as probabilistic generative models for large collections of texts. Traditional probabilistic topic models tend to find a closed form solution of model parameters and approach the intractable posteriors via approximation methods, which usually lead to the inaccurate inference of parameters and low efficiency when it comes to a quite large volume of data. Recently, an emerging trend of neural variational inference can overcome the above issues, which offers a scalable and powerful deep generative framework for modeling latent topics via neural networks. Interestingly, a common assumption for the most neural variational topic models is that topics are independent and irrelevant to each other. However, this assumption is unreasonable in many practical scenarios. In this paper, we propose a novel Centralized Transformation Flow to capture the correlations among topics by reshaping topic distributions. Furthermore, we present the Transformation Flow Lower Bound to improve the performance of the proposed model. Extensive experiments on two standard benchmark datasets have well-validated the effectiveness of the proposed approach.
Predicting pregnancy using large-scale datafrom a women's health tracking mobile application
Authors: Bo Liu, Shuyang Shi, Yongshang Wu, Daniel Thomas, Laura Symul, Emma Pierson, Jure Leskovec
Venue: WWW
Year: 2019

Predicting pregnancy has been a fundamental problem in women's health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of women's health tracking mobile apps offers potential for reaching a much broader population. However, the feasibility of predicting pregnancy from mobile health tracking data is unclear. Here we develop four models - a logistic regression model, and 3 LSTM models - to predict a woman's probability of becoming pregnant using data from a women's health tracking app, Clue by BioWink GmbH. Evaluating our models on a dataset of 79 million logs from 65,276 women with ground truth pregnancy test data, we show that our predicted pregnancy probabilities meaningfully stratify women: women in the top 10% of predicted probabilities have a 89% chance of becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for women in the bottom 10%. We develop a technique for extracting interpretable time trends from our deep learning models, and show these trends are consistent with previous fertility research. Our findings illustrate the potential that women's health tracking data offers for predicting pregnancy on a broader population; we conclude by discussing the steps needed to fulfill this potential.
Globally-Optimized Realtime Supply-Demand Matching in On-Demand Ridesharing
Authors: Yifang Liu, Will Skinner, Chongyuan Xiang
Venue: WWW
Year: 2019

In on-demand ridesharing, optimizing the quality of supply-demand matches and minimizing the number of unfulfilled trip requests is an important business problem. One approach is to match supply with demand based on greedy local heuristics, which is sub-optimal at the market level. Another approach is to find the globally optimal matching over the whole marketplace. However, because the computation has high latency, and usually requires aggregate supply and demand data over time, instead of only the supply and demand at the time of individual trip requests, it is unfeasible to perform the global optimization for every trip request in realtime. This paper proposes a solution that performs a global optimization offline periodically based on forecasted supply and demand data, and uses the offline results to guide realtime supply and demand matching. The solution is a novel two-stage robust optimization scheme for supply-demand matching in on-demand ridesharing. We implemented the proposed solution and evaluated it using simulated trips based on public industry data. It reduces the number of unfulfilled trips significantly compared to a state-of-the-art approach.
Efficient (a,β)-core Computation: an Index-based Approach
Authors: Boge Liu, Long Yuan, Xuemin Lin, Lu Qin, Wenjie Zhang, Jingren Zhou
Venue: WWW
Year: 2019

The problem of computing (α, β)-core in a bipartite graph for given α and β is a fundamental problem in bipartite graph analysis and can be used in many applications such as online group recommendation, fraudsters detection, etc. Existing solution to computing (α, β)-core needs to traverse the entire bipartite graph once. Considering the real bipartite graph can be very large and the requests to compute (α, β)-core can be issued frequently in real applications, the existing solution is too expensive to compute the (α, β)-core. In this paper, we present an efficient algorithm based on a novel index such that the algorithm runs in linear time regarding the result size (thus, the algorithm is optimal since it needs at least linear time to output the result). We prove that the index only requires O(m) space where m is the number of edges in the bipartite graph. Moreover, we devise an efficient algorithm with time complexity O(δ·m) for index construction where δ is bounded by √m and is much smaller than √m in practice. We also discuss efficient algorithms to maintain the index when the bipartite graph is dynamically updated and parallel implementation of the index construction algorithm. The experimental results on real and synthetic graphs (more than 1 billion edges) demonstrate that our algorithms achieve up to 5 orders of magnitude speedup for computing (α, β)-core and up to 3 orders of magnitude speedup for index construction, respectively, compared with existing techniques.
A Hybrid BitFunnel and Partitioned Elias-Fano Inverted Index
Authors: Xinyu Liu, Zhaohua Zhang, Rebecca J. Stones, Yusen Li, Gang Wang, Xiaoguang Liu
Venue: WWW
Year: 2019

Search engines encounter a time vs. space trade-off: search responsiveness (i.e., a short query response time) comes at the cost of increased index storage. We propose a hybrid method which uses both (a) the recently published mapping-matrix-style index BitFunnel (BF) for search efficiency, and (b) the state-of-the-art Partitioned Elias-Fano (PEF) inverted-index compression method. We use this proposed hybrid method to minimize time while satisfying a fixed space constraint, and to minimize space while satisfying a fixed time constraint. Each document is stored using either BF or PEF, and we use a local search strategy to find an approximately optimal BF-PEF partition. Since performing full experiments on each candidate BF-PEF partition is impractically slow, we use a regression model to predict the time and space costs resulting from candidate partitions (space accuracy 97.6%; time accuracy 95.2%). Compared with a hybrid mathematical index (Ottaviano et al., 2015), the time cost is reduced by up to 47% without significantly exceeding its size. Compared with three mathematical encoding methods, the hybrid BF-PEF index allows performing list intersection between around 16% to 76% faster (without significantly increasing the index size). Compared with BF, the index size is reduced by 45% while maintaining an intersection time comparable to that of BF.
Implementation of the Best Practices for Data on the Web in Brazil and Costa Rica
Authors: Bernadette Farias Lóscio, Caroline Burle, Newton Calegari
Venue: WWW
Year: 2019

The Best Practices described on the Data on the Web Best Practices (DWBP) document [3] encourages and enables the continued expansion of the Web as a medium for the exchange of data. In this context, this paper focus on two cases of implementing the DWBP. The first one concerns data published by The Regional Center for Studies on the Development of the Information Society (Cetic.br) of The Brazilian Network Information Center (NIC.br). The second use case shows the experience of the Judiciary Department of Costa Rica (Justicia Abierta) with applying the DWBP Recommendation to publish their data on the Web.
Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior
Authors: Luca Luceri, Ashok Deb, Adam Badawy, Emilio Ferrara
Venue: WWW
Year: 2019

Recent research brought awareness of the issue of bots on social media and the significant risks of mass manipulation of public opinion in the context of political discussion. In this work, we leverage Twitter to study the discourse during the 2018 US midterm elections and analyze social bot activity and interactions with humans. We collected 2.6 million tweets for 42 days around the election day from nearly 1 million users. We use the collected tweets to answer three research questions: (i) Do social bots lean and behave according to a political ideology? (ii) Can we observe different strategies among liberal and conservative bots? (iii) How effective are bot strategies in engaging humans? We show that social bots can be accurately classified according to their political leaning and behave accordingly. Conservative bots share most of the topics of discussion with their human counterparts, while liberal bots show less overlap and a more inflammatory attitude. We studied bot interactions with humans and observed different strategies. Finally, we measured bots embeddedness in the social network and the extent of human engagement with each group of bots. Results show that conservative bots are more deeply embedded in the social network and more effective than liberal bots at exerting influence on humans.
Behavior Matching between Different Domains based on Canonical Correlation Analysis
Authors: Yuan Lyu, Daichi Amagata, Takuya Maekawa, Takahiro Hara, Hao Niu, Kei Yonekawa, Mori Kurokawa
Venue: WWW
Year: 2019

With the recent proliferation of e-commerce services, online shopping has become more and more popular among customers. Because it is necessary to recommend proper items to customers, to improve the accuracy of recommendation, high-performance recommender systems are required. However, current recommender systems are mainly based on information of their own domain, resulting in low accurate recommendation for customers with limited purchasing histories. The accuracy may suffer due to a lack of information. In order to use information from other domains, it is necessary to associate behaviors in different domains of the behaviorally related users. This paper presents a preliminary analysis of matching behaviors of the behaviorally related users in different domains. The result shows that we got a better prediction rate than linear regression.
Exploring User Behavior in Email Re-Finding Tasks
Authors: Joel M. Mackenzie, Kshitiz Gupta, Fang Qiao, Ahmed Hassan Awadallah, Milad Shokouhi
Venue: WWW
Year: 2019

Email continues to be one of the most commonly used forms of online communication. As inboxes grow larger, users rely more heavily on email search to effectively find what they are looking for. However, previous studies on email have been exclusive to enterprises with access to large user logs, or limited to small-scale qualitative surveys and analyses on limited public datasets such as Enron1 and Avocado2. In this work, we propose a novel framework that allows for experimentation with real email data. In particular, our approach provides a realistic way of simulating email re-finding tasks in a crowdsourcing environment using the workers' personal email data. We use our approach to experiment with various ranking functions and quality degradation to measure how users behave under different conditions, and conduct analysis across various email types and attributes. Our results show that user behavior can be significantly impacted as a result of the quality of the search ranker, but only when differences in quality are very pronounced. Our analysis confirms that time-based ranking begins to fail as email age increases, suggesting that hybrid approaches may help bridge the gap between relevance-based rankers and the traditional time-based ranking approach. Finally, we also found that users typically reformulate search queries by either entirely re-writing the query, or simply appending terms to the query, which may have implications for email query suggestion facilities.