OpenRaiser/Intern-Atlas huggingface dataset:
Disinformation from the Inside: Combining Machine Learning and Journalism to Investigate Sockpuppet Campaigns
Authors: Christopher Schwartz, Rebekah Overdorf
Venue: WWW
Year: 2020

This paper brings together machine learning and investigative journalism to examine sockpuppets accounts, a historical breed of fake accounts that are non-automated and human-controlled. Due to their flexible and human-centered nature, sockpuppets pose a complication for purely technological approaches to detecting and studying fake accounts. We find that as machine learning-based detection methods of bots slowly grow stronger, adversaries engaging in disinformation are turning to such sockpuppets accounts, and in particular a subset of sockpuppets that we call “infiltrators” — those that aim to integrate into a community in order spread disinformation. This represents a new stage in the evolution of the sockpuppet concept: where bots seek to simulate audiences and drown online social media platforms with a particular point of view, infiltrators seek to persuade and assimilate genuine audiences from within. In addition to these insights into infiltrator sockpuppets, combining machine learning and investigative journalism enables learning something more than detection and important patterns of activity: it can also gain a sense of the motivations and reasoning of adversaries who engage in disinformation.
The Tongue Can Paint: Understanding Popularity in Podcasting Societies
Authors: Hossein Shafiei, Ahmad Khonsari, Sina Kashipazha, Aresh Dadlani
Venue: WWW
Year: 2020

Podcast listeners have tripled over the past 5 years to over 100 million subscribers inside the US alone. Yet, no research study has focused on obtaining insights into the growth and popularity of contents among the subscribers. In this paper, we investigate the main factors that contribute to the popularity of podcasts. Our study helps content producers, service providers, and investors to better understand the rationales behind the popularity of podcasts.
NetTaxo: Automated Topic Taxonomy Construction from Text-Rich Network
Authors: Jingbo Shang, Xinyang Zhang, Liyuan Liu, Sha Li, Jiawei Han
Venue: WWW
Year: 2020

The automated construction of topic taxonomies can benefit numerous applications, including web search, recommendation, and knowledge discovery. One of the major advantages of automatic taxonomy construction is the ability to capture corpus-specific information and adapt to different scenarios. To better reflect the characteristics of a corpus, we take the meta-data of documents into consideration and view the corpus as a text-rich network. In this paper, we propose NetTaxo, a novel automatic topic taxonomy construction framework, which goes beyond the existing paradigm and allows text data to collaborate with network structure. Specifically, we learn term embeddings from both text and network as contexts. Network motifs are adopted to capture appropriate network contexts. We conduct an instance-level selection for motifs, which further refines term embedding according to the granularity and semantics of each taxonomy node. Clustering is then applied to obtain sub-topics under a taxonomy node. Extensive experiments on two real-world datasets demonstrate the superiority of our method over the state-of-the-art, and further verify the effectiveness and importance of instance-level motif selection.
Entity Resolution in Dynamic Heterogeneous Networks
Authors: Shubhranshu Shekhar, Deepak Pai, Sriram Ravindran
Venue: WWW
Year: 2020

Networks evolve continuously over time not only with the addition and deletion of links and nodes but also with changes in the importance of edges. Even though many networks contain this type of temporal weightings, vast majority of research in network representation learning and classification has focused on static snapshots of the graph, while largely ignoring the temporal dynamics. In this work, we describe two approaches for incorporating weighted temporal information into network embedding methods such as Graph Convolutional Networks (GCNs). While the first approach aggregates time-weighted edges and nodes, the second approach uses temporal random walks to find relevant convolution nodes. With experiments on public and proprietary datasets, we demonstrate the effectiveness of the proposed TimeSage for link prediction tasks. By applying these predictions, we show improvements in our task of identifying fraudulent actors on a large e-commerce website selling software as subscriptions.
Read Between the Lines: An Empirical Measurement of Sensitive Applications of Voice Personal Assistant Systems
Authors: Faysal Hossain Shezan, Hang Hu, Jiamin Wang, Gang Wang, Yuan Tian
Venue: WWW
Year: 2020

Voice Personal Assistant (VPA) systems such as Amazon Alexa and Google Home have been used by tens of millions of households. Recent work demonstrated proof-of-concept attacks against their voice interface to invoke unintended applications or operations. However, there is still a lack of empirical understanding of what type of third-party applications that VPA systems support, and what consequences these attacks may cause. In this paper, we perform an empirical analysis of the third-party applications of Amazon Alexa and Google Home to systematically assess the attack surfaces. A key methodology is to characterize a given application by classifying the sensitive voice commands it accepts. We develop a natural language processing tool that classifies a given voice command from two dimensions: (1) whether the voice command is designed to insert action or retrieve information; (2) whether the command is sensitive or nonsensitive. The tool combines a deep neural network and a keyword-based model, and uses Active Learning to reduce the manual labeling effort. The sensitivity classification is based on a user study (N=404) where we measure the perceived sensitivity of voice commands. A ground-truth evaluation shows that our tool achieves over 95% of accuracy for both types of classifications. We apply this tool to analyze 77,957 Amazon Alexa applications and 4,813 Google Home applications (198,199 voice commands from Amazon Alexa, 13,644 voice commands from Google Home) over two years (2018-2019). In total, we identify 19,263 sensitive “action injection” commands and 5,352 sensitive “information retrieval” commands. These commands are from 4,596 applications (5.55% out of all applications), most of which belong to the “smart home” category. While the percentage of sensitive applications is small, we show the percentage is increasing over time from 2018 to 2019.
Projection-based Relevance Model for Table Retrieval
Authors: Roee Shraga, Haggai Roitman, Guy Feigenblat, Bar Weiner
Venue: WWW
Year: 2020

We propose a novel relevance model for table-retrieval using table columns (“projections”) as pseudo-relevance feedback. To demonstrate the merits of our proposed approach, we re-rank tables that were initially retrieved from a Wikipedia corpus by state-of-the-art table retrieval methods. Overall, our proposed projection-based relevance model (PTRM) results in significant performance gains.
Detecting Uncertainty in Text using Multi-Channel CNN-TreeBiLSTM Network
Authors: Manjira Sinha, Tirthankar Dasgupta
Venue: WWW
Year: 2020

Uncertainty in a text refers to the propositions that exhibit fuzziness in meaning. In this paper, we present a novel Multi-Channel Tree-LSTM model that integrates a relation aware self-attention along with multiple embeddings to automatically detect uncertainty cues in texts. We have evaluated the models with data sources across multiple domains that include bio-medical texts, privacy policies, and consumer reviews. Our preliminary analysis showed that the proposed model outperforms the existing baseline systems across all the domains.
A Hierarchical Clustering Algorithm for Characterizing Social Media Users
Authors: Priyanka Sinha, Lipika Dey, Pabitra Mitra, Dilys Thomas
Venue: WWW
Year: 2020

In this paper we propose a method to characterize user behavior from their engagement with enterprise social media. Content analysis often suffers challenges due to noise. Here we study behavior using temporal activity, i.e., the number of posts per month represented as a time series. User posting volume on social media has a long tailed nature. It causes time series clustering algorithms to result in unbalanced clusters with either very few users or almost all users. Thus we propose a hierarchical time series clustering algorithm to group users according to their behavioral homogeneity and provide interpretable characterizations to the resulting clusters. Users in distinct clusters deviate significantly in their topics of interest while being homophilic (near identical or similar minded) within the cluster. Goodness of the clustering is observed over Enterprise Social Media (ESM); Stackexchange; and Linux Kernel Mailing List (LKML) datasets as opposed to existing clustering techniques.
Supervised Term Weight Training for Improving Question-Knowledge Matching in Chatbots
Authors: Shuangyong Song, Chao Wang, Haiqing Chen, Huan Chen
Venue: WWW
Year: 2020

We design several supervised methods of training term weights for improving results of question-knowledge matching task on a chatbot system-AliMe. Considering demand of high QPS (Query Per Second), we take a traditional regression model as the matching model rather than deep models.
A Model for Quality Control of Customer Service
Authors: Shuangyong Song, Chao Wang, Haiqing Chen, Huan Chen
Venue: WWW
Year: 2020

In E-commerce industry, customer service is with a very high quality, because of comprehensive and strict training of customer service staffs. With statistics on manual sampling results in abundant history logs, just about 0.2% answers of customer service staffs show attitudinal problems. Furthermore, even for those 0.2% answers, they can hardly be detected by some obvious rules. Therefore, present manual sampling method is very inefficient and time-consuming. In this paper we proposed a problem detection model for quality control of customer service. Experimental results on a dataset from a real-world industrial customer service Alime Assist show the model’s effect.
A Two-stage Conversational Query Rewriting Model with Multi-task Learning
Authors: Shuangyong Song, Chao Wang, Qianqian Xie, Xinxing Zu, Huan Chen, Haiqing Chen
Venue: WWW
Year: 2020

Conversational context understanding aims to recognize the real intention of user from the conversation history, which is critical for building the dialogue system. However, the multi-turn conversation understanding in open domain is still quite challenging, which requires the system extracting the important information and resolving the dependencies in contexts among a variety of open topics. In this paper, we propose the conversational query rewriting model, reformulating the multi-turn conversational queries into a single turn query, which conveys the true intention of users concisely and alleviates the difficulty of the multi-turn dialogue modeling. In the model, we formulate the query rewriting as a sequence generation problem and introduce word category information via the auxiliary word category label predicting task. To train our model, we construct a new Chinese query rewriting dataset and conduct experiments on it. The experimental results show that our model outperforms compared models, and prove the effectiveness of the word category information in improving the rewriting performance.
From Fugu With Love: New Capabilities for the Web
Authors: Thomas Steiner, Pete LePage, Thomas Nattestad, Rory McClelland, Alex Russell, Dominick Ng
Venue: WWW
Year: 2020

We present a greeting card web application that is useful in all modern browsers, but sees its experience progressively enhanced through new and upcoming browser capabilities such as native file system access, system clipboard access, contacts retrieval, periodic background sync, screen wake lock, web sharing features, and more.
Scaling PageRank to 100 Billion Pages
Authors: Stergios Stergiou
Venue: WWW
Year: 2020

Distributed graph processing frameworks formulate tasks as sequences of supersteps within which communication is performed asynchronously by sending messages over the graph edges. PageRank’s communication pattern is identical across all its supersteps since each vertex sends messages to all its edges. We exploit this pattern to develop a new communication paradigm that allows us to exchange messages that include only edge payloads, dramatically reducing bandwidth requirements. Experiments on a web graph of 38 billion vertices and 3.1 trillion edges yield execution times of 34.4 seconds per iteration, suggesting more than an order of magnitude improvement over the state-of-the-art.
Seeding Network Influence in Biased Networks and the Benefits of Diversity
Authors: Ana-Andreea Stoica, Jessy Xinyi Han, Augustin Chaintreau
Venue: WWW
Year: 2020

The problem of social influence maximization is widely applicable in designing viral campaigns, news dissemination, or medical aid. State-of-the-art algorithms often select “early adopters” that are most central in a network unfortunately mirroring or exacerbating historical biases and leaving under-represented communities out of the loop. Through a theoretical model of biased networks, we characterize the intricate relationship between diversity and efficiency, which sometimes may be at odds but may also reinforce each other. Most importantly, we find a mathematically proven analytical condition under which more equitable choices of early adopters lead simultaneously to fairer outcomes and larger outreach. Analysis of data on the DBLP network confirms that our condition is often met in real networks. We design and test a set of algorithms leveraging the network structure to optimize the diffusion of a message while avoiding to create disparate impact among participants based on their demographics, such as gender or race.
Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach
Authors: Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant G. Honavar
Venue: WWW
Year: 2020

Graph Neural Networks (GNN) offer the powerful approach to node classification in complex networks across many domains including social media, E-commerce, and FinTech. However, recent studies show that GNNs are vulnerable to attacks aimed at adversely impacting their node classification performance. Existing studies of adversarial attacks on GNN focus primarily on manipulating the connectivity between existing nodes, a task that requires greater effort on the part of the attacker in real-world applications. In contrast, it is much more expedient on the part of the attacker to inject adversarial nodes, e.g., fake profiles with forged links, into existing graphs so as to reduce the performance of the GNN in classifying existing nodes. Hence, we consider a novel form of node injection poisoning attacks on graph data. We model the key steps of a node injection attack, e.g., establishing links between the injected adversarial nodes and other nodes, choosing the label of an injected node, etc. by a Markov Decision Process. We propose a novel reinforcement learning method for Node Injection Poisoning Attacks (NIPA), to sequentially modify the labels and links of the injected nodes, without changing the connectivity between existing nodes. Specifically, we introduce a hierarchical Q-learning network to manipulate the labels of the adversarial nodes and their links with other nodes in the graph, and design an appropriate reward function to guide the reinforcement learning agent to reduce the node classification performance of GNN. The results of the experiments show that NIPA is consistently more effective than the baseline node injection attack methods for poisoning graph data on three benchmark datasets.
Improving Learning Outcomes with Gaze Tracking and Automatic Question Generation
Authors: Rohail Syed, Kevyn Collins-Thompson, Paul N. Bennett, Mengqiu Teng, Shane Williams, Wendy W. Tay, Shamsi T. Iqbal
Venue: WWW
Year: 2020

As AI technology advances, it offers promising opportunities to improve educational outcomes when integrated with an overall learning experience. We investigate forward-looking interactive reading experiences that leverage both automatic question generation and analysis of attention signals, such as gaze tracking, to improve short- and long-term learning outcomes. We aim to expand the known pedagogical benefits of adjunct questions to more general reading scenarios, by investigating the benefits of adjunct questions generated after participants attend to passages in an article, based on their gaze behavior. We also compare the effectiveness of manually-written questions with those produced by Automatic Question Generation (AQG). We further investigate gaze and reading patterns indicative of low vs. high learning in both short- and long-term scenarios (one-week followup). We show AQG-generated adjunct questions have promise as a way to scale to a wide variety of reading material where the cost of manually curating questions may be prohibitive.
Deconstructing Google's Web Light Service
Authors: Ammar Tahir, Muhammad Tahir Munir, Shaiq Munir Malik, Zafar Ayyub Qazi, Ihsan Ayyub Qazi
Venue: WWW
Year: 2020

Web Light is a transcoding service introduced by Google to show lighter and faster webpages to users searching on slow mobile clients. The service detects slow clients (e.g., users on 2G) and tries to convert webpages on the fly into a version optimized for these clients. Web Light claims to significantly reduce page load times, save user data, and substantially increase traffic to such webpages. However, there are several concerns around this service, including, its effectiveness in, preserving relevant content on a page, showing third-party advertisements, improving user performance as well as privacy concerns for users and publishers. In this paper, we perform the first independent, empirical analysis of Google’s Web Light service to shed light on these concerns. Through a combination of experiments with thousands of real Web Light pages as well as controlled experiments with synthetic Web Light pages, we (i) deconstruct how Web Light modifies webpages, (ii) investigate how ads are shown on Web Light and which ad networks are supported, (iii) measure and compare Web Light’s page load performance, (iv) discuss privacy concerns for users and publishers and (v) investigate the potential use of Web Light as a censorship circumvention tool.
FakeFinder: Twitter Fake News Detection on Mobile
Authors: Lin Tian, Xiuzhen Zhang, Min Peng
Venue: WWW
Year: 2020

Misinformation, or fake news, spreads quickly on the social media platform Twitter. Mobile devices are widely used to read Twitter posts. A mobile app that can detect fake news from the live Twitter stream and alert users in real time is an effective way to contain the spread of misinformation on Twitter. Towards this objective, the prediction model needs to be small to achieve fast prediction. In this paper, we design and develop a fake news detection mobile app with a device-based prediction model based on the small language model ALBERT. Experiments show that it can achieve real-time, accurate detection of fake news.
Data Aware Web-Assembly Function Placement
Authors: Mayank Tiwary, Pritish Mishra, Shashank Jain, Deepak Puthal
Venue: WWW
Year: 2020

Existing container based serverless computing systems are limited by cold-start problems and complex architecture for stateful services, multi-tenancy, etc. This paper presents serverless functions to be placed as per data locality and executed as a web-assembly sandbox, which results better execution latency and reduced network usage as compared to the existing architectures. The designed serverless runtime features resource isolation in terms of CPU, Memory, and file-system isolation and falicitates multi-tenancy executions. The proposed architecture is evaluated using IoT workloads with different performance metrics.
Large-Scale Network-Traffic-Identification Method with Domain Adaptation
Authors: Shun Tobiyama, Bo Hu, Kazunori Kamiya, Kenji Takahashi
Venue: WWW
Year: 2020

With the continuous evolution of the Internet, a variety of web applications, such as video streaming and social network services, are widely used, significantly increasing the amount of traffic. Classifying the types of network traffic in detail is becoming more important from the perspectives of resource management and security analysis. In an Internet service provider (ISP) level large-scale network, details of packet payload cannot be collected due to the huge amount of traffic. Alternatively, network flow data representing the statistics of a sequence of packets are collected from devices. However, due to the lack of packet details, the granularity of classification is limited to the protocol level. For better understanding of the types of traffic in detail, we propose an application-level traffic identification method combining both packet data of small-scale networks and flow data of large-scale networks. With our proposed method, we adapt an identification model trained from small-scale network data to large-scale network data with domain adaptation.
Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs
Authors: Trung-Kien Tran, Mohamed H. Gad-Elrab, Daria Stepanova, Evgeny Kharlamov, Jannik Strötgen
Venue: WWW
Year: 2020

Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering. As KGs are often automatically constructed, they may contain incorrect facts. Detecting them is a crucial, yet extremely expensive task. Prominent solutions detect and explain inconsistency in KGs with respect to accompanying ontologies that describe the KG domain of interest. Compared to machine learning methods they are more reliable and human-interpretable but scale poorly on large KGs. In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns. Though much smaller, KG abstractions preserve inconsistency and their explanations. Our experiments with large KGs (e.g., DBpedia and Yago) demonstrate the feasibility of our approach and show that it significantly outperforms the popular baseline.
PKE: A Model for Recommender Systems in Online Service Platform
Authors: Yun-Chien Tseng
Venue: WWW
Year: 2020

Graph embedding is a technique that has grown attention in recent years. Apart from mining implicit information in a graph representation data, graph embedding can be used in recommender system. Recommender system is a tool that can help e-sellers collect users’ information more easily. This is beneficial for platform providers to mine users’ interests with more data. However, it is easy to be distracted by other unrelated information when too many data are collected. In this paper, we proposed an idea called information matrix to combine information from different data. This idea considers data as graphics; hence, connects data with common nodes and extends to vectors through keyword embedding. In addition, we constructed a model that illustrates the efficiency and ability of the information matrix. This model was tested on data provided by e-sellers. Our aim was to combine browsing data and order data, and transfer these data into information matrix with high accuracy. Although the accuracy rate drops after transferring information into embedded type, it provides an idea for potential solution of cold start, a common problem in recommender systems.
Recommendations under Multi-Product Purchase Behavior
Authors: Theja Tulabandhula, Deeksha Sinha, Prasoon Patidar
Venue: WWW
Year: 2020

We model consumer behavior wherein users select multiple items from the offered recommendations. We then formulate and solve an optimization problem that computes the best recommendation sets that the e-commerce platform should display to maximize its expected revenues. Numerical simulations show the scalability of our approach in real-world settings.