OpenRaiser/Intern-Atlas huggingface dataset:
Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach
Authors: Srishti Gupta, Abhinav Khattar, Arpit Gogia, Ponnurangam Kumaraguru, Tanmoy Chakraborty
Venue: WWW
Year: 2018

Cybercriminals have leveraged the popularity of a large user base available on Online Social Networks~(OSNs) to spread spam campaigns by propagating phishing URLs, attaching malicious contents, etc. However, another kind of spam attacks using phone numbers has recently become prevalent on OSNs, where spammers advertise phone numbers to attract users» attention and convince them to make a call to these phone numbers. The dynamics of phone number based spam is different from URL-based spam due to an inherent trust associated with a phone number. While previous work has proposed strategies to mitigate URL-based spam attacks, phone number based spam attacks have received less attention. In this paper, we aim to detect spammers that use phone numbers to promote campaigns on Twitter. To this end, we collected information (tweets, user meta-data, etc.) about 3,370 campaigns spread by 670,251 users. We model the Twitter dataset as a heterogeneous network by leveraging various interconnections between different types of nodes present in the dataset. In particular, we make the following contributions -- (i) We propose a simple yet effective metric, called Hierarchical Meta-Path Score (HMPS) to measure the proximity of an unknown user to the other known pool of spammers. (ii) We design a feedback-based active learning strategy and show that it significantly outperforms three state-of-the-art baselines for the task of spam detection. Our method achieves 6.9% and 67.3% higher F1-score and AUC, respectively compared to the best baseline method. (iii) To overcome the problem of less training instances for supervised learning, we show that our proposed feedback strategy achieves 25.6% and 46% higher F1-score and AUC respectively than other oversampling strategies. Finally, we perform a case study to show how our method is capable of detecting those users as spammers who have not been suspended by Twitter (and other baselines) yet.
Developers' Track Chairs' Welcome & Organization
Authors: Amy Guy, Thomas Steiner
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 Developers' Track. We had 12 submissions in total, out of which 7 were papers for the proceedings (5 long, 2 short), and 5 were free-form formats that did not go in the proceedings. A lot of great publications are accompanied by great implementations that sometimes risk going almost unnoticed in favor of the more glamorous research results they helped produce. Likewisethe Web being a moving and ever-developing targeta lot of sometimes tedious and oftentimes less obvious work happens on standardization of future Web APIs and programming languages. The Developers' Track aims to put this implementation and standardization work front and center. It highlights research submissions that describe technically challenging Web applications of all sorts. Apart from classic papers (that we do understand are a fixed requirement for some people in order be allowed to the conference), the Developers' Track was not limited to formats that can be printed, and authors were encouraged to be creative in finding the most effective way to communicate their work, and we have included dynamic or interactive contributions.
Knowledge Extraction for the Web of Things (KE4WoT): WWW 2018 Challenge Summary
Authors: Amelie Gyrard, Manas Gaur, Swati Padhee, Amit P. Sheth, Mihaela Juganaru-Mathieu
Venue: WWW
Year: 2018

The Web of Things (WoT) is an extension of the Internet of Things (IoT) to ease the access to data generated by things/devices using the benefits of Web technologies. Data is exploited by WoT applications to monitor healthcare or even control home automation devices. The purpose of the Knowledge Extraction for the Web of Things (KE4WoT) challenge is to automatically extract the relevant knowledge from already designed smart WoT applications in various applicative domains. Those applications design and release Knowledge Bases (e.g., datasets and/or models) on the web.
Cold Start Thread Recommendation as Extreme Multi-label Classification
Authors: Kishaloy Halder, Lahari Poddar, Min-Yen Kan
Venue: WWW
Year: 2018

In public online discussion forums, the large user base and frequent posts can create challenges for recommending threads to users. Importantly, traditional recommender systems, based on collaborative filtering, are not capable of handlingnever-seen-before items (threads). We can view this task as a form of Extreme Multi-label Classification (XMLC), where for a newly-posted thread, we predict the set of users (labels) who will want to respond to it. Selecting a subset of users from the set of all users in the community poses significant challenges due to scalability, and sparsity. We propose a neural network architecture to solve thisnew thread recommendation task. Our architecture uses stacked bi-directional Gated Recurrent Units (GRU) for text encoding along with cluster sensitive attention for exploiting correlations among the large label space. Experimental evaluation with four datasets from different domains show that our model outperforms both the state-of-the-art recommendation systems as well as other XMLC approaches for this task in terms of MRR, Recall, and NDCG.
International Workshop on Cybersafety Chairs' Welcome & Organization
Authors: Richard Han, Jeremy Blackburn, Homa Hosseinmardi, Qin Lv, Bert Huang, Shivakant Mishra
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 Workshop on Computational Methods in Cybersafety, Online Harassment, and Misinformation. The theme of cybersafety is an important emerging research topic on the Internet that manifests itself daily as users navigate the Web and networked applications. After two successful workshops on cybersafety, the main goal of this third edition of this workshop on cybersafety is to build and grow the cybersafety research community by bringing together the leading researchers and practitioners from academia, industry, government, and research labs working in the general area of cybersafety to discuss the unique challenges in addressing various cybersafety issues and to share experiences, solutions, tools, and techniques. The focus is on the detection, prevention, and mitigation of various cybersafety issues, as well as education and promoting safe practices. The focus of this workshop is on computational methods in cybersafety, including new algorithms, tools, data mining techniques, analysis, systems, and applications for the detection, prevention and mitigation of various cybersafety issues, as well as education and promoting safe practices. Our program features two invited keynote speakers. We will have Dr. April Edwards, Vice President for Academic Affairs and Dean of the Faculty at Elmhurst College, speak about Racial and Gender Differences in Cyberbullying Behavior. And we will have Dr. Neil Shah, Research Scientist at Snap Inc., speak about Anomaly Detection on Large Social Graphs. We will also feature four contributed presentations and publications selected from papers submitted to our workshop.
May I Have Your Attention, Please: - Building a Dystopian Attention Economy
Authors: Sven Helmer
Venue: WWW
Year: 2018

We analyze the scenario depicted in the "Black Mirror" episode "Fifteen Million Merits" from an economic point of view, focusing on treating the attention of a user or consumer as a commodity. We continue by sketching the technological requirements for building such an economic framework, looking at advertisement platforms, payment schemes, and surveillance technology. As we show, a lot of the technology already exists and we expect the gaps to be filled in the very near future. Additionally, we briefly discuss the impact on social and work environments. While we believe that a scenario as extreme as shown in the episode is unlikely, we think that certain facets of it could find their way into our society.
Information Propagation Routes between Countries in Social Media
Authors: Didier Henry, Erick Stattner, Martine Collard
Venue: WWW
Year: 2018

Today, social media are one of the fastest ways to have access to information related to several topics. Indeed, a diffused information on these supports can travel thousands of kilometres in only few seconds contrary to an article posted on a news site. Despite the fact that a large variety of studies have been conducted to understand how fast and how scale information spreads in social media, we observe that they have not yet been interested in the geographical aspect. In this paper, we perform a geographical and temporal analysis of Twitter trends spread between May and June 2017. We introduce interesting patterns which deal with the paths taken by information between countries. In addition, we observe relevant results by taking into account the topic. Finally, we conclude and give perspectives of research of this work.
Can Blockchains and Linked Data Advance Taxation
Authors: Michal R. Hoffman
Venue: WWW
Year: 2018

Permissioned distributed ledgers (permissioned blockchains) supporting smart contracts that automatically adjust accounts and coordinate records among multiple parties, present a valid platform opportunity for establishing a fully digital tax regime. We propose a permissioned blockchain-based system aimed at eliminating some of the losses that tax authorities globally are currently struggling with. These multi-billion flaws manifest themselves as the tax gap, or the inability to collect the full amount that is owed by a given entity to a particular authority. Illegitimate or inefficient tax operations could be prevented with a global suite of smart contracts deployed on top of a consortium distributed ledger with on-chain governance. We also introduce the vision for a VAT Invoice 2.0 modelled as a Linked Data document. A tax reference generated by a smart contract would allow anyone with the right permissions to immediately investigate the entire commercial chain for any taxable item on an ontology-based tax document.
Profiling Graphs: Order from Chaos
Authors: Aidan Hogan
Venue: WWW
Year: 2018

Graphs are being increasingly adopted as a flexible data model in scenarios (e.g., Google's Knowledge Graph, Facebook's Graph API, Wikidata, etc.) where multiple editors are involved in content creation, where the schema is ever changing, where data are incomplete, where the connectivity of resources plays a key rolescenarios where relational models traditionally struggle. But with this flexibility comes a conceptual cost: it can be difficult to summarise and understand, at a high level, the content that a given graph contains. Hence profiling graphs becomes of increasing importance to extract order, a posteriori, from the chaotic processes by which such graphs are generated. This talk will motivate the use of graphs as a data model, abstract recent trends in graph data management, and then turn to the issue of profiling and summarising graphs: what are the goals of such profiling, the principles by which graphs can be summarised, the main techniques by which this can/could be achieved The talk will emphasise the importance of profiling graphs while highlighting a variety of open research questions yet to be tackled.
Path Ranking with Path Difference Sets for Maintaining Knowledge Base Integrity
Authors: Po-Cheng Huang, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2018

Knowledge base completion (KBC) involves in discovering missing facts. However, knowledge changes over time. Some facts need to be removed from knowledge base (KB) to keep knowledge base integrity (KBI) while new facts are inserted or old facts are deleted. This paper proposes a path-based learning model to learn the dependency of dynamic relations automatically. In this way, we can eliminate the conflicting facts and keep KB clean. That would be a significant benefit for KBC and other tasks using KB.
A Fresh Look at Understanding News Events Evolution
Authors: Longtao Huang, Shangwen Lv, Liangjun Zang, Yipeng Su, Jizhong Han, Songlin Hu
Venue: WWW
Year: 2018

This paper proposes a novel approach to retrieve news articles related to a specific event and generate a storyline to help people understand the event evolution. First, a similarity calculation method is proposed to retrieve news articles related to the specific event, which combines textual similarity, temporal similarity and entity similarity. Then a multi-view attribute graph is constructed to represent the relationship between retrieved articles. Finally, a community detection algorithm is developed to segment and chain subevents in the graph. Experimental results on real-world datasets demonstrate that the proposed approach achieve better results than existing methods.0F0F
A Cross-Platform Consumer Behavior Analysis of Large-Scale Mobile Shopping Data
Authors: Hong Huang, Bo Zhao, Hao Zhao, Zhou Zhuang, Zhenxuan Wang, Xiaoming Yao, Xinggang Wang, Hai Jin, Xiaoming Fu
Venue: WWW
Year: 2018

The proliferation of mobile devices especially smart phones brings remarkable opportunities for both industry and academia. In particular, the massive data generated from users» usage logs provide the possibilities for stakeholders to know better about consumer behaviors with the aid of data mining. In this paper, we examine the consumer behaviors across multiple platforms based on a large-scale mobile Internet dataset from a major telecom operator, which covers 9.8 million users from two regions among which 1.4 million users have visited e-commerce platforms within one week of our study. We make several interesting observations and examine users» cultural differences from different regions. Our analysis shows among the multiple e-commerce platforms available, most mobile users are loyal to their favorable sites; people (60%) tend to make quick decisions to buy something online, which usually takes less than half an hour. Furthermore, we find that people in residential areas are much easier to perform purchases than in business districts and purchases take place during non-work time. Meanwhile, people with medium socioeconomic status like browsing and purchasing on e-commerce platforms, while people with high and low socioeconomic status are much easier to conduct purchases online. We also show the predictability of cross-platform shopping behaviors with extensive experiments on the basis of our observed data. Our discoveries could be a good guide for e-commerce future strategy making.
Bid-Limited Targeting
Authors: Patrick Hummel, Uri Nadav
Venue: WWW
Year: 2018

This paper analyzes a mechanism for selling items in auctions in which the auctioneer specifies a cap on the ratio between the maximum and minimum bids that bidders may use in the different auctions. Such a mechanism is widely used in online advertising through the caps that companies impose on the minimum and maximum bid multipliers that advertisers may use in targeting. When bidders» values are independent and identically distributed, using this mechanism results in higher revenue than allowing bidders to condition their bids on the targeting information in an arbitrary way and also almost always results in higher revenue than not allowing bidders to target. Choosing the optimal cap on the ratio between the maximum bid and the minimum bid can also be more important than introducing additional competition in the auction. However, if bidders» values are not identically distributed, pure-strategy equilibria may fail to exist.
3rd International Workshop on Linked Data and Distributed Ledgers Chairs' Welcome & Organization
Authors: Luis-Daniel Ibáñez, John Domingue, Pascal Molli
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 3rd International Workshop on Linked Data and Distributed Ledgers (LD-DL). We envision the workshop as a forum for researchers and practitioners from Distributed Ledgers and Linked Data to come together to discuss common challenges; propose solutions to shortcomings of existing architectures; and identify synergies for joint initiatives. The ultimate goal is the creation of a Web of Interoperable Ledgers. We received 6 submissions from all around the world. We evaluated them regarding relevance, quality, and novelty, selecting 3 short papers and 1 long paper (66% acceptance rate) --ScienceMiles - Digital currency for researchers--Can Blockchains and Linked Data Advance Taxation? --A distributed database with explicit semantics and chained RDF graphs--When trust saves energy: A Reference Framework for Proof of Trust (PoT) Blockchains. We hope that you will find the tutorial program interesting, providing you with a valuable opportunity to learn and share ideas with other researchers and practitioners from institutions around the world.
Disease Tracking in GCC Region Using Arabic Language Tweets
Authors: Muhammad Usman Ilyas, Jalal Suliman Alowibdi
Venue: WWW
Year: 2018

Several prior studies have demonstrated the possibility of tracking the outbreak and spread of diseases using public tweets and other social media platforms. However, almost all such prior studies were restricted to geographically filtered English language tweets only. This study is the first to attempt a similar approach for Arabic language tweets originating from the Gulf Cooperation Council (GCC) countries. We obtained a list of commonly occurring diseases in the region from the Saudi Ministry of Health. We used both the English disease names as well as their Arabic translations to filter the stream of tweets. We acquired old tweets for a period spanning 29 months. All tweets were geographically filtered for the Middle East and the list of disease names in both English and Arabic languages. We observed that only a small fraction of tweets were in English, demonstrating that prior approaches to disease tracking relying on English language features are less effective for this region. We also demonstrate how Arabic language tweets can be used rather effectively to track the spread of some infectious diseases in the region. We verified our approach by demonstrating that a high degree of correlation between the occurrence of MERS-Coronavirus cases and Arabic language tweets on the disease. We also show that infectious diseases generating fewer tweets and non-infectious diseases do not exhibit the same high correlation. We also verify the usefulness of tracking cases using Twitter mentions by comparing against a ground truth data set of MERS-CoV cases obtained from the Saudi Ministry of Health.
Blurb Mining: Discovering Interesting Excerpts from E-commerce Product Reviews
Authors: Saratchandra Indrakanti, Gyanit Singh, Justin House
Venue: WWW
Year: 2018

Product reviews on modern e-commerce websites have evolved into repositories of valuable firsthand opinions on products. Showcasing the opinions that reviewers express on a product in a succinct way can not only promote the product, but also provide an engaging experience that simplifies the shopping journey for online shoppers. In the case of traditional media such as movies and books, employingblurbs or excerpts from critic reviews for promotional purposes is an established practice among movie publicists and book editors that has proven to be an effective way of capturing attention of customers. Such excerpts can be discovered from e-commerce product reviews to highlight interesting reviewer opinions and add emotive elements to otherwise bland e-commerce product pages. While traditional movie or book blurbs are manually extracted, they must be automatically extracted from e-commerce product reviews owing to the scale of catalogues. Further, traditional blurbs are generally phrased to be very positive in tone and sometimes may take some words out of context. However, excerpts for e-commerce products must represent the true opinions of the reviewers and must capture the context in which the words were used to retain trust of users. To that end, we introduce the problem of extracting engaging excerpts from e-commerce product reviews in this paper. We present methods to automatically discover such excerpts from reviews at scale by leveraging natural language properties such as syntactic structure of sentences and sentiment, and discuss some of the underlying challenges. We further present an evaluation of the effectiveness of the proposed methods in terms of the quality of the blurbs generated and their ranking orders produced.
Modelling Formation of Online Temporal Communities
Authors: Isa Inuwa-Dutse
Venue: WWW
Year: 2018

Contemporary social media networks can be viewed as a break to the early two-step flow model in which influential individuals act as intermediaries between the media and the public for information diffusion. Today's social media platforms enable users to both generate and consume online contents. Users continuously engage and disengage in discussions with varying degrees of interaction leading to formation of distinct online communities. Such communities are often formed at high-level either based on metadata, such as hashtags on Twitter, or popular content triggered by few influential users. These online communities often do not reflect true connectivity and lack the cohesiveness of traditional communities. In this study, we investigate real-time formation of temporal communities on Twitter. We aim at defining both high and low levels connections and to reveal the magnitude of clustering cohesion on temporal basis. Inspired by a real-life event center sitting arrangement scenario, the proposed method aims to cluster users into distinct and cohesive online temporal communities. Membership to a community relies on intrinsic tweet properties to define similarity as the basis for interaction networks. The proposed method can be useful for local event monitoring and clique-based marketing among other applications.
ScienceMiles: Digital Currency for Researchers
Authors: Zeeshan Jan, Allan Third, Luis-Daniel Ibáñez, Michelle Bachler, Elena Simperl, John Domingue
Venue: WWW
Year: 2018

Peer-reviewing is a community-driven activity where volunteer researchers assess the work of other researchers. Peer-reviewing is an important and time-consuming activity that has very little recognition. This lack of incentive may lead to poor-quality reviews and frustration from researchers. In this paper, we envision ScienceMiles, a Blockchain-based platform to manage the incentivization of peer-reviewers through a crypto-currency.
Aspect-Based Financial Sentiment Analysis using Deep Learning
Authors: Hitkul Jangid, Shivangi Singhal, Rajiv Ratn Shah, Roger Zimmermann
Venue: WWW
Year: 2018

Aspect based sentiment analysis aims to detect an aspect (i.e. features) in a given text and then perform sentiment analysis of the text with respect to that aspect. This paper aims to give a solution for the FiQA 2018 challenge subtask 1. We perform aspect-based sentiment analysis on the microblogs and headlines of financial domain. We use a multi-channel convolutional neural network for sentiment analysis and a recurrent neural network with bidirectional long short-term memory units to extract aspect from a given headline or microblog. Our proposed model produces a weighted average F1 score of 0.69 for the aspect extraction task and predicts sentiment intensity scores with a mean squared error of 0.112 on 10-fold cross validation. We believe that the developed system has direct applications in the financial domain.