OpenRaiser/Intern-Atlas huggingface dataset:
Natural Language Annotations for Search Engine Optimization
Authors: Porter Jenkins, Jennifer Zhao, Heath Vinicombe, Anant Subramanian, Arun Prasad, Atillia Dobi, Eileen Li, Yunsong Guo
Venue: WWW
Year: 2020

Understanding content at scale is a difficult but important problem for many platforms. Many previous studies focus on content understanding to optimize engagement with existing users. However, little work studies how to leverage better content understanding to attract new users. In this work, we build a framework for generating natural language content annotations and show how they can be used for search engine optimization. The proposed framework relies on an XGBoost model that labels “pins” with high probability phrases, and a logistic regression layer that learns to rank aggregated annotations for groups of content. The pipeline identifies keywords that are descriptive and contextually meaningful. We perform a large-scale production experiment deployed on the Pinterest platform and show that natural language annotations cause a 1-2% increase in traffic from leading search engines. This increase is statistically significant. Finally, we explore and interpret the characteristics of our annotations framework.
Revealing Donation Dynamics in Social Live Video Streaming
Authors: Adele Lu Jia, Yuanxing Rao, Hongru Li, Ran Tian, Siqi Shen
Venue: WWW
Year: 2020

Social live video streaming has become a global economic and social phenomenon with the rise of platforms like Facebook-Live, Youtube-Live, and Twitch. The phenomenon of user donation in these communities is rapidly emerging, towards which however we have very limited understandings. In this preliminary work, we reveal the dynamics of user donations based on a publicly available (anonymized) dataset with detailed information on over 2 million users and worth in total over 200 million US dollars. Among other results, we find that (i) both the donations received and the donations made are highly skewed, (ii) user donation is strongly correlated with the atmosphere (the volume and the sentiment of real-time user chats) and in the long run, the loss of broadcasters, and (iii) donors are loyal and very generous to their favorite broadcasters while in the mean time they also support others moderately. Our findings represent a first step towards understanding user donations which will shed lights on the donor retention problem and the design of social live video streaming services.
Factoring Fact-Checks: Structured Information Extraction from Fact-Checking Articles
Authors: Shan Jiang, Simon Baumgartner, Abe Ittycheriah, Cong Yu
Venue: WWW
Year: 2020

Fact-checking, which investigates claims made in public to arrive at a verdict supported by evidence and logical reasoning, has long been a significant form of journalism to combat misinformation in the news ecosystem. Most of the fact-checks share common structured information (called factors) such as claim, claimant, and verdict. In recent years, the emergence of ClaimReview as the standard schema for annotating those factors within fact-checking articles has led to wide adoption of fact-checking features by online platforms (e.g., Google, Bing). However, annotating fact-checks is a tedious process for fact-checkers and distracts them from their core job of investigating claims. As a result, less than half of the fact-checkers worldwide have adopted ClaimReview as of mid-2019. In this paper, we propose the task of factoring fact-checks for automatically extracting structured information from fact-checking articles. Exploring a public dataset of fact-checks, we empirically show that factoring fact-checks is a challenging task, especially for fact-checkers that are under-represented in the existing dataset. We then formulate the task as a sequence tagging problem and fine-tune the pre-trained BERT models with a modification made from our observations to approach the problem. Through extensive experiments, we demonstrate the performance of our models for well-known fact-checkers and promising initial results for under-represented fact-checkers.
Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph Embedding
Authors: Zhuoren Jiang, Zheng Gao, Jinjiong Lan, Hongxia Yang, Yao Lu, Xiaozhong Liu
Venue: WWW
Year: 2020

The recent success of deep graph embedding innovates the graphical information characterization methodologies. However, in real-world applications, such a method still struggles with the challenges of heterogeneity, scalability, and multiplex. To address these challenges, in this study, we propose a novel solution, Genetic hEterogeneous gRaph eMbedding (GERM), which enables flexible and efficient task-driven vertex embedding in a complex heterogeneous graph. Unlike prior efforts for this track of studies, we employ a task-oriented genetic activation strategy to efficiently generate the “Edge Type Activated Vector” (ETAV) over the edge types in the graph. The generated ETAV can not only reduce the incompatible noise and navigate the heterogeneous graph random walk at the graph-schema level, but also activate an optimized subgraph for efficient representation learning. By revealing the correlation between the graph structure and task information, the model interpretability can be enhanced as well. Meanwhile, an activated heterogeneous skip-gram framework is proposed to encapsulate both topological and task-specific information of a given heterogeneous graph. Through extensive experiments on both scholarly and e-commerce datasets, we demonstrate the efficacy and scalability of the proposed methods via various search/recommendation tasks. GERM can significantly reduces the running time and remove expert-intervention without sacrificing the performance (or even modestly improve) by comparing with baselines.
End-to-End Deep Attentive Personalized Item Retrieval for Online Content-sharing Platforms
Authors: Jyun-Yu Jiang, Tao Wu, Georgios Roumpos, Heng-Tze Cheng, Xinyang Yi, Ed H. Chi, Harish Ganapathy, Nitin Jindal, Pei Cao, Wei Wang
Venue: WWW
Year: 2020

Modern online content-sharing platforms host billions of items like music, videos, and products uploaded by various providers for users to discover items of their interests. To satisfy the information needs, the task of effective item retrieval (or item search ranking) given user search queries has become one of the most fundamental problems to online content-sharing platforms. Moreover, the same query can represent different search intents for different users, so personalization is also essential for providing more satisfactory search results. Different from other similar research tasks, such as ad-hoc retrieval and product retrieval with copious words and reviews, items in content-sharing platforms usually lack sufficient descriptive information and related meta-data as features. In this paper, we propose the end-to-end deep attentive model (EDAM) to deal with personalized item retrieval for online content-sharing platforms using only discrete personal item history and queries. Each discrete item in the personal item history of a user and its content provider are first mapped to embedding vectors as continuous representations. A query-aware attention mechanism is then applied to identify the relevant contexts in the user history and construct the overall personal representation for a given query. Finally, an extreme multi-class softmax classifier aggregates the representations of both query and personal item history to provide personalized search results. We conduct extensive experiments on a large-scale real-world dataset with hundreds of million users from a large video media platform at Google. The experimental results demonstrate that our proposed approach significantly outperforms several competitive baseline methods. It is also worth mentioning that this work utilizes a massive dataset from a real-world commercial content-sharing platform for personalized item retrieval to provide more insightful analysis from the industrial aspects.
Active Domain Transfer on Network Embedding
Authors: Lichen Jin, Yizhou Zhang, Guojie Song, Yilun Jin
Venue: WWW
Year: 2020

Recent works show that end-to-end, (semi-) supervised network embedding models can generate satisfactory vectors to represent network topology, and are even applicable to unseen graphs by inductive learning. However, domain mismatch between training and testing network for inductive learning, as well as lack of labeled data often compromises the outcome of such methods. To make matters worse, while transfer learning and active learning techniques, being able to solve such problems correspondingly, have been well studied on regular i.i.d data, relatively few attention has been paid on networks. Consequently, we propose in this paper a method for active transfer learning on networks named active-transfer network embedding, abbreviated ATNE. In ATNE we jointly consider the influence of each node on the network from the perspectives of transfer and active learning, and hence design novel and effective influence scores combining both aspects in the training process to facilitate node selection. We demonstrate that ATNE is efficient and decoupled from the actual model used. Further extensive experiments show that ATNE outperforms state-of-the-art active node selection methods and shows versatility in different situations.
Why do Instagram Users Tag Friends in Comments?
Authors: Jiwon Kang, Jeewoo Yoon, Jinyoung Han
Venue: WWW
Year: 2020

Tagging a friend in a comment is one of the well-known mechanisms that leads user interaction in social media. This paper investigates the current practice of user tagging in Instagram by collecting a large-scale data that includes 9 K posts and their 4 M comments shared by 3 M users. Our analysis reveals that 54.8% of the comments contain user tagging, meaning that user tagging is widely-used in Instagram. To shed light on why Instagram users tag friends in comments, we develop a learning-based model that classifies the motivation of user tagging into one of the following motivations: (i) information-oriented, (ii) relationship-oriented, and (iii) discussion-oriented. We then apply our model to the comments with user tagging in our data, and reveal that user tagging is often used for interpersonal communication with friends.
Quality-aware Dynamic Task Assignment in Human+AI Crowd
Authors: Masaki Kobayashi, Kei Wakabayashi, Atsuyuki Morishima
Venue: WWW
Year: 2020

Today, crowdsourcing the creation of AI programs is a common practice. However, combining the created programs with other programs and human computations to obtain efficient human-in-the-loop solutions is not trivial. Our paper proposes a framework to address the problem of dynamically assigning tasks to a crowd of AI and human workers, and presents the results of a preliminary experiment.
Finding a Choice in a Haystack: Automatic Extraction of Opt-Out Statements from Privacy Policy Text
Authors: Vinayshekhar Bannihatti Kumar, Roger Iyengar, Namita Nisal, Yuanyuan Feng, Hana Habib, Peter Story, Sushain Cherivirala, Margaret Hagan, Lorrie Faith Cranor, Shomir Wilson, Florian Schaub, Norman M. Sadeh
Venue: WWW
Year: 2020

Website privacy policies sometimes provide users the option to opt-out of certain collections and uses of their personal data. Unfortunately, many privacy policies bury these instructions deep in their text, and few web users have the time or skill necessary to discover them. We describe a method for the automated detection of opt-out choices in privacy policy text and their presentation to users through a web browser extension. We describe the creation of two corpora of opt-out choices, which enable the training of classifiers to identify opt-outs in privacy policies. Our overall approach for extracting and classifying opt-out choices combines heuristics to identify commonly found opt-out hyperlinks with supervised machine learning to automatically identify less conspicuous instances. Our approach achieves a precision of 0.93 and a recall of 0.9. We introduce Opt-Out Easy, a web browser extension designed to present available opt-out choices to users as they browse the web. We evaluate the usability of our browser extension with a user study. We also present results of a large-scale analysis of opt-outs found in the text of thousands of the most popular websites.
eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection
Authors: Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois R. Lamy, Krishnaprasad Thirunarayan, Usha Lokala, Amit P. Sheth
Venue: WWW
Year: 2020

Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.
Incentivizing Contribution in DecentralizedCommunity Information Systems
Authors: Peter de Lange, Michal Slupczynski, Ralf Klamma
Venue: WWW
Year: 2020

Decentralized peer-to-peer architectures provide significant advantages in comparison to centralized solutions, especially for smaller Communities of Practice. They take into account the constant change of these communities and mitigate the need for central coordination or facilitation. Using blockchain technology, decentralized architectures can provide verifiable and secure collaboration, just like centralized solutions do, but without shifting the control away from the community to a single entity. However, blockchain transactions are not for free. This leads to the problem that in addition to contributing to the community, users have to pay the blockchain transaction fees. In this contribution, we try to solve this double-spending issue by introducing a reputation-based reward system for decentralized infrastructures. This system takes into account several aspects of a user’s contribution and her reputation, and on this basis calculates the reimbursement for her efforts. Our first evaluation shows promising results and this contribution provides a first step towards developing incentivizing mechanisms for decentralized community information systems.
Clustering with a faulty oracle
Authors: Kasper Green Larsen, Michael Mitzenmacher, Charalampos E. Tsourakakis
Venue: WWW
Year: 2020

Clustering, i.e., finding groups in the data, is a problem that permeates multiple fields of science and engineering. Recently, the problem of clustering with a noisy oracle has drawn attention due to various applications including crowdsourced entity resolution [33], and predicting signs of interactions in large-scale online social networks [20, 21]. Here, we consider the following fundamental model for two clusters as proposed by Mitzenmacher and Tsourakakis [28], and Mazumdar and Saha [25]; there exist n items, belonging to two unknown groups. We are allowed to query any pair of nodes whether they belong to the same cluster or not, but the answer to the query is corrupted with some probability . Let 1 > δ = 1 − 2q > 0 be the bias. In this work, we provide a polynomial time algorithm that recovers all signs correctly with high probability in the presence of noise with queries. This is the best known result for this problem for all but tiny δ, improving on the current state-of-the-art due to Mazumdar and Saha [25].
RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System
Authors: Helena H. Lee, Shu Ke, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, Lav R. Varshney
Venue: WWW
Year: 2020

Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes. We present RecipeGPT, a novel online recipe generation and evaluation system. The system provides two modes of text generations: (1) instruction generation from given recipe title and ingredients; and (2) ingredient generation from recipe title and cooking instructions. Its back-end text generation module comprises a generative pre-trained language model GPT-2 fine-tuned on a large cooking recipe dataset. Moreover, the recipe evaluation module allows the users to conveniently inspect the quality of the generated recipe contents and store the results for future reference. RecipeGPT can be accessed online at https://recipegpt.org/
Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network
Authors: Xi Tong Lee, Arijit Khan, Sourav Sen Gupta, Yu Hann Ong, Xuan Liu
Venue: WWW
Year: 2020

Blockchains are increasingly becoming popular due to the prevalence of cryptocurrencies and decentralized applications. Ethereum is a distributed public blockchain network that focuses on running code (smart contracts) for decentralized applications. More simply, it is a platform for sharing information in a global state that cannot be manipulated or changed. Ethereum blockchain introduces a novel ecosystem of human users and autonomous agents (smart contracts). In this network, we are interested in all possible interactions: user-to-user, user-to-contract, contract-to-user, and contract-to-contract. This requires us to construct interaction networks from the entire Ethereum blockchain data, where vertices are accounts (users, contracts) and arcs denote interactions. Our analyses on the networks reveal new insights by combining information from the four networks. We perform an in-depth study of these networks based on several graph properties consisting of both local and global properties, discuss their similarities and differences with social networks and the Web, draw interesting conclusions, and highlight important, future research directions.
"What Apps Did You Use?": Understanding the Long-term Evolution of Mobile App Usage
Authors: Tong Li, Mingyang Zhang, Hancheng Cao, Yong Li, Sasu Tarkoma, Pan Hui
Venue: WWW
Year: 2020

The prevalence of smartphones has promoted the popularity of mobile apps in recent years. Although significant effort has been made to understand mobile app usage, existing studies are based primarily on short-term datasets with limited time span, e.g., a few months. Therefore, many basic facts about the long-term evolution of mobile app usage are unknown. In this paper, we study how mobile app usage evolves over a long-term period. We first introduce an app usage collection platform named carat, from which we have gathered app usage records of 1,465 users from 2012 to 2017. We then conduct the first study on the long-term evolution processes on a macro-level, i.e., app-category, and micro-level, i.e., individual app. We discover that, on both levels, there is a growth stage enabled by the introduction of new technologies. Then there is a plateau stage caused by high correlations between app categories and a pareto effect in individual app usage, respectively. Additionally, the evolution of individual app usage undergoes an elimination stage due to fierce intra-category competition. Nevertheless, the diverseness of app-category and individual app usage exhibit opposing trends: app-category usage assimilates while individual app usage diversifies. Our study provides useful implications for app developers, market intermediaries, and service providers.
Deep Transfer Learning for Search and Recommendation
Authors: Yanen Li, Yang Yang, Sen Zhou, Jian Qiao, Bo Long
Venue: WWW
Year: 2020

Training data sparsity is a common problem for many real-world applications in Search and Recommendation domains. Even for applications with a lot of training data, in the cold-start scenario we usually do not get enough labeled data. Transfer Learning is a promising approach for addressing this problem. In addition, features might interact with each other in a complex way that traditional approaches might not be able to represent, Deep Transfer Learning, which leverages Deep Neural Networks for Transfer Learning, might be able to catch such deep patterns hidden in complex feature interactions. Due to these reasons, recently Deep Transfer Learning research has gained a lot of attention and has been successfully applied to many real-world applications. This tutorial offers an overview of Deep Transfer Learning approaches in Search and Recommendation domains from the industry perspective. In this tutorial We first introduce the basic concepts and major categories of Deep Transfer Learning. Then we focus on recent developments of Deep Transfer Learning approaches in the Search and Recommendation domains. After that we will introduce two real-world examples of how to apply Deep Transfer Learning methods to improve Search and Recommendation performance at LinkedIn. Finally we will conclude the tutorial with discussion of future directions.
Explainable Detection of Fake News and Cyberbullying on Social Media
Authors: Cheng-Te Li
Venue: WWW
Year: 2020

While social media had ubiquitously penetrate into into people’s daily life, where allows interactions between people, user-generated text data not only enables novel applications, but also provides user digital footprints for us to analyze a variety of human behaviors. In this talk, we will share two of our recent studies on combating anti-social behaviors: detecting fake news and identifying cyberbullying behaviors on social media. We will reveal three important insights. First, it is possible to predict anti-social behaviors without social network information. Second, graph neural networks (GNN) is effective in improving the performance of such two tasks. Third, our models can provide model explainability to understand the language use of anti-social behaviors. In the end of this talk, we will point out future directions on fighting with fake news and cyberbullying in social media.
An End-to-End Tool for News Processing and Semantic Search
Authors: Quanzhi Li, Satish Avadhanam, Qiong Zhang
Venue: WWW
Year: 2020

In this demonstration, we present an intelligent system for business analysis and market research that can produce actionable competitive and strategic insight. It has two sub systems, a news content processing pipeline and a semantic search engine. The pipeline collects global business news and processes them to establish context, themes, topics, entities, sentiment and relationships. The semantic search system integrates all the information produced by the pipeline and provides users the semantic search and information exploration ability. This paper introduces all the components of this system.