OpenRaiser/Intern-Atlas huggingface dataset:
OpenCrowd: A Human-AI Collaborative Approach for Finding Social Influencers via Open-Ended Answers Aggregation
Authors: Ines Arous, Jie Yang, Mourad Khayati, Philippe Cudré-Mauroux
Venue: WWW
Year: 2020

Finding social influencers is a fundamental task in many online applications ranging from brand marketing to opinion mining. Existing methods heavily rely on the availability of expert labels, whose collection is usually a laborious process even for domain experts. Using open-ended questions, crowdsourcing provides a cost-effective way to find a large number of social influencers in a short time. Individual crowd workers, however, only possess fragmented knowledge that is often of low quality. To tackle those issues, we present OpenCrowd, a unified Bayesian framework that seamlessly incorporates machine learning and crowdsourcing for effectively finding social influencers. To infer a set of influencers, OpenCrowd bootstraps the learning process using a small number of expert labels and then jointly learns a feature-based answer quality model and the reliability of the workers. Model parameters and worker reliability are updated iteratively, allowing their learning processes to benefit from each other until an agreement on the quality of the answers is reached. We derive a principled optimization algorithm based on variational inference with efficient updating rules for learning OpenCrowd parameters. Experimental results on finding social influencers in different domains show that our approach substantially improves the state of the art by 11.5% AUC. Moreover, we empirically show that our approach is particularly useful in finding micro-influencers, who are very directly engaged with smaller audiences.
Individual vs. Group Violent Threats Classification in Online Discussions
Authors: Noman Ashraf, Rabia Mustafa, Grigori Sidorov, Alexander F. Gelbukh
Venue: WWW
Year: 2020

Violent threat is a serious crime affecting the targeted individuals or groups. It is essential for media providers to block the users that post such threats. In this paper, we focused on detection of violent threat language in YouTube comments. We categorized the threatening comments into those targeting an individual or a group. We started from an existing dataset with violent threat language identified, but without any categorization into comments targeting individuals or groups. We adopted a binary classification approach for the prediction of individual- vs. group-targeting threats. We compared two text representations: bag of words (BOW) and pre-trained word embedding such as GloVe and fastText. We used deep-learning classifiers such as 1D-CNN, LSTM, and bidirectional LSTM (BiLSTM). GloVe embedding showed the worst results, fastText performed much better, and BiLSTM on BOW with term frequency-inverse document frequency (TF-IDF) weighting scheme gave the best results, achieving 0.94% ROC-AUC and Macro-F1 score of 0.85%.
Negative Purchase Intent Identification in Twitter
Authors: Samed Atouati, Xiao Lu, Mauro Sozio
Venue: WWW
Year: 2020

Social network users often express their discontent with a product or a service from a company on social media. Such a reaction is more pronounced in the aftermath of a corporate scandal such as a corruption scandal or food poisoning in a chain restaurant. In our work, we focus on identifying negative purchase intent in a tweet, i.e. the intent of a user of not purchasing any product or consuming any service from a company. We develop a binary classifier for such a task, which consists of a generalization of logistic regression leveraging the locality of purchase intent in posts from Twitter. We conduct an extensive experimental evaluation against state-of-the-art approaches on a large collection of tweets, showing the effectiveness of our approach in terms of F1 score. We also provide some preliminary results on which kinds of corporate scandals might affect the purchase intent of customers the most.
Biases on Social Media Data: (Keynote Extended Abstract)
Authors: Ricardo Baeza-Yates
Venue: WWW
Year: 2020

Social media data is often used to pulse the opinion of online communities, either by predicting sentiment or stances (e.g., political), to mention just two typical use cases. However, those analysis assume that the data samples really represent the underlying demographics of the overall community, both, in number and characteristics, which in most cases is not true. As a result, extrapolating these results to larger populations usually do not work. This happens because social media data is inherently biased, mainly due to two facts: (1) not all people is equally active in social media platforms and most of them are really passive; and (2) there are demographic biases in gender and age, among other attributes. Hence, the questions of how representative is the data and if is possible to make it representative are of crucial importance. We also discuss related issues such as using public samples of mostly private platforms as well as typical errors in the analysis of such data.
Metapath-Guided Credit Allocation for Identifying Representative Works
Authors: Peng Bao, Jiahui Wang
Venue: WWW
Year: 2020

The ability to identify the representative works of a researcher has important implications in a wide range of areas, including hiring, funding, and promotion systems. In this paper, we propose a metapath-guided credit allocation method (MGCA) for identifying the representative works of individual researchers. MGCA utilizes metapath-guided neighbours to exploit rich semantic information in heterogeneous information network for locating a researcher’s field of expertise, and explicitly captures the importance of a paper, its relevance to other papers, and the unequally distributed contribution of each citation via a two-step credit allocation. We validate MGCA by applying it on the American Physical Society dataset in the scenario of identifying the Nobel prize winning papers of the Nobel laureates. Experiments demonstrate that the proposed method can significantly outperform the existing approaches.
Designing for Trust: A Behavioral Framework for Sharing Economy Platforms
Authors: Natã M. Barbosa, Emily Sun, Judd Antin, Paolo Parigi
Venue: WWW
Year: 2020

Trust is a fundamental prerequisite in the growth and sustainability of sharing economy platforms. Many of such platforms rely on actions that require trust to take place, such as entering a stranger’s car or sleeping at a stranger’s place. For this reason, understanding, measuring, and tracking trust can be of great benefit to such platforms, enabling them to identify trust behaviors, both online and offline, and identify groups which may benefit from trust-building interventions. In this work, we present the design and evaluation of a behavioral framework to measure a user’s propensity to trust others on Airbnb. We conducted an online experiment with 4,499 Airbnb users in the form of an investment game in order to capture users’ propensity to trust other users on Airbnb. Then, we used the experimental data to generate both explanatory and predictive models of trust propensity. Our contribution is a framework that can be used to measure trust propensity in sharing economy platforms via online and offline signals. We discuss which affordances need to be in place so that sharing economy platforms can get signals of trust, in addition to how such a framework can be used to inform design around trust in the short and long term.
iStory: Intelligent Storytelling with Social Data
Authors: Amin Beheshti, Alireza Tabebordbar, Boualem Benatallah
Venue: WWW
Year: 2020

The production of knowledge from ever increasing amount of social data is seen by many organizations as an increasingly important capability that can complement the traditional analytics sources. Examples include extracting knowledge and deriving insights from social data to improve government services, predict intelligence activities, personalize the advertisements in elections and improve national security and public health. Understanding social data can be challenging as the analysis goal can be subjective. In this context, storytelling is considered as an appropriate metaphor as it facilitates understanding and surfacing insights which is embedded within the data. In this paper, we focus on the research problem of ‘understanding the social data’ in general and more particularly the curation, summarization and presentation of large amounts of social data. The goal is to enable intelligent narrative construction based on the important features (extracted and ranked automatically) and enable storytelling at multiple levels and from different views using novel summarization techniques. We implement an interactive storytelling dashboard, namely iStory, and focus on a motivating scenario for analyzing Urban Social Issues from Twitter as it relates to the Australian Government Budget, to highlight how storytelling can significantly facilitate understanding social data.
MineThrottle: Defending against Wasm In-Browser Cryptojacking
Authors: Weikang Bian, Wei Meng, Mingxue Zhang
Venue: WWW
Year: 2020

In-browser cryptojacking is an urgent threat to web users, where an attacker abuses the users’ computing resources without obtaining their consent. In-browser mining programs are usually developed in WebAssembly (Wasm) for its great performance. Several prior works have measured cryptojacking in the wild and proposed detection methods using static features and dynamic features. However, there exists no good defense mechanism within the user’s browser to stop the malicious drive-by mining behavior. In this work, we propose MineThrottle, a browser-based defense mechanism against Wasm cryptojacking. MineThrottle instruments Wasm code on the fly to detect mining behavior using block-level program profiling. It then throttles drive-by mining behavior based on a user-configurable policy. Our evaluation of MineThrottle with the Alexa top 1M websites demonstrates that it can accurately detect and mitigate in-browser cryptojacking with both a low false positive rate and a low false negative rate.
Social Network Influence Ranking via Embedding Network Interactions for User Recommendation
Authors: Hongbo Bo, Ryan McConville, Jun Hong, Weiru Liu
Venue: WWW
Year: 2020

Within social networks user influence may be modelled based on user interactions. Further, it is typical to recommend users to others. What is the role of user influence in user recommendation? In this paper, we first propose to use a node embedding approach to integrate many types of interaction into embedded spaces where we then define a novel closeness measure to quantify the closeness of users based on interactions. We then propose a new influence ranking algorithm based on PageRank by incorporating the closeness measure into the ranking mechanism. We evaluate our algorithm, EIRank, using a dataset collected from Twitter. Our experimental results show that our algorithm measures user influence better by way of a user recommendation task, where our algorithm outperforms TwitterRank across a range of experimental network settings.
Understanding the Performance Costs and Benefits of Privacy-focused Browser Extensions
Authors: Kevin Borgolte, Nick Feamster
Venue: WWW
Year: 2020

Advertisements and behavioral tracking have become an invasive nuisance on the Internet in recent years. Indeed, privacy advocates and expert users consider the invasion significant enough to warrant the use of ad blockers and anti-tracking browser extensions. At the same time, one of the largest advertisement companies in the world, Google, is developing the most popular browser, Google Chrome. This conflict of interest, that is developing a browser (a user agent) and being financially motivated to track users’ online behavior, possibly violating their privacy expectations, while claiming to be a ”user agent,” did not remain unnoticed. As a matter of fact, Google recently sparked an outrage when proposing changes to Chrome how extensions can inspect and modify requests to ”improve extension performance and privacy,” which would render existing privacy-focused extensions inoperable. In this paper, we analyze how eight popular privacy-focused browser extensions for Google Chrome and Mozilla Firefox, the two desktop browsers with the highest market share, affect browser performance. We measure browser performance through several metrics focused on user experience, such as page-load times, number of fetched resources, as well as response sizes. To address potential regional differences in advertisements or tracking, such as influenced by the European General Data Protection Regulation (GDPR), we perform our study from two vantage points, the United States of America and Germany. Moreover, we also analyze how these extensions affect system performance, in particular CPU time, which serves as a proxy indicator for battery runtime of mobile devices. Contrary to Google’s claims that extensions which inspect and block requests negatively affect browser performance, we find that a browser with privacy-focused request-modifying extensions performs similar or better on our metrics compared to a browser without extensions. In fact, even a combination of such extensions performs no worse than a browser without any extensions. Our results highlight that privacy-focused extensions not only improve users’ privacy, but can also increase users’ browsing experience.
Mapping of Smart Cities in Order to Understand the Cities' Sustainability
Authors: Carmen Cantuarias-Villessuzanne, Romain Weigel
Venue: WWW
Year: 2020

This paper aims at building a mapping of smart cities based on the concept of core capabilities defined by Teece et al. [11]. We pursue two objectives: improving on the analysis of the practices of the smart cities, and understanding the sustainability of city strategies according to their economic, geographic and demographic characteristics.
Interpretable Complex Question Answering
Authors: Soumen Chakrabarti
Venue: WWW
Year: 2020

We will review cross-community co-evolution of question answering (QA) with the advent of large-scale knowledge graphs (KGs), continuous representations of text and graphs, and deep sequence analysis. Early QA systems were information retrieval (IR) systems enhanced to extract named entity spans from high-scoring passages. Starting with WordNet, a series of structured curations of language and world knowledge, called KGs, enabled further improvements. Corpus is unstructured and messy to exploit for QA. If a question can be answered using the KG alone, it is attractive to ‘interpret’ the free-form question into a structured query, which is then executed on the structured KG. This process is called KGQA. Answers can be high-quality and explainable if the KG has an answer, but manual curation results in low coverage. KGs were soon found useful to harness corpus information. Named entity mention spans could be tagged with fine-grained types (e.g., scientist), or even specific entities (e.g., Einstein). The QA system can learn to decompose a query into functional parts, e.g., “which scientist” and “played the violin”. With increasing success of such systems, ambition grew to address multi-hop or multi-clause queries, e.g., “the father of the director of La La Land teaches at which university?” or “who directed an award-winning movie and is the son of a Princeton University professor?” Questions limited to simple path traversals in KGs have been encoded to a vector representation, which a decoder then uses to guide the KG traversal. Recently the corpus counterpart of such strategies has also been proposed. However, for general multi-clause queries that do not necessarily translate to paths, and seek to bind multiple variables to satisfy multiple clauses, or involve logic, comparison, aggregation and other arithmetic, neural programmer-interpreter systems have seen some success. Our key focus will be on identifying situations where manual introduction of structural bias is essential for accuracy, as against cases where sufficient data can get around distant or no supervision.
Real-Time Clustering for Large Sparse Online Visitor Data
Authors: Gromit Yeuk-Yin Chan, Fan Du, Ryan A. Rossi, Anup B. Rao, Eunyee Koh, Cláudio T. Silva, Juliana Freire
Venue: WWW
Year: 2020

Online visitor behaviors are often modeled as a large sparse matrix, where rows represent visitors and columns represent behavior. To discover customer segments with different hierarchies, marketers often need to cluster the data in different splits. Such analyses require the clustering algorithm to provide real-time responses on user parameter changes, which the current techniques cannot support. In this paper, we propose a real-time clustering algorithm, sparse density peaks, for large-scale sparse data. It pre-processes the input points to compute annotations and a hierarchy for cluster assignment. While the assignment is only a single scan of the points, a naive pre-processing requires measuring all pairwise distances, which incur a quadratic computation overhead and is infeasible for any moderately sized data. Thus, we propose a new approach based on MinHash and LSH that provides fast and accurate estimations. We also describe an efficient implementation on Spark that addresses data skew and memory usage. Our experiments show that our approach (1) provides a better approximation compared to a straightforward MinHash and LSH implementation in terms of accuracy on real datasets, (2) achieves a 20 × speedup in the end-to-end clustering pipeline, and (3) can maintain computations with a small memory. Finally, we present an interface to explore customer segments from millions of online visitor records in real-time.
Deconstruct Densest Subgraphs
Authors: Lijun Chang, Miao Qiao
Venue: WWW
Year: 2020

In this paper, we aim to understand the distribution of the densest subgraphs of a given graph under the density notion of average-degree. We show that the structures, the relationships and the distributions of all the densest subgraphs of a graph G can be encoded in O(L) space in an index called the ds-Index. Here L denotes the maximum output size of a densest subgraph of G. More importantly, ds-Indexcan report all the minimal densest subgraphs of G collectively in O(L) time and can enumerate all the densest subgraphs of G with an O(L) delay. Besides, the construction of ds-Indexcosts no more than finding a single densest subgraph using the state-of-the-art approach. Our empirical study shows that for web-scale graphs with one billion edges, the ds-Indexcan be constructed in several minutes on an ordinary commercial machine.
Stupify: A Hardware Countermeasure of KRACKs in WPA2 using Physically Unclonable Functions
Authors: Urbi Chatterjee, Rajat Sadhukhan, Debdeep Mukhopadhyay, Rajat Subhra Chakraborty, Debashis Mahata, Mukesh M. Prabhu
Venue: WWW
Year: 2020

A digital communication network is typically the backbone of world wide web and web based applications. Security protocols, specifically in wireless network has undergone several rounds of modifications and upgrades in order to prevent supplicants (or clients) or authenticators (or access points) from attackers either sitting physically around the wireless coverage area or being hooked up to a wired network connected to wireless clients. The latest security protocol in the series is WPA2 (Wi-Fi Protected Access II) which has been implemented in most of the Wi-Fi stations (clients or access points) that are being used in traditional wireless networking as well as recent IoT and CPS devices. Recently a severe replay attack named Key Reinstallation AttaCK (KRACK) has shown that the handshake in WPA2 protocol suite can be compromised and enforce the stations to reuse an old set of initialization vectors (IVs). In this work, we propose to use an unconventional hardware security primitive named Physically Unclonable Functions (PUFs) to nullify the impact of KRACK attack by ensuring a mutual authentication before establishing the communication between the authenticators and the supplicants. In this demo, we show i) how the hardware intrinsic properties of a device can be leveraged to embed a PUF instance in each device, ii) a working prototype of PUF based authentication protocol using Z-Turn board integrated with dual-core ARM Cortex-A9 processor and Artix-7 FPGA, iii) how this protocol can be integrated with existing handshake protocol in WiFi network to resist against KRACK attacks.
Time Series Forecasting for Cold-Start Items by Learning from Related Items using Memory Networks
Authors: Ayush Chauhan, Archiki Prasad, Parth Gupta, Prashanth Amireddy, Shiv Kumar Saini
Venue: WWW
Year: 2020

Time series forecasting for new items is very important in a wide variety of applications. Existing solutions for time series forecasting, however, do not address this cold start problem. The underlying machine learning models in these solutions rely heavily on the availability of the past data points of the time series. Here, we propose to use a modified Dynamic Key-Value Memory Network (DKVMN) that enables knowledge sharing across items. The network is conventionally used for binary tasks in knowledge tracing. We modify it for our regression-based forecasting use-case. Specifically, we change the output layer, include feedback for error correction, add a mechanism to handle scale across items. We test our solution on the SKU level data of a large e-commerce company and compare the results to the widely used LSTM model, outperforming it by over 25% across multiple metrics.
Robust and Scalable Conversational AI
Authors: Yun-Nung Chen
Venue: WWW
Year: 2020

Even conversational systems have attracted a lot of attention recently, the current systems sometimes fail due to the errors from different components. This keynote talk presents following research directions for improving robustness and scalability of conversational systems: 1) we first focus on learning language embeddings specifically for spoken scenarios such as more noisy inputs during inference, and 2) secondly we extend the dialogue systems to access not only structured but unstructured knowledge and propose a novel learning framework for natural language understanding and generation on top of duality towards better scalability. The enhanced robustness and scalability shows the great potential of guiding future research directions.
Proactive Discovery of Fake News Domains from Real-Time Social Media Feeds
Authors: Zhouhan Chen, Juliana Freire
Venue: WWW
Year: 2020

The proliferation of web sites that disseminate fake news is a growing problem in our society. Not surprisingly, the problem of identifying whether a web page contains fake news has attracted substantial attention. However, the problem of discovering new sources of fake news has been largely unexplored. Timely discovery of such sources is critical to combat misinformation and minimize its potential harm. In this paper, we present an automatic discovery system that proactively surfaces fake news domains before they are flagged by humans. Our system operates in two-steps: first, it uses Twitter feeds to uncover user co-sharing structures to discover political websites; then it uses a topic-agnostic classifier to score and rank newly discovered domains. To demonstrate the effectiveness of our system, we conduct an experimental evaluation in which we collect tweets related to the 2020 presidential impeachment process in the United States, and show that not only our system is able to discover new sites, but that a large percentage of these sites are indeed publishing fake news. We also design an integrated user interface to support fact-checkers and leverage their knowledge. Through this interface, fact-checkers can visualize domain interaction networks, query domain fakeness score, and tag incorrectly predicted results. Our proactive discovery system will expedite fact-checking process and can be a powerful weapon in the toolbox to combat misinformation.