OpenRaiser/Intern-Atlas huggingface dataset:
Is your digital neighbor a reliable investment advisor?
Authors: Daisuke Kawai, Alejandro Cuevas Villalba, Bryan R. Routledge, Kyle Soska, Ariel Zetlin-Jones, Nicolas Christin
Venue: WWW
Year: 2023

The web and social media platforms have drastically changed how investors produce and consume financial advice. Historically, individual investors were often relying on newsletters and related prospectus backed by the reputation and track record of their issuers. Nowadays, financial advice is frequently offered online, by anonymous or pseudonymous parties with little at stake. As such, a natural question is to investigate whether these modern financial “influencers” operate in good faith, or whether they might be misleading their followers intentionally. To start answering this question, we obtained data from a very large cryptocurrency derivatives exchange, from which we derived individual trading positions. Some of the investors on that platform elect to link to their Twitter profiles. We were thus able to compare the positions publicly espoused on Twitter with those actually taken in the market. We discovered that 1) staunchly “bullish” investors on Twitter often took much more moderate, if not outright opposite, positions in their own trades when the market was down, 2) their followers tended to align their positions with bullish Twitter outlooks, and 3) moderate voices on Twitter (and their own followers) were on the other hand far more consistent with their actual investment strategies. In other words, while social media advice may attempt to foster a sense of camaraderie among people of like-minded beliefs, the reality is that this is merely an illusion, which may result in financial losses for people blindly following advice.
HTTP Steady Connections for Robust Web Acceleration
Authors: Sunjae Kim, Wonjun Lee
Venue: WWW
Year: 2023

HTTP’s intrinsic request-and-response traffic pattern makes most web servers often idle, leaving a potential to accelerate page loads. We present the notion of HTTP steady connections, which fully utilizes the server’s available network bandwidth during a page load using the promising HTTP/3 server push, transforming the intermittent workload of loading a page into a more steady one. To construct a proper server push policy to achieve this, we separate the structure of a page, which is a relatively static factor, from the page load environments including client and network characteristics, which are generally dynamic and unknown to servers. We formulate a deadline-based sequencing problem using a page load model with dependency graphs and design a feedback-based reprioritization mechanism within HTTP server push to reactively match client progress robustly. Experiments with a prototype and a wide range of real-world pages show that HTTP steady connections significantly improve web page loads compared with state-of-the-art accelerators, even under packet losses and without any prior knowledge of network environments.
Public Spot Instance Dataset Archive Service
Authors: Kyunghwan Kim, Subin Park, Jaeil Hwang, Hyeonyoung Lee, Seokhyeon Kang, Kyungyong Lee
Venue: WWW
Year: 2023

Spot instances offered by major cloud vendors allow users to use cloud instances cost-effectively but with the risk of sudden instance interruption. To enable efficient use of spot instances by users, cloud vendors provide various datasets that reflect the current status of spot instance services, such as savings ratio, interrupt ratio, and instant availability. However, this information is scattered, and they require distinct access mechanisms and pose query constraints. Hence, ordinary users find it difficult to use the dataset to optimize spot instance usage. To resolve this issue, we propose a multi-cloud spot instance dataset service that is publicly available. This will help cloud users and system researchers to use spot instances from multiple cloud vendors to build a cost-efficient and reliable environment expediting cloud system research.
Confident Action Decision via Hierarchical Policy Learning for Conversational Recommendation
Authors: Heeseon Kim, Hyeongjun Yang, Kyong-Ho Lee
Venue: WWW
Year: 2023

Conversational recommender systems (CRS) aim to acquire a user’s dynamic interests for a successful recommendation. By asking about his/her preferences, CRS explore current needs of a user and recommend items of interest. However, previous works may not determine a proper action in a timely manner which leads to the insufficient information gathering and the waste of conversation turns. Since they learn a single decision policy, it is difficult for them to address the general decision problems in CRS. Besides, existing methods do not distinguish whether the past behaviors inferred from the historical interactions are closely related to the user’s current preference. To address these issues, we propose a novel Hierarchical policy learning based Conversational Recommendation framework (HiCR). HiCR formulates the multi-round decision making process as a hierarchical policy learning scheme, which consists of both a high-level policy and a low-level policy. In detail, the high-level policy aims to determine what type of action to take, such as a recommendation or a query, by observing the comprehensive conversation information. According to the decided action type, the low-level policy selects a specific action, such as which attribute to ask or which item to recommend. The hierarchical conversation policy enables CRS to decide an optimal action, resulting in reducing the unnecessary consumption of conversation turns and the continuous failure of recommendations. Furthermore, in order to filter out the unnecessary historical information when enriching the current user preference, we extract and utilize the informative past behaviors that are attentive to the current needs. Empirical experiments on four real-world datasets show the superiority of our approach against the current state-of-the-art methods.
A Decentralised Persistent Identification Layer for DCAT Datasets
Authors: Fabian Kirstein, Anton Altenbernd, Sonja Schimmler, Manfred Hauswirth
Venue: WWW
Year: 2023

The Data Catalogue Vocabulary (DCAT) standard is a popular RDF vocabulary for publishing metadata about data catalogs and a valuable foundation for creating Knowledge Graphs. It has widespread application in the (Linked) Open Data and scientific communities. However, DCAT does not specify a robust mechanism to create and maintain persistent identifiers for the datasets. It relies on Internationalized Resource Identifiers (IRIs), that are not necessarily unique, resolvable and persistent. This impedes findability, citation abilities, and traceability of derived and aggregated data artifacts. As a remedy, we propose a decentralized identifier registry where persistent identifiers are managed by a set of collaborative distributed nodes. Every node gives full access to all identifiers, since an unambiguous state is shared across all nodes. This facilitates a common view on the identifiers without the need for a (virtually) centralized directory. To support this architecture, we propose a data model and network methodology based on a distributed ledger and the W3C recommendation for Decentralized Identifiers (DID). We implemented our approach as a working prototype on a five-peer test network based on Hyperledger Fabric.
How Algorithm Awareness Impacts Algospeak Use on TikTok
Authors: Daniel Klug, Ella Steen, Kathryn Yurechko
Venue: WWW
Year: 2023

Algospeak refers to social media users intentionally altering or substituting words when creating or sharing online content, for example, using ‘le$bean’ for ‘lesbian’. This study discusses the characteristics of algospeak as a computer-mediated language phenomenon on TikTok with regards to users’ algorithmic literacy and their awareness of how the platform’s algorithms work. We then present results from an interview study with TikTok creators on their motivations to utilize algospeak. Our results indicate that algospeak is used to oppose TikTok’s algorithmic moderation system in order to prevent unjust content violations and shadowbanning when posting about benign yet seemingly unwanted subjects on TikTok. In this, we find that although algospeak helps to prevent consequences, it often impedes the creation of quality content. We provide an adapted definition of algospeak and new insights into user-platform interactions in the context of algorithmic systems and algorithm awareness.
Shield: Secure Allegation Escrow System with Stronger Guarantees
Authors: Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, Bhavish Raj Gopal
Venue: WWW
Year: 2023

The rising issues of harassment, exploitation, corruption and other forms of abuse have led victims to seek comfort by acting in unison against common perpetrators. This is corroborated by the widespread #MeToo movement, which was explicitly against sexual harassment. Installation of escrow systems has allowed victims to report such incidents. The escrows are responsible for identifying the perpetrator and taking the necessary action to bring justice to all its victims. However, users hesitate to participate in these systems due to the fear of such sensitive reports being leaked to perpetrators, who may further misuse them. Thus, to increase trust in the system, cryptographic solutions are being designed to realize web-based secure allegation escrow (SAE) systems. While the work of Arun et al. (NDSS’20) presents the state-of-the-art solution, we identify attacks that can leak sensitive information and compromise victim privacy. We also report issues present in prior works that were left unidentified. Having identified the attacks and issues in all prior works, we put forth an SAE system that overcomes these while retaining all the existing salient features. The cryptographic technique of secure multiparty computation (MPC) serves as the primary underlying tool in designing our system. At the heart of our system lies a new duplicity check protocol and an improved matching protocol. We also provide essential features such as allegation modification and deletion, which were absent in the state of the art. To demonstrate feasibility, we benchmark the proposed system with state-of-the-art MPC protocols and report the cost of processing an allegation. Different settings that affect system performance are analyzed, and the reported values showcase the practicality of our solution.
Geographic Information Retrieval Using Wikipedia Articles
Authors: Amir Krause, Sara Cohen
Venue: WWW
Year: 2023

Assigning semantically relevant, real-world locations to documents opens new possibilities to perform geographic information retrieval. We propose a novel approach to automatically determine the latitude-longitude coordinates of appropriate Wikipedia articles with high accuracy, leveraging both text and metadata in the corpus. By examining articles whose base-truth coordinates are known, we show that our method attains a substantial improvement over state of the art works. We subsequently demonstrate how our approach could yield two benefits: (1) detecting significant geolocation errors in Wikipedia; and (2) proposing approximated coordinates for hundreds of thousands of articles which are not traditionally considered to be locations (such as events, ideas or people), opening new possibilities for conceptual geographic retrievals over Wikipedia.
CySoc 2023: 4th International Workshop on Cyber Social Threats
Authors: Ugur Kursuncu, Kai-Cheng Yang, Francesco Pierri, Matthew R. DeVerna, Yelena Mejova, Jeremy Blackburn
Venue: WWW
Year: 2023

INTRODUCTION The rise of online platforms as a communication tool for individuals worldwide has brought an increase in their misuse and abuse. Social platforms have been implicated in promoting and amplifying hate speech, misinformation, extremism, harassment, cyberbullying, human trafcking, drug dealing, gender-based stereotyping, and other forms of violence. These cyber social threats are inherently multifaceted, entailing signifcant implications for both individuals and communities, which make them challenging to combat while crucial to address. We are pleased to present the proceedings of the 4th International Workshop on Cyber Social Threats held on April 30, 2023, in Austin, Texas, U.S. The goal of this workshop was to bring together researchers from various disciplines to explore the complex problems that accompany digital platforms. Given the importance of accurate and reliable information in light of the COVID-19 pandemic and other global crises, the spotlight topic for this year’s workshop was "Information Integrity during Crises."
Weedle: Composable Dashboard for Data-Centric NLP in Computational Notebooks
Authors: Nahyun Kwon, Hannah Kim, Sajjadur Rahman, Dan Zhang, Estevam Hruschka
Venue: WWW
Year: 2023

Data-centric NLP is a highly iterative process requiring careful exploration of text data throughout entire model development lifecycle. Unfortunately, existing data exploration tools are not suitable to support data-centric NLP because of workflow discontinuity and lack of support for unstructured text. In response, we propose Weedle, a seamless and customizable exploratory text analysis system for data-centric NLP. Weedle is equipped with built-in text transformation operations and a suite of visual analysis features. With its widget, users can compose customizable dashboards interactively and programmatically in computational notebooks.
What are "personal data spaces"?
Authors: Viivi Lähteenoja
Venue: WWW
Year: 2023

While the concept of “data spaces” is no longer new, its specific application to individuals and personal data management is still undeveloped. This short paper presents a vision for “personal data spaces” in the shape of a work-in-progress description of them and some of the conceptual and implementation features envisioned. It is offered for discussion, debate, and improvement by professionals, policymakers, and researchers operating in the intersection of data spaces and personal data management.
Machine Learning for Streaming Media
Authors: Sudarshan Lamkhede, Praveen Chandar, Vladan Radosavljevic, Amit Goyal, Lan Luo
Venue: WWW
Year: 2023

Streaming media has become a popular medium for consumers of all ages, with people spending several hours a day streaming videos, games, music, or podcasts across devices. Most global streaming services have introduced Machine Learning (ML) into their operations to personalize consumer experience, improve content, and further enhance the value proposition of streaming services. Despite the rapid growth, there is a need to bridge the gap between academic research and industry requirements and build connections between researchers and practitioners in the field. This workshop aims to provide a unique forum for practitioners and researchers interested in Machine Learning to get together, exchange ideas and get a pulse for the state of the art in research and burning issues in the industry.
Personalization and Recommendations in Search
Authors: Sudarshan Lamkhede, Anlei Dong, Moumita Bhattacharya, Hongning Wang
Venue: WWW
Year: 2023

The utility of a search system for its users can be further enhanced by providing personalized results and recommendations within the search context. However, the research discussions around these aspects of search remain fragmented across different conferences and workshops. Hence, this workshop aims to bring together researchers and practitioners from industry and academia to engage in the discussions of algorithmic and system challenges in search personalization and effectively recommending within search context.
Towards a Data Space for Interoperability of Analytic Provenance
Authors: Tristan Langer, André Pomp, Tobias Meisen
Venue: WWW
Year: 2023

Capturing, visualizing and analyzing provenance data to better understand and support analytic reasoning processes is a rapidly growing research field named analytic provenance. Provenance data includes the state of a visualization within a tool as well as the user’s interactions performed while interacting with the tool. Research in this field has produced in many new approaches that generate data for specific tools and use cases. However, since a variety of tools are used and analytic tasks are performed in real analysis use cases there is a problem in building an interoperable baseline data corpus for investigation of the transferability of different approaches. In this paper, we present a visionary data space architecture for integrating and processing analytic provenance data in a unified way using semantic modeling. We discuss emerging challenges and research opportunities to realize such a vision using semantic models in data spaces to enable analytic provenance data interoperability.
Efficient Sampling for Big Provenance
Authors: Sara Moshtaghi Largani, Seokki Lee
Venue: WWW
Year: 2023

Provenance has been studied extensively to explain existing and missing results for many applications while focusing on scalability and usability challenges. Recently, techniques that efficiently compute a compact representation of provenance have been introduced. In this work, we introduce a practical solution that computes a sample of provenance for existing results without computing full provenance. Our technique computes a sample of provenance based on the distribution of provenance wrt the query result that is estimated from the distribution of input data while considering the correlation among the data. The preliminary evaluation demonstrates that comparing to the naive approach our method efficiently computes a sample of (large size of) provenance with low errors.
Hybrid Query and Instance Explanations and Repairs
Authors: Seokki Lee, Boris Glavic, Adriane Chapman, Bertram Ludäscher
Venue: WWW
Year: 2023

Prior work on explaining missing (unexpected) query results identifies which parts of the query or data are responsible for the erroneous result or repairs the query or data to fix such errors. The problem of generating repairs is typically expressed as an optimization problem, i.e., a single repair is returned that is optimal wrt. to some criterion such as minimizing the repair’s side effects. However, such an optimization objective may not concretely model a user’s (often hard to formalize) notion of which repair is “correct”. In this paper, we motivate hybrid explanations and repairs, i.e., that fix both the query and the data. Instead of returning one “optimal” repair, we argue for an approach that empowers the user to explore the space of possible repairs effectively. We also present a proof-of-concept implementation and outline open research problems.
Net-track: Generic Web Tracking Detection Using Packet Metadata
Authors: Dongkeun Lee, Minwoo Joo, Wonjun Lee
Venue: WWW
Year: 2023

While third-party trackers breach users’ privacy by compiling large amounts of personal data through web tracking techniques, combating these trackers is still left at the hand of each user. Although network operators may attempt a network-wide detection of trackers through inspecting all web traffic inside the network, their methods are not only privacy-intrusive but of limited accuracy as these are susceptible to domain changes or ineffective against encrypted traffic. To this end, in this paper, we propose Net-track, a novel approach to managing a secure web environment through platform-independent, encryption-agnostic detection of trackers. Utilizing only side-channel data from network traffic that are still available when encrypted, Net-track accurately detects trackers network-wide, irrespective of user’s browsers or devices without looking into packet payloads or resources fetched from the web server. This prevents user data from leaking to tracking servers in a privacy-preserving manner. By measuring statistics from traffic traces and their similarities, we show distinctions between benign traffic and tracker traffic in their traffic patterns and build Net-track based on the features that fully capture trackers’ distinctive characteristics. Evaluation results show that Net-track is able to detect trackers with 94.02% accuracy and can even discover new trackers yet unrecognized by existing filter lists. Furthermore, Net-track shows its potential for real-time detection, maintaining its performance when using only a portion of each traffic trace.
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case
Authors: Pantelitsa Leonidou, Nicolas Kourtellis, Nikos Salamanos, Michael Sirivianos
Venue: WWW
Year: 2023

Users are exposed to a large volume of harmful content that appears daily on various social network platforms. One solution to users’ protection is developing online moderation tools using Machine Learning (ML) techniques for automatic detection or content filtering. On the other hand, the processing of user data requires compliance with privacy policies. In this paper, we propose a framework for developing content moderation tools in a privacy-preserving manner where sensitive information stays on the users’ device. For this purpose, we apply Differentially Private Federated Learning (DP–FL), where the training of ML models is performed locally on the users’ devices, and only the model updates are shared with a central entity. To demonstrate the utility of our approach, we simulate harmful text classification on Twitter data in a distributed FL fashion– but the overall concept can be generalized to other types of misbehavior, data, and platforms. We show that the performance of the proposed FL framework can be close to the centralized approach – for both the DP–FL and non–DP FL. Moreover, it has a high performance even if a small number of clients (each with a small number of tweets) are available for the FL training. When reducing the number of clients (from fifty to ten) or the tweets per client (from 1K to 100), the classifier can still achieve AUC. Furthermore, we extend the evaluation to four other Twitter datasets that capture different types of user misbehavior and still obtain a promising performance (61% – 80% AUC). Finally, we explore the overhead on the users’ devices during the FL training phase and show that the local training does not introduce excessive CPU utilization and memory consumption overhead.
Privacy-Preserving Online Content Moderation with Federated Learning
Authors: Pantelitsa Leonidou, Nicolas Kourtellis, Nikos Salamanos, Michael Sirivianos
Venue: WWW
Year: 2023

Users are exposed to a large volume of harmful content that appears daily on various social network platforms. One solution to users’ protection is developing online moderation tools using Machine Learning (ML) techniques for automatic detection or content filtering. On the other hand, the processing of user data requires compliance with privacy policies. This paper proposes a privacy–preserving Federated Learning (FL) framework for online content moderation that incorporates Central Differential Privacy (CDP). We simulate the FL training of a classifier for detecting tweets with harmful content, and we show that the performance of the FL framework can be close to the centralized approach. Moreover, it has a high performance even if a small number of clients (each with a small number of tweets) are available for the FL training. When reducing the number of clients (from fifty to ten) or the tweets per client (from 1K to 100), the classifier can still achieve AUC. Furthermore, we extend the evaluation to four other Twitter datasets that capture different types of user misbehavior and still obtain a promising performance (61% – 80% AUC).