OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Labels and Topics: Discovering Causal Relationships in Neural Topic Modeling
Authors: Yi-Kun Tang, Heyan Huang, Xuewen Shi, Xian-Ling Mao
Venue: WWW
Year: 2024

Topic models that can take advantage of labels are broadly used in identifying interpretable topics from textual data. However, existing topic models tend to merely view labels as names of topic clusters or as categories of texts, thereby neglecting the potential causal relationships between supervised information and latent topics, as well as within these elements themselves. In this paper, we focus on uncovering possible causal relationships both between and within the supervised information and latent topics to better understand the mechanisms behind the emergence of the topics and the labels. To this end, we propose Causal Relationship-Aware Neural Topic Model (CRNTM), a novel neural topic model that can automatically uncover interpretable causal relationships between and within supervised information and latent topics, while concurrently discovering high-quality topics. In CRNTM, both supervised information and latent topics are treated as nodes, with the causal relationships represented as directed edges in a Directed Acyclic Graph (DAG). A Structural Causal Model (SCM) is employed to model the DAG. Experiments are conducted on three public corpora with different types of labels. Experimental results show that the discovered causal relationships are both reliable and interpretable, and the learned topics are of high quality comparing with eight start-of-the-art topic model baselines.
Who is Creating Malware Repositories on GitHub and Why?
Authors: Nishat Ara Tania, Md Rayhanul Masud, Md Omar Faruk Rokon, Qian Zhang, Michalis Faloutsos
Venue: WWW
Year: 2024

Recent studies have found thousands of malware source code repositories on GitHub. For the first time, we propose to understand the origins and motivations behind the creation of such malware repositories. For that, we collect and profile the authors of malware repositories using a three-fold systematic approach. First, we identify 14K users in GitHub who have authored at least one malware repository. Second, we leverage a pretrained large language model (LLM) to estimate the likelihood of malicious intent of these authors. This innovative approach led us to categorize 3339 as Malicious, 3354 as Likely Malicious, and 7574 as Benign authors. Further, to validate the accuracy and reliability of our classification, we conduct a manual review of 200 randomly selected authors. Third, our analysis provides insights into the authors' profiles and motivations. We find that Malicious authors often have sparse profiles and focus on creating and spreading malware, while Benign authors typically have complete profiles with a focus on cybersecurity research and education. Likely Malicious authors show varying levels of engagement and ambiguous intentions. We see our study as a key step towards understanding the ecosystem of malware authorship on GitHub.
Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems
Authors: Anuja Tayal, Aman Tyagi
Venue: WWW
Year: 2024

When interacting with Retrieval-Augmented Generation (RAG)-based conversational agents, the users must carefully craft their queries to be understood correctly. Yet, understanding the system's capabilities can be challenging for the users, leading to ambiguous questions that necessitate further clarification. This work aims to bridge the gap by developing a suggestion question generator. To generate suggestion questions, our approach involves utilizing dynamic context, which includes both dynamic few-shot examples and dynamically retrieved contexts. Through experiments, we show that the dynamic contexts approach can generate better suggestion questions as compared to other prompting approaches.
Optimizing Network Resilience via Vertex Anchoring
Authors: Siyi Teng, Jiadong Xie, Fan Zhang, Can Lu, Juntao Fang, Kai Wang
Venue: WWW
Year: 2024

Network resilience is a critical ability of a network to maintain its functionality against disturbances. A network is resilient/robust when a large portion of the nodes are to be better engaged in the network, i.e., they are less likely to leave given the changes on the network. Existing studies validate that the engagement of a node can be well captured by its coreness on network topology. Therefore, it is promising to maximize the number of nodes with increasing coreness values. In this paper, we propose and study thefollower maximization problem: maximizing the resilience gain (the number of coreness-increased vertices) via anchoring a set of vertices within a given budget. We prove that the problem is NP-hard and W[2]-hard, and it is NP-hard to approximate within an O(n^1-ε ) factor. We first propose an advanced greedy approach, followed by a time-dependent framework designed to quickly find high-quality results. The framework is initialized by the advanced greedy algorithm and incorporates novel techniques for optimizing the search space. The effectiveness and efficiency of our solution are verified with extensive experiments on 8 real-life datasets. Our source codes are available at https://github.com/Tsyxxxka/Follower-Maximization.
RUHate-MM: Identification of Hate Speech and Targets using Multimodal Data from Russia-Ukraine Crisis
Authors: Surendrabikram Thapa, Farhan Ahmad Jafri, Kritesh Rauniyar, Mehwish Nasim, Usman Naseem
Venue: WWW
Year: 2024

During the conflict between Ukraine and Russia, hate speech targeted toward specific groups was widespread on different social media platforms. With most social platforms allowing multimodal content, the use of multimodal content to express hate speech is widespread on the Internet. Although there has been considerable research in detecting hate speech within unimodal content, the investigation into multimodal content remains insufficient. The limited availability of annotated multimodal datasets further restricts our ability to explore new methods to interpret and identify hate speech and its targets. The availability of annotated datasets for hate speech detection during political events, such as invasions, are even limited. To fill this gap, we introduce a comprehensive multimodal dataset consisting of 20,675 posts related to the Russia-Ukraine crisis, which were manually annotated as either 'Hate Speech' or 'No Hate Speech'. Additionally, we categorize the hate speech data into three targets: 'Individual', 'Organization', and 'Community'. Our benchmarked evaluations show that there is still room for improvement in accurately identifying hate speech and its targets. We hope that the availability of this dataset and the evaluations performed on it will encourage the development of new methods for identifying hate speech and its targets during political events like invasions and wars. The dataset and resources are made available at https://github.com/Farhan-jafri/Russia-Ukraine.
Foreword for the First International Workshop on Multimodal Content Analysis for Social Good (MM4SG 2024)
Authors: Surendrabikram Thapa, Usman Naseem, Roy Ka-Wei Lee, Mehwish Nasim
Venue: WWW
Year: 2024

The first international workshop on multimedia content analysis for social good (MM4SG) was held in conjunction with the Web Conference 2024. The workshop aimed to address the challenge of effectively analyzing and moderating multimodal content across digital platforms. In an era where multimodal data including memes, text-embedded images, and fabricated content swiftly capture the public's attention and influence societal discourse, the need for advanced content moderation strategies is more pressing than ever. This workshop serves as a platform for research and collaboration between experts in natural language processing, machine learning, computational social science, and ethics. In this paper, we describe the inaugural edition of the MM4SG workshop. We also include the future directions for our workshop's upcoming editions.
Fingerprinting the Shadows: Unmasking Malicious Servers with Machine Learning-Powered TLS Analysis
Authors: Andreas Theofanous, Eva Papadogiannaki, Alexander Shevtsov, Sotiris Ioannidis
Venue: WWW
Year: 2024

Over the last few years, the adoption of encryption in network traffic has been constantly increasing. The percentage of encrypted communications worldwide is estimated to exceed 90%. Although network encryption protocols mainly aim to secure and protect users' online activities and communications, they have been exploited by malicious entities that hide their presence in the network. It was estimated that in 2022, more than 85% of the malware used encrypted communication channels. In this work, we examine state-of-the-art fingerprinting techniques and extend a machine learning pipeline for effective and practical server classification. Specifically, we actively contact servers to initiate communication over the TLS protocol and through exhaustive requests, we extract communication metadata. We investigate which features favor an effective classification, following state-of-the-art approaches. Our extended pipeline can indicate whether a server is malicious or not with 91% precision and 95% recall, while it can specify the botnet family with 99% precision and 99% recall.
A Category-agnostic Graph Attention-based Approach for Determining Notability of Articles for Wikipedia
Authors: Gokul Thota, Vasudeva Varma
Venue: WWW
Year: 2024

Wikipedia is a highly essential platform because of its informative, dynamic, and easily accessible nature. To identify topics/titles warranting their own Wikipedia article, editors of Wikipedia defined "Notability" guidelines. So far notability is enforced by humans, which makes scalability an issue. There has been no significant work on Notability determination for titles with complex category dependencies. We design a mechanism to identify such titles. We construct a dataset with 9k such titles and propose a category-agnostic approach utilizing Graph neural networks, for their notability determination. Our system outperforms machine learning-based, transformer-based classifiers and entity salience methods. It provides a scalable alternative for notability detection.
Structural Podcast Content Modeling with Generalizability
Authors: Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo, Hugues Bouchard
Venue: WWW
Year: 2024

Podcast content modeling is crucial for a variety of practical web uses, such as the recommendation and classification of podcasts. However, previous studies on podcast content modeling rely on task-specific datasets to train dedicated models for each downstream application, which are labels heavily dependent and the learned representations are non-generalizable across different tasks. In addition, the rich and intricate structural information among users, podcasts, and topics are neglected. In this paper, we propose to model podcast content without labels and learn general podcast representations without prior knowledge of downstream tasks. Moreover, the learned podcast representations encode crucial structural information, complementary to the independent content information of each podcast. In particular, we first collect a new and large-scale podcast graph from Spotify. Then, we propose Podcast2Vec, a novel self-supervised podcast content modeling method to learn podcast representations. Podcast2Vec captures general transferable knowledge across different tasks and complex structures via a metapath-based neighbor sampling strategy and a multi-view relational modeling framework. Thorough experiments demonstrate the superiority of our method on four real-world podcast content modeling tasks.
ACCORD: Constraint-driven Mediation of Multi-user Conflicts in Cloud Services
Authors: Abhiroop Tippavajjula, Primal Pappachan, Anna Cinzia Squicciarini, Jose Such
Venue: WWW
Year: 2024

When multiple users adopt collaborative cloud services like Google Drive to work on a shared resource, incorrect or missing permis- sions may cause conflicting or inconsistent access or use privileges. These issues (or conflicts) compromise resources confidentiality, integrity, or availability leading to a lack of trust in cloud services. An example conflict is when a user with editor permissions changes the permissions on a shared resource without consent from the orig- inal resource owner. In this demonstration, we introduce ACCORD, a web application built on top of Google Drive able to detect and resolve multi-user conflicts. ACCORD employs a simulator to help users preemptively identify potential conflicts and assists them in defining action constraints. Using these constraints, ACCORD can automatically detect and resolve any future conflicts.
Beyond Facts: 4th International Workshop on Computational Methods for Online Discourse Analysis
Authors: Konstantin Todorov, Pavlos Fafalios, Stefan Dietze, Dimitar Dimitrov
Venue: WWW
Year: 2024

Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (events, entities). This data constitutes a valuable source of insights for studies into misinformation spread, bias reinforcement, echo chambers or political agenda setting. Computational methods, mostly from the field of NLP, have emerged that tackle a wide range of tasks in this context, including argument and opinion mining, claim detection, checkworthiness detection, stance detection or fact verification. However, computational models require robust definitions of classes and concepts under investigation. Thus, these computational tasks require a strong interdisciplinary and epistemological foundation, specifically with respect to the underlying definitions of key concepts such as claims, arguments, stances, check-worthiness or veracity. This requires a highly interdisciplinary approach combining expertise from fields such as communication studies, computational linguistics and computer science. As opposed to facts, claims are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understandings strongly diverge across communities. From a computational perspective, in order to address this complexity, the synergy of multiple approaches, coming both from symbolic (knowledge representation) and statistical AI seem to be promising to tackle such challenges. This workshop aims at strengthening the relations between these communities, providing a forum for shared works on the modeling, extraction and analysis of discourse on the Web. It will address the need for a shared understanding and structured knowledge about discourse data in order to enable machine-interpretation, discoverability and reuse, in support of scientific or journalistic studies into the analysis of societal debates on the Web. Beyond research into information and knowledge extraction, data consolidation and modeling for knowledge graphs building, the workshop targets communities focusing on the analysis of online discourse, relying on methods from machine learning, natural language processing, large language models and Web data mining.
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Authors: Riku Togashi, Kenshi Abe, Yuta Saito
Venue: WWW
Year: 2024

Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video) and their providers. However, there has been a growing understanding that the latter is crucial to consider for a wide range of applications, since it determines the utility of those being recommended. Prior approaches to fairness-aware recommendation optimize a regularized objective to balance user satisfaction and item fairness based on some notion such as exposure fairness. These existing methods have been shown to be effective in controlling fairness, however, most of them are computationally inefficient, limiting their applications to only unrealistically small-scale situations. This indeed implies that the literature does not yet provide a solution to enable a flexible control of exposure in the industry-scale recommender systems where millions of users and items exist. To enable a computationally efficient exposure control even for such large-scale systems, this work develops a scalable, fast, and fair method called exposure-aware ADMM (exADMM ). exADMM is based on implicit alternating least squares (iALS), a conventional scalable algorithm for collaborative filtering, but optimizes a regularized objective to achieve a flexible control of accuracy-fairness tradeoff. A particular technical challenge in developing exADMM is the fact that the fairness regularizer destroys the separability of optimization subproblems for users and items, which is an essential property to ensure the scalability of iALS. Therefore, we develop a set of optimization tools to enable yet scalable fairness control with provable convergence guarantees as a basis of our algorithm. Extensive experiments performed on three recommendation datasets demonstrate that exADMM enables a far more flexible fairness control than the vanilla version of iALS, while being much more computationally efficient than existing fairness-aware recommendation methods.
FaST: Accelerating Web Front-end Data Binding with Compiler and Visible Anchor
Authors: Tatsuru Tomizawa, Seiki Makino, Taiga Kume, Satoki Hamanaka, Tadashi Okoshi, Jin Nakazawa
Venue: WWW
Year: 2024

Data binding in web front-end development has made a significant contribution to removing complexity from development and simplifying programming. However, data binding has caused a degradation of website performance at the cost of reducing the burden on programmers. In this paper, we propose Visible Anchor to solve the performance degradation caused by data binding. We develop a compiler called FaST that implements the method. Then, We compared the rendering time among websites built by existing methods and FaST compiler. The evaluation result revealed that the websites built by FaST compiler are at minimum 2.9 times faster to be rendered than the ones built by the existing methods. FaST made a significant contribution to improving the performance of web front-end data binding. Consequently, data binding with FaST can be a better choice for web front-end development.
"All of Me": Mining Users' Attributes from their Public Spotify Playlists
Authors: Pier Paolo Tricomi, Luca Pajola, Luca Pasa, Mauro Conti
Venue: WWW
Year: 2024

In the age of digital music streaming, playlists on platforms like Spotify have become an integral part of individuals' musical experiences. People create and publicly share their own playlists to express their musical tastes, promote the discovery of their favorite artists, and foster social connections. In this work, we aim to address the question: can we infer users' private attributes from their public Spotify playlists? To this end, we conducted an online survey involving 739 Spotify users, resulting in a dataset of 10,286 publicly shared playlists comprising over 200,000 unique songs and 55,000 artists. Then, we utilize statistical analyses and machine learning algorithms to build accurate predictive models for users' attributes.
DIAERESIS: Knowledge Graph Partitioning for Efficient Query Answering
Authors: Georgia Troullinou, Kostas Stefanidis, Dimitris Plexousakis, Haridimos Kondylakis
Venue: WWW
Year: 2024

The rapid explosion of linked data demands effective and efficient storage, management, and querying methods. Apache Spark is one of the most widely used engines for big data processing, with more and more systems adopting it for efficient query answering. Existing approaches, exploiting Spark for querying RDF data, adopt partitioning techniques for reducing the data that need to be accessed in order to improve efficiency. However, simplistic methods for data partitioning fail to minimize data access at query answering and effectively improve query efficiency. In this demonstration, we present DIAERESIS, a novel platform that exploits a summary-based partitioning strategy achieving a significant improvement in minimizing data access and as such improving query-answering efficiency. DIAERESIS first identifies the top-k most important schema nodes and distributes the other schema nodes to the centroid they mostly depend on. Then, it allocates the corresponding instance nodes to the schema nodes they are instantiated under, creating vertical sub-partitions and indexes. We allow conference participants to actively identify the impact of our partitioning methodology on data distribution and replication, data accessed for query answering, and query answering efficiency. Further, we contrast our approach with existing partitioning approaches adopted by state-of-the-art systems in the domain, providing a deep understanding of the challenges in the area.
Identifying Risky Vendors in Cryptocurrency P2P Marketplaces
Authors: Taro Tsuchiya, Alejandro Cuevas Villalba, Nicolas Christin
Venue: WWW
Year: 2024

Peer-to-Peer (P2P) cryptocurrency exchanges are two-sided marketplaces, similar to eBay, where individuals can offer to sell cryptocurrencies in exchange for payment. Due to disintermediation, these marketplaces trade off increased privacy for higher risk (e.g., scams/fraud). Although these marketplaces use feedback systems to encourage healthier transactions, anecdotal evidence suggests that feedback often fails to capture vendor-associated risks. This work documents the online safety of cryptocurrency P2P marketplaces, identifies underlying issues in feedback-based reputation systems, and proposes improved mechanisms for predicting/monitoring risky accounts. We collect data from two cryptocurrency marketplaces, Paxful and LocalCoinSwap (LCS) for 12 months (06/2022--06/2023). The data includes over 396,000 listings, 67,000 vendors, and 4.7 million feedback for Paxful; and about 52,000 listings, 14,000 users, and 146,000 feedback for LCS.First, we show that the current feedback system does not sufficiently convey enough information about risky vendors, and is susceptible to reputation manipulation through user collusion and automation. Second, combining various publicly available information, we build machine learning models to predict account suspension, and achieve a 0.86 F1-score and 0.93 AUC for Paxful. Third, while our models appear to have limited transferability across markets, we identify which features most help account suspension across platforms. Finally, we perform a month-long online evaluation to show that our models are significantly more successful than mere feedback-based reputation schemes at predicting which users will be suspended in the future.