OpenRaiser/Intern-Atlas huggingface dataset:
Self-Paced Pairwise Representation Learning for Semi-Supervised Text Classification
Authors: Junfan Chen, Richong Zhang, Jiarui Wang, Chunming Hu, Yongyi Mao
Venue: WWW
Year: 2024

Text classification is one vital tool assisting web content mining. Semi-supervised text classification (SSTC) offers an approach to alleviate the burden of annotation costs by training on a few labeled texts alongside many unlabeled texts. Unsolved challenges in SSTC are the overfitting problem caused by the limited labeled data and the mislabeling problem of unlabeled texts. To address these issues, this paper proposes a Self-Paced PairWise representation learning (SPPW) model. Concretely, SPPW alleviates the overfitting problem by replacing the overfitting-prone learning of a parameterized classifier with representation learning in a pair-wise manner. Besides, we propose a novel self-paced text filtering method that effectively integrates both label confidence and text hardness to reduce mislabeled texts synergistically. Extensive experiments on 3 benchmark SSTC datasets show that SPPW outperforms baselines and is effective in mitigating overfitting and mislabeling problems.
Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems
Authors: Xiaoshuang Chen, Gengrui Zhang, Yao Wang, Yulin Wu, Shuo Su, Kaiqiao Zhan, Ben Wang
Venue: WWW
Year: 2024

Modern large-scale recommender systems are built upon computation-intensive infrastructure and usually suffer from a huge difference in traffic between peak and off-peak periods. In peak periods, it is challenging to perform real-time computation for each request due to the limited budget of computational resources. The recommendation with a cache is a solution to this problem, where a user-wise result cache is used to provide recommendations when the recommender system cannot afford a real-time computation. However, the cached recommendations are usually suboptimal compared to real-time computation, and it is challenging to determine the items in the cache for each user. In this paper, we provide a cache-aware reinforcement learning (CARL) method to jointly optimize the recommendation by real-time computation and by the cache. We formulate the problem as a Markov decision process with user states and a cache state, where the cache state represents whether the recommender system performs recommendations by real-time computation or by the cache. The cache state is determined by the computational load of the recommender system. We perform reinforcement learning based on such a model to improve user engagement over multiple requests. Moreover, we show that the cache will introduce a challenge called critic dependency, which deteriorates the performance of reinforcement learning. To tackle this challenge, we propose an eigenfunction learning (EL) method to learn independent critics for CARL. Experiments show that CARL can significantly improve the users' engagement when considering the result cache. CARL has been fully launched in Kwai app, serving over 100 million users.
DualCL: Principled Supervised Contrastive Learning as Mutual Information Maximization for Text Classification
Authors: Junfan Chen, Richong Zhang, Yaowei Zheng, Qianben Chen, Chunming Hu, Yongyi Mao
Venue: WWW
Year: 2024

Text classification is a fundamental task in web content mining. Although the existing supervised contrastive learning (SCL) approach combined with pre-trained language models (PLMs) has achieved leading performance in text classification, it lacks fundamental principles. Theoretically motivated by a derived lower bound of mutual information maximization, we propose a dual contrastive learning framework DualCL that satisfies three properties, i.e., parameter-free, augmentation-easy and label-aware. DualCL generates classifier parameters from the PLM and simultaneously uses them for classification and as augmented views of the input text for supervised contrastive learning. Extensive experiments conclusively demonstrate that DualCL excels in learning superior text representations and consistently outperforms baseline models.
Modeling Multidimensional Cognitive Search in Creativity with Generalized Additive Model
Authors: Jia Lin Cheoh
Venue: WWW
Year: 2024

Creativity is the ability to develop innovative functional ideas through unconventional associations. The consensus view on creativity in the literature involves divergence from stereotypical and habitual thought patterns [29, 39]. Creativity relies on search to explore diverse solutions. Search requires charting the mental terrain, leveraging past experiences and knowledge to manipulate and reconfigure components for new solutions [28]. The generally-accepted and overly-narrow view on creativity, however, neglects the fact that creativity is multidimensional [14]. This one-dimensional view of creativity triggers questions such as "Does one consider an unethical but novel creation to be creative?" and "Does one consider a new iPhone with mainstream functionalities but advanced camera features to be creative?" This research challenges the one-dimensional view of creativity, offering a more all-encompassing conceptualization of creativity [14]. The research examines the multidimensional nature of creativity by building a computational model of a designer's mutual search process across multiple mutually dependent search spaces. The research examines the trajectory of mutual search across multiple cognitive search spaces using a Generalized Additive Model (GAM). The field experiment employs 108 designers who develop their web designs through five iterations, utilizing computer graphics methods to extract the images. Through measuring the distance of search by considering changes in visual and source code in each iteration, the study argues that the search patterns differ in the degree of exploration in these search spaces over time. The research concludes that designers' search processes are non-linear and argues that there are more than one or two search spaces. The research also provides perceptual explanations of the multiple search processes in designs and argues for a more encompassing view of creativity.
Outgroup Dehumanisation in Telegram - the Role of Ingroup Identity and Perception
Authors: Elizaveta Chernenko
Venue: WWW
Year: 2024

Intergroup dehumanisation represents a pressing concern for today's society. It hinders empathy, prosocial behaviour, and contributes to between-group aggression. Its consequences are particularly dangerous in the context of international military conflicts as dehumanisation contributes to support for war, war-related violence, and usually accompanies genocidal conflicts. This motivated the focus of this study on the blatant forms of dehumanisation towards an outgroup defined in political or national terms, with a specific focus on the relations between Ukrainians, Russians, and Belarusians around the time of the Russian invasion of Ukraine in 2022. The study draws attention to previously under-researched aspect in outgroup dehumanisation, specifically the role of ingroup perception in it. Outgroup dehumanisation involves excluding the outgroup from the community one identifies with, thus reinforcing the boundary between ingroup and outgroup. This highlights the comparative nature of dehumanisation, suggesting its basis might lie more in comparative ingroup superiority bias rather than in outgroup inferiority bias. Existing research however generally concentrates solely on negative aspects of outgroup perception in dehumanising attitudes. While some studies have gauged dehumanisation through ingroup-outgroup perception differences, they lacked a ground truth measure for dehumanisation, leaving its comparative nature largely unexamined. Employing generative Large Language Model, we develop a dataset of Telegram channels posts, classified as dehumanising or neutral. Utilising NLP tools we analyse the role of ingroup-outgroup perception disparities in dehumanisation, specifically addressing its relation to affective polarisation.
Discrete Choice and Applications
Authors: Flavio Chierichetti, Ravi Kumar, Andrew Tomkins
Venue: WWW
Year: 2024

Modern machine learning and AI have revolutionized the generation of ranking and recommendations across many domains, taking data-driven approaches to inferring the candidate items a user is most likely to select. The theory of discrete choice provides the theoretical underpinnings for study of these problems. This theory is central to economics and behavioral sciences, and was recognized with the 2000 Nobel Prize in economics awarded to Daniel McFadden for his work on the analysis of discrete choice. Classical work in this area, and a wide range of recent advances, have much to offer in thinking about how to support users in making choices. However, many of the central tools of discrete choice are not broadly known to the researchers in our area. In this proposed tutorial, we will cover the foundations of the field, and provide connections to common tools of machine learning such as logistic regression, multinomial regression, and softmax. We will cover a number of results about the representational power and complexity of learning various models of choice. And finally, we will suggest both open problems in the field, and areas where discrete choice tools are relevant to key problems in the Web Conference community.
PhishinWebView: Analysis of Anti-Phishing Entities in Mobile Apps with WebView Targeted Phishing
Authors: Yoonjung Choi, Woonghee Lee, Junbeom Hur
Venue: WWW
Year: 2024

Despite the relentless efforts on developing anti-phishing techniques, phishing attacks continue to proliferate, often incorporating evasion techniques to bypass detection. While recent studies have continuously enhanced our understanding of their evasion techniques in desktop environments, few studies have been conducted to explore how the phishing attack is being handled in mobile environments, specifically WebView. In this study, we systematically evaluate the blocking processes of anti-phishing entities in individual apps in the real world by designing the phishing attack tailored to WebView. Specifically, we select eight well-known apps using WebView, and report 80 typical phishing sites (without evasion techniques) and 130 user-agent-specific phishing sites (accessible exclusively via each app's WebView). For scalable analysis, we develop an autonomous evaluation framework and investigate accessibility of both apps and Safe Browsing entities. As a result, we find that user-agent-specific (UA-specific) phishing sites successfully evade blocking across all of the eight Android apps. We also investigate accessing strategies of anti-phishing crawlers of both the apps and Safe Browsing entities; and find that only two apps' crawlers can access UA-specific phishing sites without any subsequent actions such as blocking the link. Based on our experiment results, we present security recommendations to take proactive phishing cautions using link preview bots. To the best of our knowledge, this is the first study that explores how the WebView environments handle phishing attacks and disclose their limitation in the real world.
DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
Authors: Seungyoon Choi, Wonjoong Kim, Sungwon Kim, Yeonjun In, Sein Kim, Chanyoung Park
Venue: WWW
Year: 2024

We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods. Existing rehearsal-based GCL methods select the most representative nodes for each class and store them in a replay buffer for later use in training subsequent tasks. However, we discovered that considering only the class representativeness of each replayed node makes the replayed nodes to be concentrated around the center of each class, incurring a potential risk of overfitting to nodes residing in those regions, which aggravates catastrophic forgetting. Moreover, as the rehearsal-based approach heavily relies on a few replayed nodes to retain knowledge obtained from previous tasks, involving the replayed nodes that have irrelevant neighbors in the model training may have a significant detrimental impact on model performance. In this paper, we propose a GCL model named DSLR, specifically, we devise a coverage-based diversity (CD) approach to consider both the class representativeness and the diversity within each class of the replayed nodes. Moreover, we adopt graph structure learning (GSL) to ensure that the replayed nodes are connected to truly informative neighbors. Extensive experimental results demonstrate the effectiveness and efficiency of DSLR. Our source code is available at https://github.com/seungyoon-Choi/DSLR_official.
What News Do People Get on Social Media? Analyzing Exposure and Consumption of News through Data Donations
Authors: Salim Chouaki, Abhijnan Chakraborty, Oana Goga, Savvas Zannettou
Venue: WWW
Year: 2024

Understanding how exposure to news on social media impacts public discourse and exacerbates political polarization is a significant endeavor in both computer and social sciences. Unfortunately, progress in this area is hampered by limited access to data due to the closed nature of social media platforms. Consequently, prior studies have been constrained to considering only fragments of users' news exposure and reactions. To overcome this obstacle, we present an innovative measurement approach centered on donating personal data for scientific purposes, facilitated through a privacy-preserving tool that captures users' interactions with news on Facebook. This approach offers a nuanced perspective on users' news exposure and consumption, encompassing different types of news exposure: selective, incidental, algorithmic, and targeted, driven by the diverse underlying mechanisms governing news appearance on users' feeds. Our analysis of data from 472 participants based in the U.S. reveals several interesting findings. For instance, users are more prone to encountering misinformation because of their active selection of low-quality news sources rather than being exposed solely due to friends or platform algorithms. Furthermore, our study uncovers that users are open to engaging with news sources with opposite political ideology as long as these interactions are not visible to their immediate social circles. Overall, our study showcases the viability of data donation as a means to provide clarity to longstanding questions in this field, offering new perspectives on the intricate dynamics of social media news consumption and its effects.
An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce
Authors: Nurendra Choudhary, Edward W. Huang, Karthik Subbian, Chandan K. Reddy
Venue: WWW
Year: 2024

The problem of search relevance in the E-commerce domain is a challenging one since it involves understanding the intent of a user's short nuanced query and matching it with the appropriate products in the catalog. This problem has traditionally been addressed using language models (LMs) and graph neural networks (GNNs) to capture semantic and inter-product behavior signals, respectively. However, the rapid development of new architectures has created a gap between research and the practical adoption of these techniques. Evaluating the generalizability of these models for deployment requires extensive experimentation on complex, real-world datasets, which can be non-trivial and expensive. Furthermore, such models often operate on latent space representations that are incomprehensible to humans, making it difficult to evaluate and compare the effectiveness of different models. This lack of interpretability hinders the development and adoption of new techniques in the field. To bridge this gap, we propose Plug and Play Graph LAnguage Model (PP-GLAM), an explainable ensemble of plug and play models. Our approach uses a modular framework with uniform data processing pipelines. It employs additive explanation metrics to independently decide whether to include (i) language model candidates, (ii) GNN model candidates, and (iii) inter-product behavioral signals. For the task of search relevance, we show that PP-GLAM outperforms several state-of-the-art baselines as well as a proprietary model on real-world multilingual, multi-regional e-commerce datasets. To promote better model comprehensibility and adoption, we also provide an analysis of the explainability and computational complexity of our model. We also provide the public codebase and provide a deployment strategy for practical implementation.
Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response
Authors: Md Towhidul Absar Chowdhury, Soumyajit Datta, Naveen Sharma, Ashiqur R. KhudaBukhsh
Venue: WWW
Year: 2024

Current research concentrates on studying discussions on social media related to structural failures to improve disaster response strategies. However, detecting social web posts discussing concerns about anticipatory failures is under-explored. If such concerns are channeled to the appropriate authorities, it can aid in the prevention and mitigation of potential infrastructural failures. In this paper, we develop an infrastructure ombudsman -- that automatically detects specific infrastructure concerns. Our work considers several recent structural failures in the US. We present a first-of-its-kind dataset of 2,662 social web instances for this novel task mined from Reddit and YouTube.
Navigating Multidimensional Ideologies with Reddit's Political Compass: Economic Conflict and Social Affinity
Authors: Ernesto Colacrai, Federico Cinus, Gianmarco De Francisci Morales, Michele Starnini
Venue: WWW
Year: 2024

The prevalent perspective in quantitative research on opinion dynamics flattens the landscape of the online political discourse into a traditional left--right dichotomy. While this approach helps simplify the analysis and modeling effort, it also neglects the intrinsic multidimensional richness of ideologies. In this study, we analyze social interactions on Reddit, under the lens of a multi-dimensional ideological framework: the political compass. We examine over 8 million comments posted on the subreddits /r/PoliticalCompass and /r/PoliticalCompassMemes during 2020--2022. By leveraging their self-declarations, we disentangle the ideological dimensions of users into economic (left--right) and social (libertarian--authoritarian) axes. In addition, we characterize users by their demographic attributes (age, gender, and affluence). We find significant homophily for interactions along the social axis of the political compass and demographic attributes. Compared to a null model, interactions among individuals of similar ideology surpass expectations by 6%. In contrast, we uncover a significant heterophily along the economic axis: left/right interactions exceed expectations by 10%. Furthermore, heterophilic interactions are characterized by a higher language toxicity than homophilic interactions, which hints at a conflictual discourse between every opposite ideology. Our results help reconcile apparent contradictions in recent literature, which found a superposition of homophilic and heterophilic interactions in online political discussions. By disentangling such interactions into the economic and social axes we pave the way for a deeper understanding of opinion dynamics on social media.
Towards Graph Foundation Models for Personalization
Authors: Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas
Venue: WWW
Year: 2024

In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions. In this regard, two of the biggest trends in research around this subject are Graph Neural Networks (GNNs) and Foundation Models (FM). While GNNs emerged as a popular solution in industry for powering personalization at scale, FMs have only recently caught attention for their promising performance in personalization tasks like ranking and retrieval. In this paper, we present a graph-based foundation modeling approach tailored to personalization. Central to this approach is a Heterogeneous GNN (HGNN) designed to capture multi-hop content and consumption relationships across a range of recommendable item types. To ensure the generality required from a Foundation Model, we employ a Large Language Model (LLM) text-based featurization of nodes that accommodates all item types, and construct the graph using co-interaction signals, which inherently transcend content specificity. To facilitate practical generalization, we further couple the HGNN with an adaptation mechanism based on a two-tower (2T) architecture, which also operates agnostically to content type. This multi-stage approach ensures high scalability; while the HGNN produces general purpose embeddings, the 2T component models in a continuous space the sheer size of user-item interaction data. Our comprehensive approach has been rigorously tested and proven effective in delivering recommendations across a diverse array of products within a real-world, industrial audio streaming platform.
Near-duplicate Question Detection
Authors: Preetam Prabhu Srikar Dammu, Omar Alonso
Venue: WWW
Year: 2024

Suggesting relevant questions to users is an important task in various applications, such as community Q&A or e-commerce websites. To ensure that there is no redundancy in the selected set of candidate questions, it is essential to filter out any near-duplicate questions. Identifying near-duplicate questions has another use case in light of the adoption of Large Language Models (LLMs) - fetching pre-computed answers for similar questions. However, identifying the similarity of questions is a bit more complex in comparison to generic text, as questions entail open-ended information that is not explicitly contained within the wording of the question itself. We introduce a taxonomy that accounts for the subtle intricacies characteristic of near-duplicate questions and propose a method for detecting them utilizing the capabilities of LLMs.
Knowledge Induced Transformer Network for Causality Prediction
Authors: Tirthankar Dasgupta, Manjira Sinha, Abir Naskar
Venue: WWW
Year: 2024

Causal extraction from text plays a crucial role in various downstream analytical and predictive tasks, such as constructing repositories of causal insights for reasoning. However, existing models often overlook the rich contextual commonsense knowledge that could enhance the reasoning process and evaluate underlying causal mechanisms. In this study, we introduce a knowledge-induced transformer architecture for predicting causality. Our model accepts an antecedent and a set of contextual knowledge as input, then ranks plausible consequences from a given set of hypotheses. To enhance semantic understanding, we augment the transformer with a relational graph network, which computes fine-grained semantic information between the antecedent, knowledge, and hypotheses using a similarity matrix that quantifies word-to-word similarity. We evaluate the proposed architecture against state-of-the-art models using openly available datasets and demonstrate its superior performance.