OpenRaiser/Intern-Atlas huggingface dataset:
"I Have No Text in My Post": Using Visual Hints to Model User Emotions in Social Media
Authors: Junho Song, Kyungsik Han, Sang-Wook Kim
Venue: WWW
Year: 2022

As an emotion plays an important role in people’s everyday lives and is often mirrored in their social media use, extensive research has been conducted to characterize and model emotions from social media data. However, prior research has not sufficiently considered trends of social media use—the increasing use of images and the decreasing use of text—nor identified the features of images in social media that are likely to be different from those in non-social media. Our study aims to fill this gap by (1) considering the notion of visual hints that depict contextual information of images, (2) presenting their characteristics in positive or negative emotions, and (3) demonstrating their effectiveness in emotion prediction modeling through an in-depth analysis of their relationship with the text in the same posts. The results of our experiments showed that our visual hint-based model achieved 20% improvement in emotion prediction, compared with the baseline. In particular, the performance of our model was comparable with that of the text-based model, highlighting not only a strong relationship between visual hints of the image and emotion, but also the potential of using only images for emotion prediction which well reflects current and future trends of social media use.
12th Temporal Web Analytics Workshop (TempWeb) Overview
Authors: Marc Spaniol, Ricardo Baeza-Yates, Omar Alonso
Venue: WWW
Year: 2022

TempWeb focuses on investigating infrastructures, scalable methods, and innovative software for aggregating, querying, and analyzing heterogeneous data at Web scale. Emphasis is given to data analysis along the time dimension for web data that has been collected over extended time periods. A major challenge in this regard is the sheer size of the data it exposes and the ability to make sense of it in a useful and meaningful manner for its users. It is worth noting that this trend of using big data to make inferences is not specific to Web content analytics, so work presented here might be useful in other areas, too. As such, longitudinal aspects in Web content analysis become relevant for analysts from from various domains, including, but not limited to sociology, marketing, environmental studies, politics, etc. Studies in this context range from “low-level” structural network log analysis over time, up to “high-level” entity-level Web content analytics and terminology evolution. While both before mentioned aspects represent the extremes of the spectrum, they have one thing in common: Web scale data analytics needs to develop infrastructures and extended analytical tools in order to make use of that data. TempWeb has been created for this purpose and is the ideal venue to discuss about all its facets.
Rumor Detection on Social Media with Graph Adversarial Contrastive Learning
Authors: Tiening Sun, Zhong Qian, Sujun Dong, Peifeng Li, Qiaoming Zhu
Venue: WWW
Year: 2022

Rumors spread through the Internet, especially on Twitter, have harmed social stability and residents’ daily lives. Recently, in addition to utilizing the text features of posts for rumor detection, the structural information of rumor propagation trees has also been valued. Most rumors with salient features can be quickly locked by graph models dominated by cross entropy loss. However, these conventional models may lead to poor generalization, and lack robustness in the face of noise and adversarial rumors, or even the conversational structures that is deliberately perturbed (e.g., adding or deleting some comments). In this paper, we propose a novel Graph Adversarial Contrastive Learning (GACL) method to fight these complex cases, where the contrastive learning is introduced as part of the loss function for explicitly perceiving differences between conversational threads of the same class and different classes. At the same time, an Adversarial Feature Transformation (AFT) module is designed to produce conflicting samples for pressurizing model to mine event-invariant features. These adversarial samples are also used as hard negative samples in contrastive learning to make the model more robust and effective. Experimental results on three public benchmark datasets prove that our GACL method achieves better results than other state-of-the-art models.
Walks in Cyberspace: Improving Web Browsing and Network Activity Analysis with 3D Live Graph Rendering
Authors: Lionel Tailhardat, Raphaël Troncy, Yoan Chabot
Venue: WWW
Year: 2022

Web navigation generates traces that are useful for Web cartography, user behavior analysis (UEBA) and resource allocation planning. However, this data still needs to be interpreted, sometimes enriched and appropriately visualized to reach its full potential. In this paper, we propose to explore the strengths and weaknesses of standard data collection methods such as mining Web browser history and network traffic dumps. We developed the DynaGraph framework that combines classical traces dumping tools with a Web application for live 3D rendering of graph data. We show that mining navigation history provides useful insights but fails to provide real-time analytics and is not easy to deploy. Conversely, mining network traffic dumps appears easy to set up but rapidly fails once the data traffic is encrypted. We show that 3D rendering allows to highlight navigation patterns for a given data sampling rate.
Knowledge-based Temporal Fusion Network for Interpretable Online Video Popularity Prediction
Authors: Shisong Tang, Qing Li, Xiaoteng Ma, Ci Gao, Dingmin Wang, Yong Jiang, Qian Ma, Aoyang Zhang, Hechang Chen
Venue: WWW
Year: 2022

Predicting the popularity of online videos has many real-world applications, such as recommendation, precise advertising, and edge caching strategies. Despite many efforts have been dedicated to the online video popularity prediction, there still exist several challenges: (1) The meta-data from online videos is usually sparse and noisy, which makes it difficult to learn a stable and robust representation. (2) The influence of content features and temporal features in different life cycles of online videos is dynamically changing, so it is necessary to build a model that can capture the dynamics. (3) Besides, there is a great need to interpret the predictive behavior of the model to assist administrators of video platforms in the subsequent decision-making. In this paper, we propose a Knowledge-based Temporal Fusion Network (KTFN) that incorporates knowledge graph representation to address the aforementioned challenges in the task of online video popularity prediction. To be more specific, we design a Tree Attention Network (TAN) to learn the embedding of online video entities in knowledge graphs via selectively aggregating local neighborhood information, thus enabling our model to learn the importance of different entities under the same relation. Besides, an Attention-based Long Short-Term Memory (ALSTM) is utilized to learn the temporal feature representation. Finally, we propose an Adaptively Temporal Feature Fusion (ATFF) scheme to adaptively fuse content features and temporal features, in which a learnable exponential decay function with the global attention mechanism is constructed. We collect two large-scale real-world datasets from the server logs of two popular Chinese online video platforms, and experimental results on the two datasets have demonstrated the superiority and interpretability of KTFN.
Revisiting Graph based Social Recommendation: A Distillation Enhanced Social Graph Network
Authors: Ye Tao, Ying Li, Su Zhang, Zhirong Hou, Zhonghai Wu
Venue: WWW
Year: 2022

Social recommendation, which leverages social connections to construct Recommender Systems (RS), plays an important role in alleviating information overload. Recently, Graph Neural Networks (GNNs) have received increasing attention due to their great capacity for graph data. Since data in RS is essentially in the structure of graphs, GNN-based RS is flourishing. However, existing works lack in-depth thinking of social recommendations. These methods contain implicit assumptions that are not well analyzed in practice. To tackle these problems, we conduct statistical analyses on widely used social recommendation datasets. We design metrics to evaluate the social information, which can provide guidance about whether and how we should use this information in the RS task. Based on these analyses, we propose a Distillation Enhanced SocIal Graph Network (DESIGN). We train a model that integrates information from the user-item interaction graph and the user-user social graph and train two auxiliary models that only use one of the above graphs respectively. These models are trained simultaneously, where the knowledge distillation technique restricts the training process and makes them learn from each other. Our extensive experiments show that our model significantly and consistently outperforms the state-of-the-art competitors on real-world datasets.
How the Web Will Shape the Hybrid Work Era: Keynote Talk
Authors: Jaime Teevan
Venue: WWW
Year: 2022

Jaime Teevan has given a Keynote Talk at The Web Conference on Friday 29th April 2022. This page provides a summary of the topics she addressed during her talk.
MI-GCN: Node Mutual Information-based Graph Convolutional Network
Authors: Lei Tian, Huaming Wu
Venue: WWW
Year: 2022

Graph Neural Networks (GNNs) have been widely used in various processing tasks for processing graphs and complex network data. However, in recent studies, GNNs cannot effectively process the structural topology information and characteristics of the nodes in the graph, or even fail to deal with the information of the nodes. For optimal node embedding aggregation and delivery, this weakness may severely affect the ability of GNNs to classify nodes. In order to overcome this issue, we propose a novel node Mutual Information-based Graph Convolutional Network (MI-GCN) for semi-supervised node classification. First, we analyze the node information entropy that measures the importance of nodes in the complex network, and further define the node joint information entropy and node mutual information in the graph data. Then, we use node mutual information to strengthen the ability of GNNs to fuse node structural information. Extensive experiments demonstrate that our MI-GCN not only retains the advantages of the most advanced GNNs, but also improves the ability to fuse node structural information. MI-GCN can achieve superior performance on node classification compared to several baselines on real-world multi-type datasets, including fixed data splits and random data splits.
BeyondFacts'22: 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis
Authors: Konstantin Todorov, Pavlos Fafalios, Stefan Dietze
Venue: WWW
Year: 2022

Expressing opinions and interacting with others on the Web has led to an abundance of online discourse: claims and viewpoints on controversial topics, their sources and contexts. This constitutes a valuable source of insights for studies into mis- / disinformation spread, bias reinforcement, echo chambers or political agenda setting. While knowledge graphs promise to provide the key to a Web of structured information, they are mainly focused on facts without keeping track of the diversity, connection or temporal evolution of online discourse. As opposed to facts, claims and viewpoints 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 computational social science, to argumentation mining, fact-checking, or viewpoint/stance detection. The 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis (BeyondFacts’22, equivalently abbreviated as KnOD’22) 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 addresses the need for a shared understanding and structured knowledge about discourse in order to enable machine-interpretation, discoverability and reuse, in support of studies into the analysis of societal debates.
Improving Operation Efficiency through Predicting Credit Card Application Turnaround Time with Index-based Encoding
Authors: Jing Xiang Toh, Kay Jan Wong, Samarth Agarwal, Xuejie Zhang, John Jianan Lu
Venue: WWW
Year: 2022

This paper presents the successful use of index encoding and machine learning to predict the turnaround time of a complex business process – the credit card application process. Predictions are made on in-progress processes and refreshed when new information is available. The business process is complex, with each individual instance having different steps, sequence, and length. For instances predicted to have higher than normal turnaround time, model explain-ability is employed to identify the top reasons. This allows for intervention in the process to potentially reduce turnaround time before completion.
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices
Authors: Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra
Venue: WWW
Year: 2022

Graph classification has a wide range of applications in bioinformatics, social sciences, automated fake news detection, web document classification, and more. In many practical scenarios, including web-scale applications, labels are scarce or hard to obtain. Unsupervised learning is thus a natural paradigm for these settings, but its performance often lags behind that of supervised learning. However, recently contrastive learning (CL) has enabled unsupervised computer vision models to perform comparably to supervised models. Theoretical and empirical works analyzing visual CL frameworks find that leveraging large datasets and task relevant augmentations is essential for CL framework success. Interestingly, graph CL frameworks report high performance while using orders of magnitude smaller data, and employing domain-agnostic graph augmentations (DAGAs) that can corrupt task relevant information. Motivated by these discrepancies, we seek to determine why existing graph CL frameworks continue to perform well, and identify flawed practices in graph data augmentation and popular graph CL evaluation protocols. We find that DAGA can destroy task-relevant information and harm the model’s ability to learn discriminative representations. We also show that on small benchmark datasets, the inductive bias of graph neural networks can significantly compensate for these limitations, while on larger graph classification tasks commonly-used DAGAs perform poorly. Based on our findings, we propose better practices and sanity checks for future research and applications, including adhering to principles in visual CL when designing context-aware graph augmentations. For example, in graph-based document classification, which can be used for better web search, we show task-relevant augmentations improve accuracy by up to 20.
KAHAN: Knowledge-Aware Hierarchical Attention Network for Fake News detection on Social Media
Authors: Yu-Wun Tseng, Hui-Kuo Yang, Wei-Yao Wang, Wen-Chih Peng
Venue: WWW
Year: 2022

In recent years, fake news detection has attracted a great deal of attention due to the myriad amounts of misinformation. Some previous methods have focused on modeling the news content, while others have combined user comments and user information on social media. However, existing methods ignore some important clues for detecting fake news, such as temporal information on social media and external knowledge related to the news. To this end, we propose a Knowledge-Aware Hierarchical Attention Network (KAHAN) that integrates this information into the model to establish fact-based associations with entities in the news content. Specifically, we introduce two hierarchical attention networks to model news content and user comments respectively, in which news content and user comments are represented by different aspects for modeling various degrees of semantic granularity. Besides, to process the random occurrences of user comments at post-level, we further designed a time-based subevent division algorithm to aggregate user comments at subevent-level to learn temporal patterns. Moreover, News towards Entities (N-E) attention and Comments towards Entities (C-E) attention are introduced to measure the importance of external knowledge. Finally, we detected the veracity of the news by combining the three aspects of news: content, user comments, and external knowledge. We conducted extensive experiments and ablation studies on two real-world datasets and showed that our proposed method outperformed the previous methods and empirically validated each component of KAHAN1.
Regulating Online Political Advertising
Authors: Eray Turkel
Venue: WWW
Year: 2022

In the United States, regulations have been established in the past to oversee political advertising in TV and radio. The laws governing these marketplaces were enacted with the fundamental premise that important political information is provided to voters through advertising, and politicians should be able to easily inform the public. Today, online advertising constitutes a large fraction of all political ad spending, but lawmakers have not been able to keep up with this rapid change. In the online advertising marketplace, ads are typically allocated to the highest bidder through an auction. Auction mechanisms provide benefits to platforms in terms of revenue maximization and automation, but they operate very differently to offline advertising, and existing approaches to regulation cannot be easily implemented in auction-based environments. We first provide a theoretical model and deliver key insights that can be used to regulate online ad auctions for political ads, and analyze the implications of the proposed interventions empirically. We characterize the optimal auction mechanisms where the regulator takes into account both the ad revenues collected and societal objectives (such as the share of ads allocated to politicians, or the prices paid by them). We use bid data generated from Twitter’s political advertising database to analyze the implications of implementing these changes. The results suggest that achieving favorable societal outcomes at a small revenue cost is possible through easily implementable, simple regulatory interventions.
Data Models for Annotating Biomedical Scholarly Publications: the Case of CORD-19
Authors: Houcemeddine Turki, Mohamed Ali Hadj Taieb, Alejandro Piad-Morffis, Mohamed Ben Aouicha, René Fabrice Bile
Venue: WWW
Year: 2022

Semantic text annotations have been a key factor for supporting computer applications ranging from knowledge graph construction to biomedical question answering. In this systematic review, we provide an analysis of the data models that have been applied to semantic annotation projects for the scholarly publications available in the CORD-19 dataset, an open database of the full texts of scholarly publications about COVID-19. Based on Google Scholar and the screening of specific research venues, we retrieve seventeen publications on the topic mostly from the United States of America. Subsequently, we outline and explain the inline semantic annotation models currently applied on the full texts of biomedical scholarly publications. Then, we discuss the data models currently used with reference to semantic annotation projects on the CORD-19 dataset to provide interesting directions for the development of semantic annotation models and projects.
ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer
Authors: Kohei Uehara, Yusuke Mori, Yusuke Mukuta, Tatsuya Harada
Venue: WWW
Year: 2022

Image narrative generation is a task to create a story from an image with a subjective viewpoint. Given the importance of the subjective feelings of writers, readers, and characters in storytelling, an image narrative generation method should consider human emotion. In this study, we propose a novel method of image narrative generation called ViNTER (Visual Narrative Transformer with Emotion arc Representation), which takes “emotion arc” as input to capture a sequence of emotional changes. Since emotion arcs represent the trajectory of emotional change, it is expected that we can include detailed information about the emotional changes in the story to the model. We present experimental results of both automatic and manual evaluations on the Image Narrative dataset and demonstrate the effectiveness of the proposed approach.
3rd Data Literacy Workshop
Authors: Manuel León Urrutia, Davide Taibi
Venue: WWW
Year: 2022

The 31st edition of the ACM Web Conference (Lyon, 2022 online) hosted the Third Data Literacy workshop. This series of Data Literacy workshops responds to the increasing relevance of data literacy in the field of education as well as to the need for a data literate workforce in our current data-driven economy and society. This half-day workshop included three presentations of the accepted and published papers, a keynote by a leading researcher in the field Ellen Mandinach, and a round table with principal investigators in European Data Literacy projects. In line with its two previous editions, this workshop aims to bring together a diverse community of practice and research from different fields with a common interest: equipping society with the necessary competencies to make sense of the sheer amounts of data to which we come across in our daily lives.
Why Bother Enabling Biomedical Literature Analysis with Semantics?
Authors: Karin Verspoor
Venue: WWW
Year: 2022

These days, ELMo [3], BERT [1], BART [2] and other similarly cutely-named models appear to have dramatically advanced the state of the art in basically every problem in natural language processing and information retrieval. It can leave a researcher wondering whether there is more to language processing than deploying or fine-tuning contextual word embeddings. What of formal semantics and knowledge representation? What value do these bring to text analysis, either in modelling or in task definitions? In this talk, I will try to explore these questions, from the perspective of my long-running experiences in biomedical information extraction and literature exploration. Perhaps we can shift the academic conversation from a one-model-fits-all solution for individual tasks to a more nuanced consideration of complex, multi-faceted problems in which such models certainly can play a critical role but aren’t necessarily “all you need” [4].
CAAW'22: 2022 International Workshop on Cryptoasset Analytics
Authors: Friedhelm Victor, Bernhard Haslhofer, Sourav Sen Gupta, Malte Möser, Rainer Böhme
Venue: WWW
Year: 2022

The half-day workshop on Cryptoasset Analytics allows researchers from different disciplines to present their newest findings related to cryptoassets. This workshop is relevant for the Web research community for two reasons. First, on a technical level, fundamental concepts of cryptoassets are increasingly integrated with Web technologies. Second, we witness the formation of socio-technical cryptoasset ecosystems, which are tightly connected to the Web. The program features a mix of invited talks and a selection of peer-reviewed submissions. Workshop topics range from empirical studies, over analytics methods and tools, to case studies, datasets, and cross-cutting issues like legal or ethical aspects.