OpenRaiser/Intern-Atlas huggingface dataset:
CaML: Carbon Footprinting of Household Products with Zero-Shot Semantic Text Similarity
Authors: Bharathan Balaji, Venkata Sai Gargeya Vunnava, Geoffrey Guest, Jared Kramer
Venue: WWW
Year: 2023

Products contribute to carbon emissions in each phase of their life cycle, from manufacturing to disposal. Estimating the embodied carbon in products is a key step towards understanding their impact, and undertaking mitigation actions. Precise carbon attribution is challenging at scale, requiring both domain expertise and granular supply chain data. As a first-order approximation, standard reports use Economic Input-Output based Life Cycle Assessment (EIO-LCA) which estimates carbon emissions per dollar at an industry sector level using transactions between different parts of the economy. EIO-LCA models map products to an industry sector, and uses the corresponding carbon per dollar estimates to calculate the embodied carbon footprint of a product. An LCA expert needs to map each product to one of upwards of 1000 potential industry sectors. To reduce the annotation burden, the standard practice is to group products by categories, and map categories to their corresponding industry sector. We present CaML, an algorithm to automate EIO-LCA using semantic text similarity matching by leveraging the text descriptions of the product and the industry sector. CaML uses a pre-trained sentence transformer model to rank the top-5 matches, and asks a human to check if any of them are a good match. We annotated 40K products with non-experts. Our results reveal that pre-defined product categories are heterogeneous with respect to EIO-LCA industry sectors, and lead to a large mean absolute percentage error (MAPE) of 51% in kgCO2e/$. CaML outperforms the previous manually intensive method, yielding a MAPE of 22% with no domain labels (zero-shot). We compared annotations of a small sample of 210 products with LCA experts, and find that CaML accuracy is comparable to that of annotations by non-experts.
Improving Netflix Video Quality with Neural Networks
Authors: Christos G. Bampis, Li-Heng Chen, Zhi Li
Venue: WWW
Year: 2023

Video downscaling is an important component of adaptive video streaming, which tailors streaming to screen resolutions of different devices and optimizes picture quality under varying network conditions. With video downscaling, a high-resolution input video is downscaled into multiple lower-resolution videos. This is typically done by a conventional resampling filter like Lanczos. In this talk, we describe how we improved Netflix video quality by developing neural networks for video downscaling and deploying them at scale.
Addressing socially destructive disinformation on the web with advanced AI tools: Russia as a case study
Authors: Florian Barbaro, Andy Skumanich
Venue: WWW
Year: 2023

Today, disinformation (i.e. deliberate misinformation) is omnipresent in all web communication channels. There is a developing explosion in this socially disruptive mode of web-based information. Increasingly, we have seen various countries developing advanced methods to spread their targeted disinformation. To address this flood of disinformation will require refined strategies to capture and evaluate the messages. In this paper, we present both a quantitative and a qualitative analysis of online social and information networks to better evaluate the characteristics of the disinformation campaigns. We focus on the case of Russian-generated disinformation, which has been developed to an elevated level. We demonstrate an effective approach based on a new dataset to study the Russian campaign composed of 14497 cases of dis-information and the corresponding counter-dis-information. Although this case is of high current relevance, there is very limited published evaluation. We provide a novel analysis and present a methodology to characterize this disinformation. We based our investigation on a Spherical k-means algorithm to determine the main topics of the disinformation and to discover the key trends. We employ distilBERT algorithm and achieve a high accuracy F1-score of 98.8 demonstrating good quantitative capabilities. We propose the methodology as a template for further exploration and analysis.
A Method to Assess and Explain Disparate Impact in Online Retailing
Authors: Rafael Becerril-Arreola
Venue: WWW
Year: 2023

This paper presents a method for assessing whether algorithmic decision making induces disparate impact in online retailing. The proposed method specifies a statistical design, a sampling algorithm, and a technological setup for data collection through web crawling. The statistical design reduces the dimensionality of the problem and ensures that the data collected are representative, variation-rich, and suitable for the investigation of the causes behind any observed disparities. Implementations of the method can collect data on algorithmic decisions, such as price, recommendations, and delivery fees that can be matched to website visitor demographic data from established sources such as censuses and large scale surveys. The combined data can be used to investigate the presence and causes of disparate impact, potentially helping online retailers audit their algorithms without collecting or holding the demographic data of their users. The proposed method is illustrated in the context of the automated pricing decisions of a leading retailer in the United States. A custom-built platform implemented the method to collect data for nearly 20,000 different grocery products at more than 3,000 randomly-selected zip codes. The data collected indicates that prices are higher for locations with high proportions of minority households. Although these price disparities can be partly attributed to algorithmic biases, they are mainly explained by local factors and therefore can be regarded as business necessities.
The Hitchhiker's Guide to Facebook Web Tracking with Invisible Pixels and Click IDs
Authors: Paschalis Bekos, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas Kourtellis
Venue: WWW
Year: 2023

Over the past years, advertisement companies have used various tracking methods to persistently track users across the web. Such tracking methods usually include first and third-party cookies, cookie synchronization, as well as a variety of fingerprinting mechanisms. Facebook (FB) (now Meta) recently introduced a new tagging mechanism that attaches a one-time tag as a URL parameter (namely FBCLID) on outgoing links to other websites. Although such a tag does not seem to have enough information to persistently track users, we demonstrate that despite its ephemeral nature, when combined with FB Pixel, it can aid in persistently monitoring user browsing behavior across i) different websites, ii) different actions on each website, iii) time, i.e., both in the past as well as in the future. We refer to this online monitoring of users as FB web tracking. We find that FB Pixel tracks a wide range of user activities on websites with alarming detail, especially on websites classified as sensitive categories under GDPR. Also, we show how the FBCLID tag can be used to match, and thus de-anonymize, activities of online users performed in the distant past (even before those users had a FB account) tracked by FB Pixel. In fact, by combining this tag with cookies that have rolling expiration dates, FB can also keep track of users’ browsing activities in the future as well. Our experimental results suggest that 23% of the 10k most popular websites have adopted this technology, and can contribute to this activity tracking on the web. Furthermore, our longitudinal study shows that this type of user activity tracking can go as far back as 2015.). Simply said, if a user creates for the first time a FB account today, FB could, under some conditions, match their anonymously collected past web browsing activity to their newly created FB profile, from as far back as 2015 and continue tracking their activity in the future.
The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity
Authors: Jacopo de Berardinis, Albert Meroño-Peñuela, Andrea Poltronieri, Valentina Presutti
Venue: WWW
Year: 2023

Computationally creative systems for music have recently achieved impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals – allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions.
An Empirical Study of the Usage of Checksums for Web Downloads
Authors: Gaël Bernard, Rémi Coudert, Bertil Chapuis, Kévin Huguenin
Venue: WWW
Year: 2023

Checksums, typically provided on webpages and generated from cryptographic hash functions (e.g., MD5, SHA256) or signature schemes (e.g., PGP), are commonly used on websites to enable users to verify that the files they download have not been tampered with when stored on possibly untrusted servers. In this paper, we elucidate the current practices regarding the usage of checksums for web downloads (hash functions used, visibility and validity of checksums, type of websites and files, etc.), as this has been mostly overlooked so far. Using a snowball-sampling strategy for the 200000 most popular domains of the Web, we first crawled a dataset of 8.5M webpages, from which we built, through an active-learning approach, a unique dataset of 277 diverse webpages that contain checksums. Our analysis of these webpages reveals interesting findings about the usage of checksums. For instance, it shows that checksums are used mostly to verify program files, that weak hash functions are frequently used, and that a non-negligible proportion of the checksums provided on webpages do not match that of their associated files. Finally, we complement our analysis with a survey of the webmasters of the considered webpages (N = 26), thus shedding light on the reasons behind the checksum-related choices they make.
CAM: A Large Language Model-based Creative Analogy Mining Framework
Authors: Bhavya, Jinjun Xiong, Chengxiang Zhai
Venue: WWW
Year: 2023

Analogies inspire creative solutions to problems, and facilitate the creative expression of ideas and the explanation of complex concepts. They have widespread applications in scientific innovation, creative writing, and education. The ability to discover creative analogies that are not explicitly mentioned but can be inferred from the web is highly desirable to power all such applications dynamically and augment human creativity. Recently, Large Pre-trained Language Models (PLMs), trained on massive Web data, have shown great promise in generating mostly known analogies that are explicitly mentioned on the Web. However, it is unclear how they could be leveraged for mining creative analogies not explicitly mentioned on the Web. We address this challenge and propose Creative Analogy Mining (CAM), a novel framework for mining creative analogies, which consists of the following three main steps: 1) Generate analogies using PLMs with effectively designed prompts, 2) Evaluate their quality using scoring functions, and 3) Refine the low-quality analogies by another round of prompt-based generation. We propose both unsupervised and supervised instantiations of the framework so that it can be used even without any annotated data. Based on human evaluation using Amazon Mechanical Turk, we find that our unsupervised framework can mine 13.7% highly-creative and 56.37% somewhat-creative analogies. Moreover, our supervised scores are generally better than the unsupervised ones and correlate moderately with human evaluators, indicating that they would be even more effective at mining creative analogies. These findings also shed light on the creativity of PLMs 1.
Demystifying Mobile Extended Reality in Web Browsers: How Far Can We Go?
Authors: Weichen Bi, Yun Ma, Deyu Tian, Qi Yang, Mingtao Zhang, Xiang Jing
Venue: WWW
Year: 2023

Mobile extended reality (XR) has developed rapidly in recent years. Compared with the app-based XR, XR in web browsers has the advantages of being lightweight and cross-platform, providing users with a pervasive experience. Therefore, many frameworks are emerging to support the development of XR in web browsers. However, little has been known about how well these frameworks perform and how complex XR apps modern web browsers can support on mobile devices. To fill the knowledge gap, in this paper, we conduct an empirical study of mobile XR in web browsers. We select seven most popular web-based XR frameworks and investigate their runtime performance, including 3D rendering, camera capturing, and real-world understanding. We find that current frameworks have the potential to further enhance their performance by increasing GPU utilization or improving computing parallelism. Besides, for 3D scenes with good rendering performance, developers can feel free to add camera capturing with little influence on performance to support augmented reality (AR) and mixed reality (MR) applications. Based on our findings, we draw several practical implications to provide better XR support in web browsers.
Interleaved Online Testing in Large-Scale Systems
Authors: Nan Bi, Bai Li, Ruoyuan Gao, Graham Edge, Sachin Ahuja
Venue: WWW
Year: 2023

Online testing is indispensable in decision making for information retrieval systems. Interleaving emerges as an online testing method with orders of magnitude higher sensitivity than the pervading A/B testing. It merges the compared results into a single interleaved result to show to users, and attributes user actions back to the systems being tested. However, its pairwise design also brings practical challenges to real-world systems, in terms of effectively comparing multiple (more than two) systems and interpreting the magnitude of raw interleaving measurement. We present two novel methods to address these challenges that make interleaving practically applicable. The first method infers the ordering of multiple systems based on interleaving pairwise results with false discovery control. The second method estimates A/B effect size based on interleaving results using a weighted linear model that adjust for uncertainties of different measurements. We showcase the effectiveness of our methods in large-scale e-commerce experiments, reporting as many as 75 interleaving results, and provide extensive evaluations of their underlying assumptions.
Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network
Authors: Wendong Bi, Bingbing Xu, Xiaoqian Sun, Easton Li Xu, Huawei Shen, Xueqi Cheng
Venue: WWW
Year: 2023

Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomplete features and rare labels, e.g., the description and political tendency of politicians (vocal) are abundant while not for ordinary civilians (silent) on the twitter’s social network. Predicting the silent majority remains a crucial yet challenging problem. However, most existing Graph Neural Networks (GNNs) assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KTGNN), which models distribution-shifts during message passing and learns representation by transferring knowledge from vocal nodes to silent nodes. Specifically, we design the domain-adapted "feature completion and message passing mechanism" for node representation learning while preserving domain difference. And a knowledge transferable classifier based on KL-divergence is followed. Comprehensive experiments on real-world scenarios (i.e., company financial risk assessment and political elections) demonstrate the superior performance of our method. Our source code has been open-sourced1.
Semi-supervised Adversarial Learning for Complementary Item Recommendation
Authors: Koby Bibas, Oren Sar Shalom, Dietmar Jannach
Venue: WWW
Year: 2023

Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.
Why are Hyperlinks Blue?: A deep dive into browser hyperlink color history
Authors: Elise Blanchard
Venue: WWW
Year: 2023

The internet has ingrained itself into every aspect of our lives, but there's one aspect of the digital world that some take for granted. Did you ever notice that many links, specifically hyperlinks, are blue? When a coworker casually asked me why links are blue, I was stumped. As a user experience designer who has created websites since 2001, I've always made my links blue. I have advocated for the specific shade of blue, and for the consistent application of blue, yes, but I've never stopped and wondered, why are links blue? It was just a fact of life. Grass is green and hyperlinks are blue. Culturally, we associate links with the color blue so much that in 2016, when Google changed its links to black, it created quite a disruption [1]. But now, I find myself all consumed by the question, WHY are links blue? WHO decided to make them blue? WHEN was this decision made, and HOW has this decision made such a lasting impact? Mosaic, an early browser released by Marc Andreessen and Eric Bina on January 23, 1993 [2], had blue hyperlinks. To truly understand the origin and evolution of hyperlinks, I took a journey through technology history and interfaces to explore how links were handled before color monitors, and how interfaces and hyperlinks rapidly evolved once color monitors became an option.
The First International Workshop on Knowledge Graphs for Sustainability - KG4S Foreword
Authors: Eva Blomqvist, María Poveda-Villalón, Raúl García-Castro, Pascal Hitzler, Mikael Lindecrantz
Venue: WWW
Year: 2023

ACM Reference Format: Eva Blomqvist, María Poveda-Villalón, Raúl García-Castro, Pascal Hitzler, and Mikael Lindecrantz. 2023. The First International Workshop on Knowledge Graphs for Sustainability – KG4S Foreword. In Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), April 30–May 04, 2023, Austin, TX, USA. ACM, New York, NY, USA, 1 page. https://doi.org/10.1145/3543873.3589745
I'm out of breath from laughing! I think? A dataset of COVID-19 Humor and its toxic variants
Authors: Neha Reddy Bogireddy, Smriti Suresh, Sunny Rai
Venue: WWW
Year: 2023

Humor is a cognitive construct that predominantly evokes the feeling of mirth. During the COVID-19 pandemic, the situations that arouse out of the pandemic were so incongruous to the world we knew that even factual statements often had a humorous reaction. In this paper, we present a dataset of 2510 samples hand-annotated with labels such as humor style, type, theme, target and stereotypes formed or exploited while creating the humor in addition to 909 memes. Our dataset comprises Reddit posts, comments, Onion news headlines, real news headlines, and tweets. We evaluate the task of humor detection and maladaptive humor detection on state-of-the-art models namely RoBERTa and GPT-3. The finetuned models trained on our dataset show significant gains over zero-shot models including GPT-3 when detecting humor. Even though GPT-3 is good at generating meaningful explanations, we observed that it fails to detect maladaptive humor due to the absence of overt targets and profanities. We believe that the presented dataset will be helpful in designing computational methods for topical humor processing as it provides a unique sample set to study the theory of incongruity in a post-pandemic world. The data is available to research community at https://github.com/smritae01/Covid19_Humor.
Blend and Match: Distilling Semantic Search Models with Different Inductive Biases and Model Architectures
Authors: Hamed R. Bonab, Ashutosh Joshi, Ravi Bhatia, Ankit Gandhi, Vijay Huddar, Juhi Naik, Mutasem Al-Darabsah, Choon Hui Teo, Jonathan May, Tarun Agarwal, Vaclav Petricek
Venue: WWW
Year: 2023

Commercial search engines use different semantic models to augment lexical matches. These models provide candidate items for a user’s query from a target space of millions to billions of items. Models with different inductive biases provide relatively different predictions, making it desirable to launch multiple semantic models in production. However, latency and resource constraints make simultaneously deploying multiple models impractical. In this paper, we introduce a distillation approach, called Blend and Match (BM), to unify two different semantic search models into a single model. We use a Bi-encoder semantic matching model as our primary model and propose a novel loss function to incorporate eXtreme Multi-label Classification (XMC) predictions as the secondary model. Our experiments conducted on two large-scale datasets, collected from a popular e-commerce store, show that our proposed approach significantly improves the recall of the primary Bi-encoder model by 11% to 17% with a minimal loss in precision. We show that traditional knowledge distillation approaches result in a sub-optimal performance for our problem setting, and our BM approach yields comparable rankings with strong Rank Fusion (RF) methods used only if one could deploy multiple models.