OpenRaiser/Intern-Atlas huggingface dataset:
Robust Preference-Guided Denoising for Graph based Social Recommendation
Authors: Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, Yong Li
Venue: WWW
Year: 2023

Graph Neural Network (GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations. However, in terms of both effectiveness and efficiency of recommendation, a large portion of social relations can be redundant or even noisy, e.g., it is quite normal that friends share no preference in a certain domain. Existing models do not fully solve this problem of relation redundancy and noise, as they directly characterize social influence over the full social network. In this paper, we instead propose to improve graph based social recommendation by only retaining the informative social relations to ensure an efficient and effective influence diffusion, i.e., graph denoising. Our designed denoising method is preference-guided to model social relation confidence and benefits user preference learning in return by providing a denoised but more informative social graph for recommendation models. Moreover, to avoid interference of noisy social relations, it designs a self-correcting curriculum learning module and an adaptive denoising strategy, both favoring highly-confident samples. Experimental results on three public datasets demonstrate its consistent capability of improving three state-of-the-art social recommendation models by robustly removing 10-40% of original relations. We release the source code at https://github.com/tsinghua-fib-lab/Graph-Denoising-SocialRec.
Coherent Topic Modeling for Creative Multimodal Data on Social Media
Authors: Junaid Rashid, Jungeun Kim, Usman Naseem
Venue: WWW
Year: 2023

The creative web is all about combining different types of media to create a unique and engaging online experience. Multimodal data, such as text and images, is a key component in the creative web. Social media posts that incorporate both text descriptions and images offer a wealth of information and context. Text in social media posts typically relates to one topic, while images often convey information about multiple topics due to the richness of visual content. Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. Our experimental results show that CTM-MM outperforms traditional multimodal topic models in terms of classification and topic coherence.
Incorporating Embedding to Topic Modeling for More Effective Short Text Analysis
Authors: Junaid Rashid, Jungeun Kim, Usman Naseem
Venue: WWW
Year: 2023

With the growing abundance of short text content on websites, analyzing and comprehending these short texts has become a crucial task. Topic modeling is a widely used technique for analyzing short text documents and uncovering the underlying topics. However, traditional topic models face difficulties in accurately extracting topics from short texts due to limited content and their sparse nature. To address these issues, we propose an Embedding-based topic modeling (EmTM) approach that incorporates word embedding and hierarchical clustering to identify significant topics. Experimental results demonstrate the effectiveness of EmTM on two datasets comprising web short texts, Snippet and News. The results indicate a superiority of EmTM over baseline topic models by its exceptional performance in both classification accuracy and topic coherence metrics.
Hierarchical Self-Attention Embedding for Temporal Knowledge Graph Completion
Authors: Xin Ren, Luyi Bai, Qianwen Xiao, Xiangxi Meng
Venue: WWW
Year: 2023

Temporal Knowledge Graph (TKG) is composed of a series of facts related to timestamps in the real world and has become the basis of many artificial intelligence applications. However, the existing TKG is usually incomplete. It has become a hot research task to infer missing facts based on existing facts in a TKG; namely, Temporal Knowledge Graph Completion (TKGC). The current mainstream TKGC models are embedded models that predict missing facts by representing entities, relations and timestamps as low-dimensional vectors. In order to deal with the TKG structure information, there are some models that try to introduce attention mechanism into the embedding process. But they only consider the structure information of entities or relations, and ignore the structure information of the whole TKG. Moreover, most of them usually treat timestamps as a general feature and cannot take advantage of the potential time series information of the timestamp. To solve these problems, wo propose a new Hierarchical Self-Attention Embedding (HSAE) model which inspired by self-attention mechanism and diachronic embedding technique. For structure information of the whole TKG, we divide the TKG into two layers: entity layer and relation layer, and then apply the self-attention mechanism to the entity layer and relation layer respectively to capture the structure information. For time series information of the timestamp, we capture them by combining positional encoding and diachronic embedding technique into the above two self-attention layers. Finally, we can get the embedded representation vectors of entities, relations and timestamps, which can be combined with other models for better results. We evaluate our model on three TKG datasets: ICEWS14, ICEWS05-15 and GDELT. Experimental results on the TKGC (interpolation) task demonstrate that our model achieves state-of-the-art results.
Towards High Resolution Urban Heat Analysis: Incorporating Thermal Drones to Enhance Satellite Based Urban Heatmaps
Authors: Bryan Rickens, Navid Hashemi Tonekaboni
Venue: WWW
Year: 2023

As remote-sensing becomes more actively utilized in the environmental sciences, our research continues the efforts in adapting smart cities by using civilian UAVs and drones for land surface temperature (LST) analysis. Given the increased spatial resolution that this technology provides as compared to standard satellite measurements, we sought to further study the urban heat island (UHI) effect – specifically when it comes to heterogeneous and dynamic landscapes such as the Charleston peninsula. Furthermore, we sought to develop a method to enhance the spatial resolution of publicly available LST temperature data (such as those measured from the Landsat satellites) by building a machine learning model utilizing remote-sensed data from drones. While we found a high correlation and an accurate degree of prediction for areas of open water and vegetation (respectively), our model struggled when it came to areas containing highly impervious surfaces. We believe, however, that these findings further illustrate the discrepancy between high and medium spatial resolutions, and demonstrate how urban environments specifically are prone to inaccurate LST measurements and are uniquely in need of an industry pursuit of higher spatial resolution for hyperlocal environmental sciences and urban analysis.
CLIME: Completeness-Constrained LIME
Authors: Claudia V. Roberts, Ehtsham Elahi, Ashok Chandrashekar
Venue: WWW
Year: 2023

We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set, where sparsity is defined by the amount of historical viewing data available to explain a movie recommendation for a particular data instance. We find that LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIME) that is superior to LIME and much faster than SHAP.
Human Dimensions of Animal Exploitation: Towards Understanding the International Wildlife Trade and Selfie-Tourism on Twitter
Authors: Sean P. Rogers, Jeremiah Onaolapo
Venue: WWW
Year: 2023

This study investigates statements of participation in an exploitative animal activity on social media website Twitter. The data include social posts (tweets) related to two exploited species - the sloth (N=32,119), and the elephant (N=15,160). Tweets for each of these case studies were examined and labeled. The initial results reveal several features of interaction with exploited species. Namely, there are a high number of tweets indicating that individuals participated in exploited species activities during vacations in destinations that double as native countries for the exploited species. The data also indicate that a large number of exploited species activities take place at fairs, carnivals, and circuses. These initial results shed light on the trends in human participation in activities with exploited species. These findings will offer insight to stakeholders seeking to bolster education programs and quantify the level of animal exploitation.
Automated WebAssembly Function Purpose Identification With Semantics-Aware Analysis
Authors: Alan Romano, Weihang Wang
Venue: WWW
Year: 2023

WebAssembly is a recent web standard built for better performance in web applications. The standard defines a binary code format to use as a compilation target for a variety of languages, such as C, C++, and Rust. The standard also defines a text representation for readability, although, WebAssembly modules are difficult to interpret by human readers, regardless of their experience level. This makes it difficult to understand and maintain any existing WebAssembly code. As a result, third-party WebAssembly modules need to be implicitly trusted by developers as verifying the functionality themselves may not be feasible. To this end, we construct WASPur, a tool to automatically identify the purposes of WebAssembly functions. To build this tool, we first construct an extensive collection of WebAssembly samples that represent the state of WebAssembly. Second, we analyze the dataset and identify the diverse use cases of the collected WebAssembly modules. We leverage the dataset of WebAssembly modules to construct semantics-aware intermediate representations (IR) of the functions in the modules. We encode the function IR for use in a machine learning classifier, and we find that this classifier can predict the similarity of a given function against known named functions with an accuracy rate of 88.07%. We hope our tool will enable inspection of optimized and minified WebAssembly modules that remove function names and most other semantic identifiers.
Augmenting Visualizations with Predictive and Investigative Insights to Facilitate Decision Making
Authors: Md Main Uddin Rony, Fan Du, Ryan A. Rossi, Jane Hoffswell, Niyati Chhaya, Iftikhar Ahamath Burhanuddin, Eunyee Koh
Venue: WWW
Year: 2023

Many people find it difficult to comprehend basic charts on the web, let alone make effective decisions from them. To address this gap, several ML models aim to automatically detect useful insights from charts and narrate them in a simpler textual format. However, most of these solutions can only detect basic factual insights (a.k.a. descriptive insights) that are already present in the chart, which may help with chart comprehension, but not decision-making. In this work, we study whether more advanced predictive and investigative insights can help users understand what will happen next and what actions they should take. These advanced insights can help decision-makers better understand the reasons behind anomaly events, predict future unfolding trends, and recommend possible actions for optimizing business outcomes. Through a study with 18 participants, we found that predictive and investigative insights lead to more insights recorded by users on average and better effectiveness ratings.
On Graph Time-Series Representations for Temporal Networks
Authors: Ryan A. Rossi, Nesreen K. Ahmed, Namyong Park
Venue: WWW
Year: 2023

Representations of temporal networks arising from a stream of edges lie at the heart of models learned on it and its performance on downstream applications. While previous work on dynamic modeling and embedding have focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale τ (e.g., 1 month), we introduce the notion of an ϵ -graph time-series that uses a fixed number of edges for each graph, and show its effectiveness in capturing fundamental structural graph statistics over time. The results indicate that the ϵ -graph time-series representation effectively captures the structural properties of the graphs across time whereas the commonly used τ -graph time-series representation captures the frequency of edges and temporal patterns with respect to their arrival in the application time. These results have many important implications especially on the design of new GNN-based models for temporal networks as well as for understanding existing models and their limitations.
Dynamic Embedding-based Retrieval for Personalized Item Recommendations at Instacart
Authors: Chuanwei Ruan, Allan Stewart, Han Li, Ryan Ye, David Vengerov, Haixun Wang
Venue: WWW
Year: 2023

Personalization is essential in e-commerce, with item recommendation as a critical task. In this paper, we describe a hybrid embedding-based retrieval system for real-time personalized item recommendations on Instacart. Our system addresses unique challenges in the multi-source retrieval system, and includes several key components to make it highly personalized and dynamic. Specifically, our system features a hybrid embedding model that includes a long-term user interests embedding model and a real-time session-based model, which are combined to capture users’ immediate intents and historical interactions. Additionally, we have developed a contextual bandit solution to dynamically adjust the number of candidates from each source and optimally allocate retrieval slots given a limited computational budget. Our modeling and system optimization efforts have enabled us to provide highly personalized item recommendations in real-time at scale to all our customers, including new and long-standing users.
Gender Pay Gap in Sports on a Fan-Request Celebrity Video Site
Authors: Nazanin Sabri, Stephen Reysen, Ingmar Weber
Venue: WWW
Year: 2023

The internet is often thought of as a democratizer, enabling equality in aspects such as pay, as well as a tool introducing novel communication and monetization opportunities. In this study we examine athletes on Cameo, a website that enables bi-directional fan-celebrity interactions, questioning whether the well-documented gender pay gaps in sports persist in this digital setting. Traditional studies into gender pay gaps in sports are mostly in a centralized setting where an organization decides the pay for the players, while Cameo facilitates grass-roots fan engagement where fans pay for video messages from their preferred athletes. The results showed that even on such a platform gender pay gaps persist, both in terms of cost-per-message, and in the number of requests, proxied by number of ratings. For instance, we find that female athletes have a median pay of 30$ per-video, while the same statistic is 40$ for men. The results also contribute to the study of parasocial relationships and personalized fan engagements over a distance. Something that has become more relevant during the ongoing COVID-19 pandemic, where in-person fan engagement has often been limited.
Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing
Authors: Danial Saef, Yuanrong Wang, Tomaso Aste
Venue: WWW
Year: 2023

The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), raises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead, we leverage recent advances in market regime (MR) clustering with the Implied Stochastic Volatility Model (ISVM) on a very recent dataset covering BTC options on the popular trading platform Deribit. Time-regime clustering is a temporal clustering method, that clusters the historic evolution of a market into different volatility periods accounting for non-stationarity. ISVM can incorporate investor expectations in each of the sentiment-driven periods by using implied volatility (IV) data. In this paper, we apply this integrated time-regime clustering and ISVM method (termed MR-ISVM) to high-frequency data on BTC options. We demonstrate that MR-ISVM contributes to overcome the burden of complex adaption to jumps in higher order characteristics of option pricing models. This allows us to price the market based on the expectations of its participants in an adaptive fashion and put the procedure to action on a new dataset covering previously unexplored DA dynamics.
Followers Tell Who an Influencer Is
Authors: Dheeman Saha, Md Rashidul Hasan, Abdullah Mueen
Venue: WWW
Year: 2023

Influencers are followed by a relatively smaller group of people on social media platforms under a common theme. Unlike the global celebrities, it is challenging to categorize influencers into general categories of fame (e.g., Politics, Religion, Entertainment, etc.) because of their overlapping and narrow reach to people interested in these categories. In this paper, we focus on categorizing influencers based on their followers. We exploit the top-1K Twitter celebrities to identify the common interest among the followers of an influencer as his/her category. We annotate the top one thousand celebrities in multiple categories of popularity, language, and locations. Such categorization is essential for targeted marketing, recommending experts, etc. We define a novel FollowerSimilarity between the set of followers of an influencer and a celebrity. We propose an inverted index to calculate similarity values efficiently. We exploit the similarity score in a K-Nearest Neighbor classifier and visualize the top celebrities over a neighborhood-embedded space.
3rd International Workshop on Scientific Knowledge Representation, Discovery, and Assessment (Sci-K 2023)
Authors: Angelo Antonio Salatino, Yi Bu, Ying Ding, Ágnes Horvát, Yong Huang, Meijun Liu, Paolo Manghi, Andrea Mannocci, Francesco Osborne, Daniel M. Romero, Dimitris Sacharidis, Misha Teplitskiy, Thanasis Vergoulis, Feng Xia, Yujia Zhai
Venue: WWW
Year: 2023

The International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2023) is now running its third edition. The Sci-K workshop is a venue that brings together researchers and practitioners from different disciplines (including, but not limited to, Digital Libraries, Information Extraction, Machine Learning, Semantic Web, Knowledge Engineering, Natural Language Processing, Scholarly Communication, Science of Science, Scientometrics and Bibliometrics), as well as professionals from the industry, to explore innovative solutions and ideas for the production and consumption of Scientific Knowledge Graphs and assessing the research impact. The workshop has called for high-quality submissions around the three main themes of research, related to scientific knowledge: representation, discovery, and assessment. In response to the call for papers, the workshop has received outstanding submissions from researchers in 15 different countries: United States of America, Germany, United Kingdom, Ireland, Sweden, Canada, India, Brazil, Australia, Italy, Slovenia, Bulgaria, Denmark, Ethiopia, and Norway. Each paper was reviewed at least by three members of the programme committee. Given the quality and the interesting topics covered by the submissions, we accepted 10 papers. Sci-K 2023 builds on two previous successful editions and keeps attracting a combined pool of attendees. The first edition (Sci-K 2021), was held on 13 April 2021 in conjunction with The Web Conference 2021. Its program consisted of two keynote talks, and the presentation of 11 research papers. The second edition (Sci-K 2022) took place on the 26 April 2022 at The Web Conference 2022. The program included the presentation of 5 long papers, 4 short papers, 2 vision papers, 2 keynote speeches and a panel on “What’s next after Microsoft Academic Graph?”. The full program as well as the list of accepted papers can be found on the Sci-K website: https://sci-k.github.io/2023/.
The Impact of Covid-19 on Online Discussions: the Case Study of the Sanctioned Suicide Forum
Authors: Elisa Sartori, Luca Pajola, Giovanni Da San Martino, Mauro Conti
Venue: WWW
Year: 2023

The COVID-19 pandemic has been at the center of the lives of many of us for at least a couple of years, during which periods of isolation and lockdowns were common. How all that affected our mental well-being, especially the ones’ who were already in distress? To investigate the matter we analyse the online discussions on Sanctioned Suicide, a forum where users discuss suicide-related topics freely. We collected discussions starting from March 2018 (before pandemic) up to July 2022, for a total of 53K threads with 700K comments and 16K users. We investigate the impact of COVID-19 on the discussions in the forum. The data show that covid, while being present in the discussions, especially during the first lockdown, has not been the main reason why new users registered to the forum. However, covid appears to be indirectly connected to other causes of distress for the users, i.e. anxiety for the economy.
Active Learning from the Web
Authors: Ryoma Sato
Venue: WWW
Year: 2023

Labeling data is one of the most costly processes in machine learning pipelines. Active learning is a standard approach to alleviating this problem. Pool-based active learning first builds a pool of unlabelled data and iteratively selects data to be labeled so that the total number of required labels is minimized, keeping the model performance high. Many effective criteria for choosing data from the pool have been proposed in the literature. However, how to build the pool is less explored. Specifically, most of the methods assume that a task-specific pool is given for free. In this paper, we advocate that such a task-specific pool is not always available and propose the use of a myriad of unlabelled data on the Web for the pool for which active learning is applied. As the pool is extremely large, it is likely that relevant data exist in the pool for many tasks, and we do not need to explicitly design and build the pool for each task. The challenge is that we cannot compute the acquisition scores of all data exhaustively due to the size of the pool. We propose an efficient method, Seafaring, to retrieve informative data in terms of active learning from the Web using a user-side information retrieval algorithm. In the experiments, we use the online Flickr environment as the pool for active learning. This pool contains more than ten billion images and is several orders of magnitude larger than the existing pools in the literature for active learning. We confirm that our method performs better than existing approaches of using a small unlabelled pool.