OpenRaiser/Intern-Atlas huggingface dataset:
Scan Me If You Can: Understanding and Detecting Unwanted Vulnerability Scanning
Authors: Xigao Li, Babak Amin Azad, Amir Rahmati, Nick Nikiforakis
Venue: WWW
Year: 2023

Web vulnerability scanners (WVS) are an indispensable tool for penetration testers and developers of web applications, allowing them to identify and fix low-hanging vulnerabilities before they are discovered by attackers. Unfortunately, malicious actors leverage the very same tools to identify and exploit vulnerabilities in third-party websites. Existing research in the WVS space is largely concerned with how many vulnerabilities these tools can discover, as opposed to trying to identify the tools themselves when they are used illicitly. In this work, we design a testbed to characterize web vulnerability scanners using browser-based and network-based fingerprinting techniques. We conduct a measurement study over 12 web vulnerability scanners as well as 159 users who were recruited to interact with the same web applications that were targeted by the evaluated WVSs. By contrasting the traffic and behavior of these two groups, we discover tool-specific and type-specific behaviors in WVSs that are absent from regular users. Based on these observations, we design and build ScannerScope, a machine-learning-based, web vulnerability scanner detection system. ScannerScope consists of a transparent reverse proxy that injects fingerprinting modules on the fly without the assistance (or knowledge) of the protected web applications. Our evaluation results show that ScannerScope can effectively detect WVSs and protect web applications against unwanted vulnerability scanning, with a detection accuracy of over 99% combined with near-zero false positives on human-visitor traffic. Finally, we show that the asynchronous design of ScannerScope results in a negligible impact on server performance and demonstrate that its classifier can resist adversarial ML attacks launched by sophisticated adversaries.
Forecasting COVID-19 Vaccination Rates using Social Media Data
Authors: Xintian Li, Aron Culotta
Venue: WWW
Year: 2023

The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user’s intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance.
Online Reviews Are Leading Indicators of Changes in K-12 School Attributes
Authors: Linsen Li, Aron Culotta, Douglas N. Harris, Nicholas Mattei
Venue: WWW
Year: 2023

School rating websites are increasingly used by parents to assess the quality and fit of U.S. K-12 schools for their children. These online reviews often contain detailed descriptions of a school’s strengths and weaknesses, which both reflect and inform perceptions of a school. Existing work on these text reviews has focused on finding words or themes that underlie these perceptions, but has stopped short of using the textual reviews as leading indicators of school performance. In this paper, we investigate to what extent the language used in online reviews of a school is predictive of changes in the attributes of that school, such as its socio-economic makeup and student test scores. Using over 300K reviews of 70K U.S. schools from a popular ratings website, we apply language processing models to predict whether schools will significantly increase or decrease in an attribute of interest over a future time horizon. We find that using the text improves predictive performance significantly over a baseline model that does not include text but only the historical time-series of the indicators themselves, suggesting that the review text carries predictive power. A qualitative analysis of the most predictive terms and phrases used in the text reviews indicates a number of topics that serve as leading indicators, such as diversity, changes in school leadership, a focus on testing, and school safety.
Knowledge Distillation on Cross-Modal Adversarial Reprogramming for Data-Limited Attribute Inference
Authors: Quan Li, Lingwei Chen, Shixiong Jing, Dinghao Wu
Venue: WWW
Year: 2023

Social media generates a rich source of text data with intrinsic user attributes (e.g., age, gender), where different parties benefit from disclosing them. Attribute inference can be cast as a text classification problem, which, however, suffers from labeled data scarcity. To address this challenge, we propose a data-limited learning model to distill knowledge on adversarial reprogramming of a visual transformer (ViT) for attribute inferences. Not only does this novel cross-modal model transfers the powerful learning capability from ViT, but also leverages unlabeled texts to reduce the demand on labeled data. Experiments on social media datasets demonstrate the state-of-the-art performance of our model on data-limited attribute inferences.
TRAVERS: A Diversity-Based Dynamic Approach to Iterative Relevance Search over Knowledge Graphs
Authors: Ziyang Li, Yu Gu, Yulin Shen, Wei Hu, Gong Cheng
Venue: WWW
Year: 2023

Relevance search over knowledge graphs seeks top-ranked answer entities that are most relevant to a query entity. Since the semantics of relevance varies with the user need and its formalization is difficult for non-experts, existing methods infer semantics from user-provided example answer entities. However, a user may provide very few examples, even none at the beginning of interaction, thereby limiting the effectiveness of such methods. In this paper, we vision a more practical scenario called labeling-based iterative relevance search: instead of effortfully inputting example answer entities, the user effortlessly (e.g., implicitly) labels current answer entities, and is rewarded with improved answer entities in the next iteration. To realize the scenario, our approach TRAVERS incorporates two rankers: a diversity-oriented ranker for supporting cold start and avoiding converging to sub-optimum caused by noisy labels, and a relevance-oriented ranker capable of handling unbalanced labels. Moreover, the two rankers and their combination dynamically evolve over iterations. TRAVERS outperformed a variety of baselines in experiments with simulated and real user behavior.
SocialNLP'23: 11th International Workshop on Natural Language Processing for Social Media
Authors: Cheng-Te Li, Lun-Wei Ku
Venue: WWW
Year: 2023

SocialNLP is an inter-disciplinary area of natural language processing (NLP) and social computing. SocialNLP has three directions: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social networks or social media; and (3) handling new problems related to both social computing and natural language processing. The 11th SocialNLP workshop is held at TheWebConf 2023. We accepted nine papers with acceptance ratio 56%. We sincerely thank to all authors, program committee members, and workshop chairs, for their great contributions and help in this edition of SocialNLP workshop.
Multi-interest Recommendation on Shopping for Others
Authors: Shuang Li, Yaokun Liu, Xiaowang Zhang, Yuexian Hou, Zhiyong Feng
Venue: WWW
Year: 2023

Existing recommendation methods based on multi-interest frameworks effectively model users from multiple aspects to represent complex user interests. However, more research still needs to be done on the behavior of users shopping for others. We propose a Multi-Demander Recommendation (MDR) model to learn different people’s interests from a sequence of actions. We first decouple the feature embeddings of items to learn the static preferences of different demanders. Next, a weighted directed global graph is constructed to model the associations among item categories. We partition short sequences by time intervals and look up category embeddings from the graph to capture dynamic intents. Finally, preferences and intentions are combined with learning the interests of different demanders. The conducted experiments demonstrate that our model improves the accuracy of recommendations.
A Cross-Media Retrieval System for Web-SNS-Map Using Suggested Keywords Generating and Ranking Method Based on Search Characteristics
Authors: Da Li, Masaki Sugihashi, Tadahiko Kumamoto, Yukiko Kawai
Venue: WWW
Year: 2023

The research on multimedia retrieval has lasted for several decades. However, past efforts generally focused on single-media retrieval, where the queries and retrieval results belong to the same media (platform) type, such as social media platforms or search engines. In single-media retrieval, users have to select search media or options based on search characteristics such as contents, time, or spatial distance, they might be unable to retrieve correct results mixed in other media if they carelessly forget to select. In this study, we propose a cross-media retrieval system using suggestion generation methods to integrate three search characteristics of the Web (textual content-based retrieval), SNS (timeliness), and map (spatial distance-aware retrieval). In our previous research, we attempted to improve search efficiency using clustering methods to provide search results to users through related terms, etc. In this paper, we focus on the search efficiency of multiple search media. We utilize Google search engine to obtain the retrieval content from the Web, Twitter to obtain timely information from SNSs, and Google Maps to get geographical information from maps. We apply the obtained retrieval results to analyze the similarities between them by clustering. Then, we generate relevant suggestions and provide them to users. Moreover, we validate the effectiveness of the search results generated by our proposed system.
Communicative MARL-based Relevance Discerning Network for Repetition-Aware Recommendation
Authors: Kaiyuan Li, Pengfei Wang, Haitao Wang, Qiang Liu, Xingxing Wang, Dong Wang, Shangguang Wang
Venue: WWW
Year: 2023

The repeated user-item interaction now is becoming a common phenomenon in the e-commerce scenario. Due to its potential economic profit, various models are emerging to predict which item will be re-interacted based on the user-item interactions. In this specific scenario, item relevance is a critical factor that needs to be concerned, which tends to have different effects on the succeeding re-interacted one (i.e., stimulating or delaying its emergence). It is necessary to make a detailed discernment of item relevance for a better repetition-aware recommendation. Unfortunately, existing works usually mixed all these types, which may disturb the learning process and result in poor performance. In this paper, we introduce a novel Communicative MARL-based Relevance Discerning Network (CARDfor short) to automatically discern the item relevance for a better repetition-aware recommendation. Specifically, CARDformalizes the item relevance discerning problem into a communication selection process in MARL. CARDtreats each unique interacted item as an agent and defines three different communication types over agents, which are stimulative, inhibitive, and noisy respectively. After this, CARDutilizes a Gumbel-enhanced classifier to distinguish the communication types among agents, and an attention-based Reactive Point Process is further designed to transmit the well-discerned stimulative and inhibitive incentives separately among all agents to make an effective collaboration for repetition decisions. Experimental results on two real-world e-commerce datasets show that our proposed method outperforms the state-of-the-art recommendation methods in terms of both sequential and repetition-aware recommenders. Furthermore, CARDis also deployed in the online sponsored search advertising system in Meituan, obtaining a performance improvement of over 1.5% and 1.2% in CTR and effective Cost Per Mille (eCPM) respectively, which is significant to the business.
An Analogical Reasoning Method Based on Multi-task Learning with Relational Clustering
Authors: Shuyi Li, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng
Venue: WWW
Year: 2023

Analogical QA task is a challenging natural language processing problem. When two word pairs are similar in their relationships, we refer to their relations as analogous. Although the analogy method based on word embedding is well developed, the analogy reasoning is far beyond this scope. At present, the methods based on pre-trained language models have explored only the tip of the iceberg. In this paper, we proposed a multi-task learning method for analogical QA task. First, we obtain word-pair representations by leveraging the output embeddings of the [MASK] token in the pre-trained language model. The representations are prepared for two tasks. The first task aims to train an analogical classifier by supervised learning. The second task is an auxiliary task based on relation clustering to generate relation pseudo-labels for word pairs and train relation classifier. Our method guides the model to analyze the relation similarity in analogical reasoning without relation labels. The experiments show that our method achieve excellent performance on four analogical reasoning datasets without the help of external corpus and knowledge. In the most difficult data set E-KAR, it has increased by at least 4%.
ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning
Authors: Yanan Li, Haitao Yuan, Zhe Fu, Xiao Ma, Mengwei Xu, Shangguang Wang
Venue: WWW
Year: 2023

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China’s largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost.
Impact of COVID-19 Pandemic on Cultural Products Interests
Authors: Ke Li, Zhiwen Yu, Ying Zhang, Bin Guo
Venue: WWW
Year: 2023

The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples’ interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users’ search logs to reflect humans’ underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples’ cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks.
TTS: A Target-based Teacher-Student Framework for Zero-Shot Stance Detection
Authors: Yingjie Li, Chenye Zhao, Cornelia Caragea
Venue: WWW
Year: 2023

The goal of zero-shot stance detection (ZSSD) is to identify the stance (in favor of, against, or neutral) of a text towards an unseen target in the inference stage. In this paper, we explore this problem from a novel angle by proposing a Target-based Teacher-Student learning (TTS) framework. Specifically, we first augment the training set by extracting diversified targets that are unseen during training with a keyphrase generation model. Then, we develop a teacher-student framework which effectively utilizes the augmented data. Extensive experiments show that our model significantly outperforms state-of-the-art ZSSD baselines on the available benchmark dataset for this task by 8.9% in macro-averaged F1. In addition, previous ZSSD requires human-annotated targets and labels during training, which may not be available in real-world applications. Therefore, we go one step further by proposing a more challenging open-world ZSSD task: identifying the stance of a text towards an unseen target without human-annotated targets and stance labels. We show that our TTS can be easily adapted to the new task. Remarkably, TTS without human-annotated targets and stance labels even significantly outperforms previous state-of-the-art ZSSD baselines trained with human-annotated data. We publicly release our code 1 to facilitate future research.
CL-WSTC: Continual Learning for Weakly Supervised Text Classification on the Internet
Authors: Miaomiao Li, Jiaqi Zhu, Xin Yang, Yi Yang, Qiang Gao, Hongan Wang
Venue: WWW
Year: 2023

Continual text classification is an important research direction in Web mining. Existing works are limited to supervised approaches relying on abundant labeled data, but in the open and dynamic environment of Internet, involving constant semantic change of known topics and the appearance of unknown topics, text annotations are hard to access in time for each period. That calls for the technique of weakly supervised text classification (WSTC), which requires just seed words for each category and has succeed in static text classification tasks. However, there are still no studies of applying WSTC methods in a continual learning paradigm to actually accommodate the open and evolving Internet. In this paper, we tackle this problem for the first time and propose a framework, named Continual Learning for Weakly Supervised Text Classification (CL-WSTC), which can take any WSTC method as base model. It consists of two modules, classification decision with delay and seed word updating. In the former, the probability threshold for each category in each period is adaptively learned to determine the acceptance/rejection of texts. In the latter, with candidate words output by the base model, seed words are added and deleted via reinforcement learning with immediate rewards, according to an empirically certified unsupervised measure. Extensive experiments show that our approach has strong universality and can achieve a better trade-off between classification accuracy and decision timeliness compared to non-continual counterparts, with intuitively interpretable updating of seed words.
CNSVRE: A Query Reformulated Search System with Explainable Summarization for Virtual Research Environment
Authors: Na Li, Yangjun Zhang, Zhiming Zhao
Venue: WWW
Year: 2023

Computational notebook environments have drawn broad attention in data-centric research applications, e.g., virtual research environment, for exploratory data analysis and algorithm prototyping. Vanilla computational notebook search solutions have been proposed but they do not pay much attention to the information needs of scientific researchers. Previous studies either treat computational notebook search as a code search problem or focus on content-based computational notebook search. The queries being considered are neither research-concerning nor diversified whereas researchers’ information needs are highly specialized and complex. Moreover, relevance evaluation for computational notebooks is tricky and unreliable since computational notebooks contain fragments of text and code and are usually poorly organized. To solve the above challenges, we propose a computational notebook search system for virtual research environment (VRE), i.e., CNSVRE, with scientific query reformulation and computational notebook summarization. We conduct a user study to demonstrate the effectiveness, efficiency, and satisfaction with the system.
ZTLS: A DNS-based Approach to Zero Round Trip Delay in TLS handshake
Authors: Sangwon Lim, Hyeonmin Lee, Hyunsoo Kim, Hyunwoo Lee, Ted Taekyoung Kwon
Venue: WWW
Year: 2023

Establishing secure connections fast to end-users is crucial to online services. However, when a client sets up a TLS session with a server, the TLS handshake needs one round trip time (RTT) to negotiate a session key. Additionally, establishing a TLS session also requires a DNS lookup (e.g., the A record lookup to fetch the IP address of the server) and a TCP handshake. In this paper, we propose ZTLS to eliminate the 1-RTT latency for the TLS handshake by leveraging the DNS. In ZTLS, a server distributes TLS handshake-related data (i.e., Diffie-Hellman elements), dubbed Z-data, as DNS records. A ZTLS client can fetch Z-data by DNS lookups and derive a session key. With the session key, the client can send encrypted data along with its ClientHello, achieving 0-RTT. ZTLS supports incremental deployability on the current TLS-based infrastructure. Our prototype-based experiments show that ZTLS is 1-RTT faster than TLS in terms of the first response time.
Enhancing User Personalization in Conversational Recommenders
Authors: Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee
Venue: WWW
Year: 2023

Conversational recommenders are emerging as a powerful tool to personalize a user’s recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. Through extensive experiments on four frequently used datasets, we find the proposed framework not only outperforms all the state-of-the-art conversational recommenders (in terms of both recommendation performance and conversation efficiency), but also provides a more personalized experience for the user under the proposed multi-groundtruth multi-round conversational recommendation setting.