Reliable relevance modeling and ranking are pivotal to legal information retrieval (IR), where graded relevance judgments are indispensable yet prohibitively expensive to acquire. Despite their importance, expert-validated graded relevance benchmarks remain scarce, especially for non-English statutory-law jurisdictions where citation practices differ fundamentally from common-law paradigms. We introduce KCC, a publicly available and reusable benchmark resource for legal IR in Korean civil law. KCC is constructed from 38,372 publicly accessible court decisions and comprises 2,942 query–candidate case pairs annotated with expert-validated four-level relevance labels. These labels jointly capture factual similarity and legal reasoning, explicitly addressing the absence of systematic citation links in statutory-law systems. To facilitate reproducibility, we provide a detailed description of the dataset construction pipeline, annotation protocol, relevance criteria, and inter-annotator agreement statistics validated by legal professionals. The annotation guidelines are designed to be adaptable to other statutory-law and non-English legal IR settings, enabling future extensions beyond the current dataset. We further present a set of reference benchmark experiments using representative retrieval approaches, including traditional IR models, neural rankers, and LLM-based prompting methods, to indicate recommended usage and evaluation practices with KCC. We believe that KCC provides a long-term resource for the research community to study relevance modeling, ranking behavior, and evaluation methodology in domain-specific legal IR scenarios beyond English and common-law traditions.

Significant efforts have been made to analyze the political stance or bias in news articles, especially as political polarization intensifies over the years. Recent advancements in machine learning have enabled researchers to develop various bias prediction models, which typically learn features not only from the text of the news articles but also from external knowledge. However, when training these models, the political bias label assigned to a news article is often based solely on the news source which published it. This approach can be problematic, as a news outlet with a particular political stance might publish an article that reflects a different political perspective. To address this issue, we first identify distinct text patterns associated with specific news sources or publishers, that are minimally relevant to predicting the political bias of a news article. We then conduct comprehensive experiments to investigate (i) whether existing models trained to predict political bias can also accurately predict the source, and (ii) whether these models change their predictions when a distinct pattern from a source with a different political stance is incorporated into a news article. Our experimental results reveal that all existing models tend to predict the source, even when trained solely to predict bias. Based on these findings, we propose a new deep learning model for political bias prediction that avoids learning source-indicative patterns specific to a given news source.

Modern multi-object tracking (MOT) predominantly relies on the tracking-by-detection paradigm to construct object trajectories. Traditional MOT attacks primarily degrade detection quality in specific frames only, lacking efficiency, while state-of-the-art (SOTA) approaches induce persistent identity (ID) switches by manipulating object positions during the association phase, even after the attack ends. In this paper, we reveal that these SOTA attacks can be easily counteracted by adjusting distance-related parameters in the association phase, exposing their lack of robustness. To overcome these limitations, we propose BankTweak, a novel adversarial attack targeting feature-based MOT systems to induce persistent ID switches (efficiency) without modifying object positions (robustness). BankTweak exploits a critical vulnerability in the Hungarian matching algorithm of MOT systems by strategically injecting altered features into feature banks during the association phase. Extensive experiments on MOT17 and MOT20 datasets, combining various detectors, feature extractors, and trackers, demonstrate that BankTweak significantly outperforms SOTA attacks up to 11.8 times, exposing fundamental vulnerabilities in the tracking-by-detection framework.

The rapid development and prevalence of generative AI have made it easy for people to create high-quality deepfake images and videos, but their abuses have also increased exponentially. To mitigate potential social disruption, it is crucial to quickly detect the authenticity of each deepfake content hidden in a sea of information. While researchers have worked on developing deep learning-based methods, the deepfake datasets utilized in these studies are far from the real world in terms of their qualities; most popular deepfake datasets are human-distinguishable. To address this problem, we present a novel deepfake dataset, HiDF, a high-quality and human-indistinguishable deepfake dataset consisting of 62K images and 8K videos. HiDF is a meticulously curated dataset that includes diverse subjects that have undergone rigorous quality checks. A comparison of the quality between HiDF and existing deepfake datasets demonstrates that HiDF is human-indistinguishable. Hence, it can be a valuable benchmark dataset for deepfake detection tasks. Data and code (https://github.com/DSAIL-SKKU/HiDF) are publicly available for future deepfake detection research.

As the explainability of mental disorder detection models has become important, symptombased methods that predict disorders from identified symptoms have been widely utilized.However, since these approaches focused on the presence of symptoms, the context of symptoms can be often ignored, leading to missing important contextual information related to detecting mental disorders.Furthermore, the result of disorder detection can be vulnerable to errors that may occur in identifying symptoms.To address these issues, we propose a novel framework that detects mental disorders by leveraging symptoms and their context while mitigating potential errors in symptom identification.In this way, we propose to use large language models to effectively extract contextual information and introduce an uncertainty-aware decision fusion network that combines predictions of multiple models based on quantified uncertainty values.To evaluate the proposed method, we constructed a new Korean mental health dataset annotated by experts, named Ko-MOS.Experimental results demonstrate that the proposed model accurately detects mental disorders even in situations where symptom information is incomplete.

The increasing utilization of newly emerging networks (e.g., private-5G) across industries underscores the need for accurate traffic prediction to manage network resources effectively. However, rapidly emerging networks face challenges in accurate prediction due to limited training data at the early stage and fluctuation in traffic load at the maintenance stage. In response, we propose ISOML (Inter-Service Online Meta-Learning), a novel traffic prediction pipeline designed for newly emerging networks. ISOML utilizes meta-learning to address data scarcity and employs the EWC (Elastic Weight Consolidation) for online learning to learn dynamics of traffic patterns. Experimental validation in real-world datasets demonstrates the efficacy of ISOML in predicting traffic for emerging network environments.

Social support or peer support in mental health has successfully settled down in online spaces by reducing the potential risk of critical mental illness (e.g., suicidal thoughts) of support-seekers. While the prior work has mostly focused on support-seekers, particularly investigating their behavioral characteristics and the effects of online social supports to support-seekers, this paper seeks to understand online social support from supporters' perspectives, who have informational or emotional resources that may affect support-seekers either positively or negatively. To this end, we collect and analyze a large-scale of dataset consisting of the supporting comments and their target posts from 55 mental health communities in Reddit. We also develop a deep-learning-based model that scores informational and emotional support to the supporting comments. Based on the collected and scored dataset, we measure the characteristics of the supporters from the behavioral and content perspectives, which reveals that the supporters tend to give emotional support than informational support and the atmosphere of social support communities tend also to be emotional. We also understand the relations between the supporters and the support-seekers by giving a notion of "social supporting network'', whose nodes and edges are the sets of the users and the supporting comments. Our analysis on top users by out-degrees and in-degrees in social supporting network demonstrates that heavily-supportive users are more likely to give informational support with diverse content while the users who attract much support exhibit continuous support-seeking behaviors by uploading multiple posts with similar content. Lastly, we identified structural communities in social supporting network to explore whether and how the supporters and the support-seeking users are grouped. By conducting topic analysis on both the support-seeking posts and the supporting comments of individual communities, we revealed that small communities deal with a specific topic such as hair-pulling disorder. We believe that the methodologies, dataset, and findings can not only expose more research questions on online social supports in mental health, but also provide insight on improving social support in online platforms.

Understanding socio-economic systems in MMORPGs can provide an important implication on how people participate in the economy and how people interact with each other. In this paper, we model the socio-economic system of an Aion, a popular MMORPG, as a multi-layer graph. Using the dataset consisting of 94,870 users and their activity records spanning three months, we examine how economic activities are associated with social interactions, and find that social interactions like participating in a party or exchanging messages are highly correlated with the trade activities. We also find that virtual economy in Aion is heavily inclined to a small number of upper-class userswho play a crucial role in virtual economy. Our analysis on the upper-class users reveals that a significant portion of them reach at the max-level and tend to either (i) have many social interactions with others or (ii) play extremely much time with no social activity. We also reveal that there are some low-level upper-class users who gain much money but hardly socialize with others. Lastly, we show how upper-class users who are at low-levels, play the game extremely much more than others, or rarely interact with other users, are associated with the Real Money Trade (RMT), which may be an illegal behavior that gathers in-game money for exchanging into real-world money. We reveal that more than half of total money exchanged through the trade are associated with the upper-class users who involve in the RMT.

Event-based social networks (EBSNs) are increasingly popular since they provide platforms on which online and offline activities are combined. Despite the increasing interest in EBSNs, little research has paid attention to the privacy issues coming from the unique features of EBSNs; the on-site information of users is highly relevant to real lives. In this paper, we try to investigate privacy leakages in Meetup, one of the most popular EBSN service. More specifically, we answer what private information can be inferred from the site's publicly available data. To this end, we conduct a measurement study by crawling webpages from Meetup containing 240K groups, 8.9M users, 27M group affiliations and 78M topical interests. By analyzing the dataset, we find that LGBT status of users, which is one of the most sensitive privacy information, can be predicted with 93% accuracy. Finally we discuss the cause of the privacy leakage on EBSNs and its possible ensuing damages.

Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their pins in Pinterest? How do the characteristics of users (e.g., gender, popularity, country) or properties of pins (e.g., category, topic) play roles in propagating pins in Pinterest? To answer these questions, we have conducted a measurement study on patterns of pin curating and sharing in Pinterest. By keeping track of all the newly posted and shared pins in each category (e.g., animal, kids, women's fashion) from June 5 to July 18, 2013, we built 350 K pin propagation trees for 3 M users. With the dataset, we investigate: (1) how users collect and curate pins, (2) how users share their pins and why, and (3) how users are related by shared pins of interest. Our key finding is that pin propagation in Pinterest is mostly driven by pin's properties like its topic, not by user's characteristics like her number of followers. We further show that users in the same community in the interest graph (i.e., representing the relations among users) of Pinterest share pins (i) in the same category with 94% probability and (ii) of the same URL where pins come from with 89% probability. Finally, we explore the implications of our findings for predicting how pins are shared in Pinterest.

Understanding the group characteristics in MMORPGs is important in user behavior studies since people tend to gather together and form groups due to their inherent nature. In this paper, we analyze the group activities of users in Aion, one of the largest MMORPGs, based on the records of the activities of 94,497 users. In particular, we focus on (i) how social interactions within a group differ from the ones across groups, (ii) what makes a group rise, sustain, or fall, (iii) how group members join and leave a group, and (iv) what makes a group end. We first find that structural patterns of social interactions within a group are more likely to be close-knit and reciprocative than the ones across groups. We also observe that members in a rising group (i.e., the number of members increases) are more cohesive, and communicate with more evenly within the group than the ones in other groups. Our analysis further reveals that if a group is not cohesive, not actively communicating, or not evenly communicating among members, members of the group tend to leave.