Psychiatric symptom identification on social media aims to infer fine-grained mental health symptoms from user-generated posts, allowing a detailed understanding of users' mental states. However, the construction of large-scale symptom-level datasets remains challenging due to the resource-intensive nature of expert labeling and the lack of standardized annotation guidelines, which in turn limits the generalizability of models to identify diverse symptom expressions from user-generated text. To address these issues, we propose SynSym, a synthetic data generation framework for constructing generalizable datasets for symptom identification. Leveraging large language models (LLMs), SynSym constructs high-quality training samples by (1) expanding each symptom into sub-concepts to enhance the diversity of generated expressions, (2) producing synthetic expressions that reflect psychiatric symptoms in diverse linguistic styles, and (3) composing realistic multi-symptom expressions, informed by clinical co-occurrence patterns. We validate SynSym on three benchmark datasets covering different styles of depressive symptom expression. Experimental results demonstrate that models trained solely on the synthetic data generated by SynSym perform comparably to those trained on real data, and benefit further from additional fine-tuning with real data. These findings underscore the potential of synthetic data as an alternative resource to real-world annotations in psychiatric symptom modeling, and SynSym serves as a practical framework for generating clinically relevant and realistic symptom expressions.

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.

The proliferation of highly realistic deepfake videos threatens public trust and the integrity of digital information. However, detecting sophisticated deepfakes requires analysis beyond surface-level visual artifacts. We propose Harmonizing Action Units with Temporal-contextual Embeddings (HAUTE), integrating physiological muscle dynamics with holistic semantic context through adaptive attention mechanisms. HAUTE captures temporal Action Unit coordination patterns and high-level contextual embeddings, enabling the model to reveal synthesis-induced inconsistencies imperceptible to isolated modalities. Extensive experiments demonstrate state-of-the-art performance with strong cross-dataset adaptability, particularly on commercial tool-based high-quality deepfakes, advancing trustworthy content verification for web ecosystems.

Sketch-based drawing assessments in art therapy are commonly used to understand the cognitive and psychological states of individuals. In conjunction with self-report measures, drawing assessments serve to enhance insights into an individual's psychological state. However, interpreting the drawing assessments is labor-intensive and substantially reliant on the experience of the art therapists. While a few automated approaches for analyzing drawing-based assessments have been proposed to remedy this issue, they mostly rely on existing object detection methods, where complex drawing attributes cannot be accurately decoded. To overcome these challenges, we propose a novel and comprehensive Draw-A-Person-in-the-Rain (DAPR) analysis system, CheckDAPR, which utilizes a Multimodal Large Language Model (MLLM) with object detection methods for in-depth evaluation. Our experimental results show the promising performance of CheckDAPR and its ability to reduce analysis time for art therapists, indicating its potential to aid professionals in art therapy.

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.

The utilization of automated depression detection significantly enhances early intervention for individuals experiencing depression. Despite numerous proposals on automated depression detection using recorded clinical interview videos, limited attention has been paid to considering the hierarchical structure of the interview questions. In clinical interviews for diagnosing depression, clinicians use a structured questionnaire that includes routine baseline questions and follow-up questions to assess the interviewee's condition. This paper introduces HiQuE (Hierarchical Question Embedding network), a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. HiQuE can effectively capture the importance of each question in diagnosing depression by learning mutual information across multiple modalities. We conduct extensive experiments on the widely-used clinical interview data, DAIC-WOZ, where our model outperforms other state-of-the-art multimodal depression detection models and emotion recognition models, showcasing its clinical utility in depression detection.

Sketch-based drawing assessments are useful in understanding individuals' cognitive and psychological states, such as cognitive impairment or mental disorders. Hence, these assessments have been developed and applied on a large scale, such as in schools and workplaces, to screen individuals who may require further clinical examination. However, the interpretation of a large number of drawing assessments solely relies on human experts, requiring much time and cost. To address this issue, we introduce a novel scene-level sketch dataset, SceneDAPR, which can be used to automatically analyze the drawing assessment, Draw-A-Person-in-the-Rain (DAPR), a popular psychological drawing assessment used for identifying stressful experiences and coping behavior. The proposed dataset consists of 6,420 objects depicted in 1,399 scene sketches drawn by humans, along with detailed supplementary information about the participants. SceneDAPR includes free-hand drawings from different age groups: children & adolescents, adults, and seniors. Leveraging the proposed SceneDAPR, we develop a web-based drawing assessment system. The extensive experiments demonstrate that our system shows a robust performance across the different age groups in the object detection task as well as a considerable performance compared to human experts. We believe that the proposed new sketch dataset can be used to develop an automatic system for psychological drawing assessments, which can support human experts by reducing the time and cost of analyzing the drawing assessments for a large population. SceneDAPR and experimental code are available at https://github.com/DSAIL-SKKU/SceneDAPR.

The emergence of hyper-realistic deepfake videos has raised significant concerns regarding their potential misuse. However, prior research on deepfake detection has primarily focused on image-based approaches, with little emphasis on video. With the advancement of generation techniques enabling intricate and dynamic manipulation of entire faces as well as specific facial components in a video sequence, capturing dynamic changes in both global and local facial features becomes crucial in detecting deepfake videos. This paper proposes a novel sequential attentive face embedding, SAFE, that can capture facial dynamics in a deepfake video. The proposed SAFE can effectively integrate global and local dynamics of facial features revealed in a video sequence using contrastive learning. Through a comprehensive comparison with the state-of-the-art methods on the DFDC (Deepfake Detection Challenge) dataset and the FaceForensic++ benchmark, we show that our model achieves the highest accuracy in detecting deepfake videos on both datasets.

As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers.

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression.

Knowledge management (KM) has emerged as an operational and strategic organisational tool. However, the influence of recent cyber regulations (CR) and security policies (SP) on the usefulness of KM, are absent from the literature. This study focuses on the intersection of knowledge management and knowledge fortification. The study employs survey data from a sample of firms and utilises partial least squares (PLS) with extensive post hoc analyses, to examine the influence of CR and SP on KM core processes (acquisition, sharing, and utilisation) and subsequently on the operational effectiveness and strategic performance of firms. Contrary to prior research, we found that external knowledge sharing negatively affected performance. These findings support our assertion that KM processes are tied to contingencies that change the relationship between KM outcomes.

The purpose of this study is to examine the effects of Confucian values, social fear and organizational knowledge sharing culture on the knowledge sharing intention of Information Systems (IS) professionals in an East Asian context. We surveyed 187 professionals working in IS in South Korean companies and applied partial least squares structural equation modeling for testing our hypotheses. The results show that Confucian values negatively influence knowledge sharing intention by increasing social fear. Conversely, knowledge sharing culture enhances knowledge sharing intention directly, and by mitigating the negative effects of Confucian values and social fear. Thus, while individuals’ reluctance to share knowledge has been conceptualized as a rational cost-benefit process in a Western context, our study shows that negative emotions and the underlying values, which stimulate these emotions, influence knowledge sharing intention in an East Asian context.

Influencer marketing has become a key marketing method for brands in recent years. Hence, brands have been increasingly utilizing influencers’ social networks to reach niche markets, and researchers have been studying various aspects of influencer marketing. However, brands have often suffered from searching and hiring the right influencers with specific interests/topics for their marketing due to a lack of available influencer data and/or limited capacity of marketing agencies. This paper proposes a multimodal deep learning model that uses text and image information from social media posts (i) to classify influencers into specific interests/topics (e.g., fashion, beauty) and (ii) to classify their posts into certain categories. We use the attention mechanism to select the posts that are more relevant to the topics of influencers, thereby generating useful influencer representations. We conduct experiments on the dataset crawled from Instagram, which is the most popular social media for influencer marketing. The experimental results show that our proposed model significantly outperforms existing user profiling methods by achieving 98% and 96% accuracy in classifying influencers and their posts, respectively. We release our influencer dataset of 33,935 influencers labeled with specific topics based on 10,180,500 posts to facilitate future research.

Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest. However, existing work mainly focuses on modeling popularity using a single metric such as the total number of likes or shares. In this work, we propose Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network. By combining user social features and image features, and encoding the diffusion path taken thus far with an explicit memory cell, our model predicts the diffusion path of an image more accurately compared to alternate baselines that either encode only image or social features, or lack memory. By mapping individual users to user prototypes, our model can generalize to new users not seen during training. Finally, we demonstrate our model»s capability of generating diffusion trees, and show that the generated trees closely resemble ground-truth trees.

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.

To achieve growth in the user base of online social networks--(OSN) based applications, word-of-mouth diffusion mechanisms, such as user-to-user invitations, are widely used. This article characterizes the adoption and cascading process of OSN-based applications that grow via user invitations. We analyze a detailed large-scale dataset of a popular Facebook gifting application, iHeart, that contains more than 2 billion entries of user activities generated by 190 million users during a span of 64 weeks. We investigate (1) how users invite their friends to an OSN-based application, (2) how application adoption of an individual user can be predicted, (3) what factors drive the cascading process of application adoptions, and (4) what are the good predictors of the ultimate cascade sizes. We find that sending or receiving a large number of invitations does not necessarily help to recruit new users to iHeart. We also find that the average success ratio of inviters is the most important feature in predicting an adoption of an individual user, which indicates that the effectiveness of inviters has strong predictive power with respect to application adoption. Based on the lessons learned from our analyses, we build and evaluate learning-based models to predict whether a user will adopt iHeart. Our proposed model that utilizes additional activity information of individual users from other similar types of gifting applications can achieve high precision (83%) in predicting adoptions in the target application (i.e., iHeart). We next identify a set of distinctive features that are good predictors of the growth of the application adoptions in terms of final population size. We finally propose a prediction model to infer whether a cascade of application adoption will continue to grow in the future based on observing the initial adoption process. Results show that our proposed model can achieve high precision (over 80%) in predicting large cascades of application adoptions. We believe our work can give an important implication in resource allocation of OSN-based product stakeholders, for example, via targeted marketing.

Knowledge sharing and organizational citizenship behavior (OCB) among project team members are crucial for project success due to the particularities of information systems (IS) projects. Bonding social capital is known to increase knowledge sharing and collaboration. However, the influence of bridging social capital on knowledge sharing is unclear. The interplay between bridging and bonding social capital is especially interesting in a collectivistic society such as South Korea where in-group ties are particularly strong. In this study, we examine the effect of the sub-dimensions of the above constructs (i.e., bonding, bridging, OCB) on knowledge sharing. The results suggest that bonding and citizenry behavior improves knowledge sharing. Bridging improves knowledge sharing by increasing organizational citizenry behavior. Unexpectedly, the effect of citizenship behavior on knowledge sharing differs from the effect of its sub-dimensions. Only helping others is positively related to knowledge sharing. Similarly, the sub-dimensions of bridging and bonding influence knowledge sharing and citizenship behavior differently than the first-order constructs. For example, shared team mental model positively affects OCB, but has no direct influence on knowledge sharing. Conversely, shared vision is the only sub-dimension of bridging that positively affects OCB. Thus, the results suggest that project managers should facilitate shared project vision and team mental model to encourage team members’ citizenship behavior and control the potential adverse influence of bridging social capital.

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.