How people seek, request, and exchange information in social interactions is shaped by personality and situational context, connecting the fields of interactive information science and attribution theory in social psychology. In everyday life, people seek information to achieve goals, collaborate, and manage social conflicts. Understanding how individual traits and contextual factors influence information-seeking behavior remains a challenge. Recent advances with large language models (LLMs) enable the simulation of socially grounded information-seeking behaviors in realistic and controllable ways. We introduce CHARISMA, a simulation framework that uses LLMs to examine how personality traits and situational factors influence information seeking as a form of social behavior. CHARISMA leverages movie characters and public figures as personality anchors, drawing on LLMs’ knowledge to simulate human-like interaction. CHARISMA’s utility is demonstrated in two studies: (1) agreeable pairs resolve conflicts more successfully, and (2) low-agreeable agents compete for information, while high-agreeable agents cooperate through prosocial exchange.

The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the context of specific ideologies, our ability to accurately characterize extremism in more generalizable terms remains underdeveloped. In this paper, we propose a novel method for extracting and analyzing extremist discourse across a range of online ideological community forums. By focusing on verbal behavioral signatures of extremist traits, we develop a framework for quantifying extremism at both user and community levels. Our research identifies 11 distinct factors, which we term "The Extremist Eleven," as a generalized psychosocial model of extremism. Applying our method to various online communities, we demonstrate an ability to characterize ideologically diverse communities across the 11 extremist traits. We demonstrate the power of this method by analyzing user histories from members of the incel community. We find that our framework accurately predicts which users join the incel community up to 10 months before their actual entry with an AUC of > 0.6, steadily increasing to AUC ~ 0.9 three to four months before the event. Further, we find that upon entry into an ideological forum, the users tend to maintain their level of extremist traits within the community, while still remaining distinguishable from the general online discourse. Our findings contribute to the study of extremism by introducing a more holistic, cross-ideological approach that transcends traditional, trait-specific models.

Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to 'what is necessary' for a given purpose. In this work, we reason that providing stricter formal privacy guarantees, while increasing the volume of user data in the model, in most cases increases benefit for all parties involved, especially for the user. We demonstrate our arguments on two existing suicide risk assessment datasets of Twitter and Reddit posts. We present the first analysis juxtaposing user history length and differential privacy budgets and elaborate how modeling additional user context enables utility preservation while maintaining acceptable user privacy guarantees.

Social media enable users to share their feelings and emotional struggles. They also offer an opportunity to provide community support to suicidal users. Recent studies on suicide risk assessment have explored the user's historic timeline and information from their social network to analyze their emotional state. However, such methods often require a large amount of user-centric data. A less intrusive alternative is to only use conversation trees arising from online community responses. Modeling such online conversations between the community and a person in distress is an important context for understanding that person's mental state. However, it is not trivial to model the vast number of conversation trees on social media, since each comment has a diverse influence on a user in distress. Typically, a handful of comments/posts receive a significantly high number of replies, which results in scale-free dynamics in the conversation tree. Moreover, psychological studies suggested that it is important to capture the fine-grained temporal irregularities in the release of vast volumes of comments, since suicidal users react quickly to online community support. Building on these limitations and psychological studies, we propose HCN, a Hyperbolic Conversation Network, which is a less user-intrusive method for suicide ideation detection. HCN leverages the hyperbolic space to represent the scale-free dynamics of online conversations. Through extensive quantitative, qualitative, and ablative experiments on real-world Twitter data, we find that HCN outperforms state-of-the art methods, while using 98% less user-specific data, and while maintaining a 74% lower carbon footprint and a 94% smaller model size. We also find that the comments within the first half an hour are most important to identify at-risk users.

In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task.While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored.We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams.Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.

With more than 22 million articles, the largest collaborative knowledge resource never sleeps, experiencing several article edits every second. Over one fifth of these articles describes individual people, the majority of which are still alive. Such articles are, by their nature, prone to corruption and vandalism. Manual quality assurance by experts can barely cope with this massive amount of data. Can it be effectively replaced by feedback from the crowd? Can we provide meaningful support for quality assurance with automated text processing techniques? Which properties of the articles should then play a key role in the machine learning algorithms and why? In this paper, we study the user-perceived quality of Wikipedia articles based on a novel Wikipedia user feedback dataset. In contrast to previous work on quality assessment which mostly relied on judgements of active Wikipedia authors, we analyze ratings of ordinary Wikipedia users along four quality dimensions (Complete, Well written, Trustworthy and Objective). We first present an empirical analysis of the novel dataset with over 36 million Wikipedia article ratings. We then select a subset of biographical articles and perform classification experiments to predict their quality ratings along each of the dimensions, exploring multiple linguistic, surface and network properties of the rated articles. Additionally, we study the classification performance and differences for the biographies of living and dead people as well as those for men and women. We demonstrate the effectiveness of our approach by the F-scores of 0.94, 0.89, 0.73, and 0.73 for the dimensions Complete, Well written, Trustworthy, and Objective. Based on the results, we believe that the quality assessment of big textual data can be effectively supported by current text classification and language processing tools.